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http://www.BillHowell.ca/ProjMajor/History/Bell: Cracking the Nazi code, Howell notes.txt
http://www.BillHowell.ca/economics, markets/Dalio world order/Dalio 2021 Changing World Order [currency, T-bond, gold, war], Howell review.html
http://www.BillHowell.ca/ProjMini/Mythology/Cardona - gods are planets, planets are gods (plus Sun).HtmWeb.html
http://www.BillHowell.ca/My sports & clubs/natural- Sacha Dobler/Dobler ~2016 The Sun and history, Howell notes.txt
http://www.BillHowell.ca/My sports & clubs/natural- Sacha Dobler/Dobler: Solar Behaviour, Howell review.txt
http://www.BillHowell.ca/ProjMini/Mythology/Freeman 2009 Canadas Stonehenge, Siksika territory, Howell review.pHoWeb.html
http://www.BillHowell.ca/ProjMini//Randell Mills/Holverstott 2016 Randell Mills hydrino energy, Howell review.pdf
http://www.BillHowell.ca/ProjMini/PuetzUWS/Howell - PuetzUWS [time, price] multifractal of detrended SPX 1871-2020.html
http://www.BillHowell.ca/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/Electric Universe/Kaal SAM nucleus/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/History/McIntyre 2018 The spy and the traitor, Howell notes.txt
http://www.BillHowell.ca/Neural nets/Schmidhuber/Schmidhuber 24Sep2021 Scientific Integrity, Howell questions.doc
http://www.BillHowell.ca/ProjMajor/Electric Universe/Scott - Birkeland current/Scott: Birkeland current magnetic structure, Howell review and comments.pdf
http://www.BillHowell.ca/ProjMajor/Pandemics, health, and the Sun/corona virus/Tamny 2021 When politicians panicked (corona virus), Howell review.pHoWeb.html
http://www.BillHowell.ca/ProjMajor/History/Temple - Egyptian Dawn/Temple, Davidovits: geopolymeric casting of pyramids.odt
http://www.BillHowell.ca/ProjMini/Mythology/Wilson 1977 Cosmic trigger, Howells review.pHoWeb.html
http://www.BillHowell.ca/ProjMini/Mythology/Wilson 1990 Quantum consciousness, Howells review.pHoWeb.html
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http://www.BillHowell.ca/ProjMajor/History/Bell: Cracking the Nazi code, Howell notes.txt
http://www.BillHowell.ca/economics, markets/Dalio world order/Dalio 2021 Changing World Order [currency, T-bond, gold, war], Howell review.html
http://www.BillHowell.ca/ProjMini/Mythology/Cardona - gods are planets, planets are gods (plus Sun).HtmWeb.html
http://www.BillHowell.ca/My sports & clubs/natural- Sacha Dobler/Dobler ~2016 The Sun and history, Howell notes.txt
http://www.BillHowell.ca/My sports & clubs/natural- Sacha Dobler/Dobler: Solar Behaviour, Howell review.txt
http://www.BillHowell.ca/ProjMini/Mythology/Freeman 2009 Canadas Stonehenge, Siksika territory, Howell review.pHoWeb.html
http://www.BillHowell.ca/ProjMini//Randell Mills/Holverstott 2016 Randell Mills hydrino energy, Howell review.pdf
http://www.BillHowell.ca/ProjMini/PuetzUWS/Howell - PuetzUWS [time, price] multifractal of detrended SPX 1871-2020.html
http://www.BillHowell.ca/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/Electric Universe/Kaal SAM nucleus/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/History/McIntyre 2018 The spy and the traitor, Howell notes.txt
http://www.BillHowell.ca/Neural nets/Schmidhuber/Schmidhuber 24Sep2021 Scientific Integrity, Howell questions.doc
http://www.BillHowell.ca/ProjMajor/Electric Universe/Scott - Birkeland current/Scott: Birkeland current magnetic structure, Howell review and comments.pdf
http://www.BillHowell.ca/ProjMajor/Pandemics, health, and the Sun/corona virus/Tamny 2021 When politicians panicked (corona virus), Howell review.pHoWeb.html
http://www.BillHowell.ca/ProjMajor/History/Temple - Egyptian Dawn/Temple, Davidovits: geopolymeric casting of pyramids.odt
http://www.BillHowell.ca/ProjMini/Mythology/Wilson 1977 Cosmic trigger, Howells review.pHoWeb.html
http://www.BillHowell.ca/ProjMini/Mythology/Wilson 1990 Quantum consciousness, Howells review.pHoWeb.html
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http://www.BillHowell.ca/ProjMajor/History/Bell: Cracking the Nazi code, Howell notes.txt
http://www.BillHowell.ca/economics, markets/Dalio world order/Dalio 2021 Changing World Order [currency, T-bond, gold, war], Howell review.html
http://www.BillHowell.ca/ProjMini/Mythology/Cardona - gods are planets, planets are gods (plus Sun).HtmWeb.html
http://www.BillHowell.ca/My sports & clubs/natural- Sacha Dobler/Dobler ~2016 The Sun and history, Howell notes.txt
http://www.BillHowell.ca/My sports & clubs/natural- Sacha Dobler/Dobler: Solar Behaviour, Howell review.txt
http://www.BillHowell.ca/ProjMini/Mythology/Freeman 2009 Canadas Stonehenge, Siksika territory, Howell review.pHoWeb.html
http://www.BillHowell.ca/ProjMini//Randell Mills/Holverstott 2016 Randell Mills hydrino energy, Howell review.pdf
http://www.BillHowell.ca/ProjMini/PuetzUWS/Howell - PuetzUWS [time, price] multifractal of detrended SPX 1871-2020.html
http://www.BillHowell.ca/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/Electric Universe/Kaal SAM nucleus/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/History/McIntyre 2018 The spy and the traitor, Howell notes.txt
http://www.BillHowell.ca/Neural nets/Schmidhuber/Schmidhuber 24Sep2021 Scientific Integrity, Howell questions.doc
http://www.BillHowell.ca/ProjMajor/Electric Universe/Scott - Birkeland current/Scott: Birkeland current magnetic structure, Howell review and comments.pdf
http://www.BillHowell.ca/ProjMajor/Pandemics, health, and the Sun/corona virus/Tamny 2021 When politicians panicked (corona virus), Howell review.pHoWeb.html
http://www.BillHowell.ca/ProjMajor/History/Temple - Egyptian Dawn/Temple, Davidovits: geopolymeric casting of pyramids.odt
http://www.BillHowell.ca/ProjMini/Mythology/Wilson 1977 Cosmic trigger, Howells review.pHoWeb.html
http://www.BillHowell.ca/ProjMini/Mythology/Wilson 1990 Quantum consciousness, Howells review.pHoWeb.html
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http://www.BillHowell.ca/ProjMajor/History/Bell: Cracking the Nazi code, Howell notes.txt
http://www.BillHowell.ca/economics, markets/Dalio world order/Dalio 2021 Changing World Order [currency, T-bond, gold, war], Howell review.html
http://www.BillHowell.ca/ProjMini/Mythology/Cardona - gods are planets, planets are gods (plus Sun).HtmWeb.html
http://www.BillHowell.ca/My sports & clubs/natural- Sacha Dobler/Dobler ~2016 The Sun and history, Howell notes.txt
http://www.BillHowell.ca/My sports & clubs/natural- Sacha Dobler/Dobler: Solar Behaviour, Howell review.txt
http://www.BillHowell.ca/ProjMini/Mythology/Freeman 2009 Canadas Stonehenge, Siksika territory, Howell review.pHoWeb.html
http://www.BillHowell.ca/ProjMini//Randell Mills/Holverstott 2016 Randell Mills hydrino energy, Howell review.pdf
http://www.BillHowell.ca/ProjMini/PuetzUWS/Howell - PuetzUWS [time, price] multifractal of detrended SPX 1871-2020.html
http://www.BillHowell.ca/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/Electric Universe/Kaal SAM nucleus/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/History/McIntyre 2018 The spy and the traitor, Howell notes.txt
http://www.BillHowell.ca/Neural nets/Schmidhuber/Schmidhuber 24Sep2021 Scientific Integrity, Howell questions.doc
http://www.BillHowell.ca/ProjMajor/Electric Universe/Scott - Birkeland current/Scott: Birkeland current magnetic structure, Howell review and comments.pdf
http://www.BillHowell.ca/ProjMajor/Pandemics, health, and the Sun/corona virus/Tamny 2021 When politicians panicked (corona virus), Howell review.pHoWeb.html
http://www.BillHowell.ca/ProjMajor/History/Temple - Egyptian Dawn/Temple, Davidovits: geopolymeric casting of pyramids.odt
http://www.BillHowell.ca/ProjMini/Mythology/Wilson 1977 Cosmic trigger, Howells review.pHoWeb.html
http://www.BillHowell.ca/ProjMini/Mythology/Wilson 1990 Quantum consciousness, Howells review.pHoWeb.html
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Programming code/
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Programming code/
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Programming code/
http://www.videolan.org/vlc/
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_wrong.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/CI_intro.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Hearing_aids.ogv
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Programming code/
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Introduction.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Wrong_theories.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_ContDrift.ogv
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Hearing_aids.ogv
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#Summary
#Key files
#References
https://www.youtube.com/user/pkinkaie/playlists
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/Howell - hydrogen future Alberta.odp
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/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/Howell - hydrogen future Alberta.pdf
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http://go.sparkpostmail2.com/f/a/6RDQLp2qv17drv4uWYAbeQ~~/AACohQA~/RgRkLI4OP0QcaHR0cHM6Ly95b3V0dS5iZS95V21lcV8wbnZPY1cDc3BjQgpiPhMJSmIeKOc5UhJCaWxsQEJpbGxIb3dlbGwuY2FYBAAAL1Y~
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#Summary
#Key files
#References
https://www.youtube.com/user/pkinkaie/playlists
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/Howell - hydrogen future Alberta.odp
./0.0 31Mar2022 full presentation.mp3
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/Howell - hydrogen future Alberta.pdf
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/scripts - hydrogen future Alberta.txt
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http://go.sparkpostmail2.com/f/a/6RDQLp2qv17drv4uWYAbeQ~~/AACohQA~/RgRkLI4OP0QcaHR0cHM6Ly95b3V0dS5iZS95V21lcV8wbnZPY1cDc3BjQgpiPhMJSmIeKOc5UhJCaWxsQEJpbGxIb3dlbGwuY2FYBAAAL1Y~
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/home/bill/web/webWork/pMenuTopStatus.html#videos
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/home/bill/web/webWork/pMenuTopHelp.html#videos
#Summary
#Key files
#References
https://www.youtube.com/user/pkinkaie/playlists
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/Howell - hydrogen future Alberta.odp
./0.0 31Mar2022 full presentation.mp3
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/Howell - hydrogen future Alberta.pdf
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/scripts - hydrogen future Alberta.txt
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http://go.sparkpostmail2.com/f/a/6RDQLp2qv17drv4uWYAbeQ~~/AACohQA~/RgRkLI4OP0QcaHR0cHM6Ly95b3V0dS5iZS95V21lcV8wbnZPY1cDc3BjQgpiPhMJSmIeKOc5UhJCaWxsQEJpbGxIb3dlbGwuY2FYBAAAL1Y~
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/home/bill/web/webWork/pMenuTopHelp.html#videos
#Summary
#Key files
#References
https://www.youtube.com/user/pkinkaie/playlists
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/Howell - hydrogen future Alberta.odp
./0.0 31Mar2022 full presentation.mp3
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/Howell - hydrogen future Alberta.pdf
/home/bill/web/Bill Howells videos/220331 Hydrogen future Alberta/scripts - hydrogen future Alberta.txt
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http://go.sparkpostmail2.com/f/a/6RDQLp2qv17drv4uWYAbeQ~~/AACohQA~/RgRkLI4OP0QcaHR0cHM6Ly95b3V0dS5iZS95V21lcV8wbnZPY1cDc3BjQgpiPhMJSmIeKOc5UhJCaWxsQEJpbGxIb3dlbGwuY2FYBAAAL1Y~
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#???
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/home/bill/web/Bill Howells videos/250927 Ocean Ru tale/A tale of Ocean Ru script.HtmWeb.html#Table of Contents
#Table of Contents
#Introduction
#Instructions
#Genesis
#Expanding Earth? (Ott Hilgenberg, James Maxlowe, Neal Adams)
#'Water World' and the early forms of life? (Tyrrell museum)
#Iceball Earth?
#A [time, length, theme] multifractal map of Earth's History: paleontology eras and periods
#The great split begins ~200 MyBP [Vancouver Canada, Arctic North-East Russia, Cairns Australia]
#Wandering split of today's continents and islands 200 MyBP-> present (Neal Adams)
#When [Eastern Australia, North-East Russia, Western Canada] were neighbors
#Great aunt Donna and the elasmosaur (Comox museum)
#Human pre-history and history
#Petroglyphs and sky plasmas (Talbot-Thornhill-Peratt, Irene Howell)
#Electric Universe Geology, arc blasts (Andrew Hall, Ben Davidson friend)
#Electric scarring of the Earth's surface (Paul Anderson)
#Mythical Egypt (Thomas Brophy, John Anthony West)
#Nabla Playa origin Map, Nubian desert, Orion constellation 11 kyBP (Thomas Brophy)
#Electricity in Ancient Egypt (Andrew Hall)
#Comox BC Queneesh Legend (Neil Howell)
#Canada's Stonehenge ~5kyBP (Gordon Freeman)
#Exodus proofs ~1.5kyrBP (Simcha Jacobovici)
#Multiple Conflicting Futures for Ocean Ru
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/Electric Universe [time, length, theme] multifractals.HtmWeb.html
#Expanding Earth? (Ott Hilgenberg, James Maxlowe, Neal Adams)
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Dark matter video 1 - initial, simple.mpeg
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Birkeland rotation in galaxy - not dark matter.HtmWeb.html
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_Neal_Adams_vid.ogv
/home/bill/web/ProjMini/Expanding Earth/Maxlow 2018 Beyond Plate Tectonics (2nd Ed 2021).pdf
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/Electric Universe [time, length, theme] multifractals.HtmWeb.html
/home/bill/web/webOther/Howell, Neil/Comox BC Legend of the Queneesh, great flood/0_Comox Queneesh Legend, elasmosaur.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Hall- electric geology/Andrew Halls electric geology [thunderblog, video]s.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Anderson 120725 Electric scarring of the Earth's surface - annotated & scanned 120910.pdf
/home/bill/web/ProjMini/Mythology/Magical Egypt/West 2001 Magical Egypt- a symbolist tour, Howell review.HtmWeb.html
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 05:08 Nabla Playa Fred Wendorf.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 05:18 Nabla Playa complex on map.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 05:24 Nabla Playa inclined rock.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 05:48 Nabla Playa central stone circle.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 06:14 Thomas Brophy: the Origin Map book cover.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 08:16 Thomas Brophy with a scale model of Nabla Playa.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 09:18 Nabla Playa summer solistice sunrise.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 10:32 Nabla Playa Orion just before sunrise.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 14:44 Nabla Playa Orion orientation at minimum of 26ky precessional cycle.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 15:34 Nabla Playa 3 sets of outlying stones.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 22:34 Nabla Playa Bellatrix distance to within accuracy of modern data.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 22:47 Nabla Playa Betelgeuse distance to within accuracy of modern data.png
/home/bill/web/ProjMajor/Electric Universe/Hall- electric geology/Andrew Halls electric geology [thunderblog, video]s.HtmWeb.html#Electricity in Ancient Egypt
/home/bill/web/webOther/Howell, Neil/Comox BC Legend of the Queneesh, great flood/0_Comox Queneesh Legend, elasmosaur.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 Exodus proofs, transcript.HtmWeb.html
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/
/home/bill/web/webWork/pMenuTopMenu.html#videos
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/home/bill/web/webWork/pMenuTopStatus.html#videos
/home/bill/web/webWork/pMenuTopCopyright.html#videos
/home/bill/web/webWork/pMenuTopHelp.html#videos
/home/bill/web/Bill Howells videos/250927 Ocean Ru tale/A tale of Ocean Ru script.HtmWeb.html#Table of Contents
#Table of Contents
#Introduction
#Instructions
#Genesis
#Expanding Earth? (Ott Hilgenberg, James Maxlowe, Neal Adams)
#'Water World' and the early forms of life? (Tyrrell museum)
#Iceball Earth?
#A [time, length, theme] multifractal map of Earth's History: paleontology eras and periods
#The great split begins ~200 MyBP [Vancouver Canada, Arctic North-East Russia, Cairns Australia]
#Wandering split of today's continents and islands 200 MyBP-> present (Neal Adams)
#When [Eastern Australia, North-East Russia, Western Canada] were neighbors
#Great aunt Donna and the elasmosaur (Comox museum)
#Human pre-history and history
#Petroglyphs and sky plasmas (Talbot-Thornhill-Peratt, Irene Howell)
#Electric Universe Geology, arc blasts (Andrew Hall, Ben Davidson friend)
#Electric scarring of the Earth's surface (Paul Anderson)
#Mythical Egypt (Thomas Brophy, John Anthony West)
#Nabla Playa origin Map, Nubian desert, Orion constellation 11 kyBP (Thomas Brophy)
#Electricity in Ancient Egypt (Andrew Hall)
#Comox BC Queneesh Legend (Neil Howell)
#Canada's Stonehenge ~5kyBP (Gordon Freeman)
#Exodus proofs ~1.5kyrBP (Simcha Jacobovici)
#Multiple Conflicting Futures for Ocean Ru
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/Electric Universe [time, length, theme] multifractals.HtmWeb.html
#Expanding Earth? (Ott Hilgenberg, James Maxlowe, Neal Adams)
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Dark matter video 1 - initial, simple.mpeg
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Birkeland rotation in galaxy - not dark matter.HtmWeb.html
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_Neal_Adams_vid.ogv
/home/bill/web/ProjMini/Expanding Earth/Maxlow 2018 Beyond Plate Tectonics (2nd Ed 2021).pdf
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/Electric Universe [time, length, theme] multifractals.HtmWeb.html
/home/bill/web/webOther/Howell, Neil/Comox BC Legend of the Queneesh, great flood/0_Comox Queneesh Legend, elasmosaur.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Hall- electric geology/Andrew Halls electric geology [thunderblog, video]s.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Anderson 120725 Electric scarring of the Earth's surface - annotated & scanned 120910.pdf
/home/bill/web/ProjMini/Mythology/Magical Egypt/West 2001 Magical Egypt- a symbolist tour, Howell review.HtmWeb.html
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 05:08 Nabla Playa Fred Wendorf.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 05:18 Nabla Playa complex on map.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 05:24 Nabla Playa inclined rock.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 05:48 Nabla Playa central stone circle.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 06:14 Thomas Brophy: the Origin Map book cover.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 08:16 Thomas Brophy with a scale model of Nabla Playa.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 09:18 Nabla Playa summer solistice sunrise.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 10:32 Nabla Playa Orion just before sunrise.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 14:44 Nabla Playa Orion orientation at minimum of 26ky precessional cycle.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 15:34 Nabla Playa 3 sets of outlying stones.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 22:34 Nabla Playa Bellatrix distance to within accuracy of modern data.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/raw/MagicEgypt DVD3 22:47 Nabla Playa Betelgeuse distance to within accuracy of modern data.png
/home/bill/web/ProjMajor/Electric Universe/Hall- electric geology/Andrew Halls electric geology [thunderblog, video]s.HtmWeb.html#Electricity in Ancient Egypt
/home/bill/web/webOther/Howell, Neil/Comox BC Legend of the Queneesh, great flood/0_Comox Queneesh Legend, elasmosaur.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 Exodus proofs, transcript.HtmWeb.html
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/
/home/bill/web/ProjMini/Mythology/???
/home/bill/web/ProjMini/Mythology/???
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/2 Batman with bats.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/rift Pacific E-W off North America.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/rift Pacific E-W off South America.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/rift seafloor ages mid-Atlantic, N&S America coast.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/Mars - same as expanding Earth.png
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/home/bill/web/My sports & clubs/museum- Tyrell/images/20250503_134003 Tyrrell Humble origins all rocks.jpg
/home/bill/web/My sports & clubs/museum- Tyrell/images/20250503_134248 Tyrrell worlds oldest macroscopic fossils Mistaken Point Newfoundland.jpg

/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/Australia - Canada ~200 MyrBP.png
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/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt 2003 Petroglyphs and lab plasmas II.png
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt petroglyphs  squatter human, stick-man-on-stone.jpg
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt etal 2007 petroglyph global distributions.png
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt etal 2007 petroglyph Zankar river site in sunlight.png
/home/bill/web/webOther/Howell, Irene/images/20251007_121827 Nazca animals quilt, Designed & made by  Irene Howell, Calgary 2015.jpg
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/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Anderson - Waimakariri River NZ.JPG
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Anderson - Yemen.JPG
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Howell 120829 Dinosaur Park, AB, Canada eye alt 16-6 km.JPG
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Howell 120829 Drumheller, AB, Canada eye alt 21 km.JPG
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 05:08 Nabla Playa Fred Wendorf.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 05:18 Nabla Playa complex on map.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 05:24 Nabla Playa inclined rock.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 05:48 Nabla Playa central stone circle.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 06:14 Thomas Brophy: the Origin Map book cover.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 08:16 Thomas Brophy with a scale model of Nabla Playa.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 09:18 Nabla Playa summer solistice sunrise.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 10:32 Nabla Playa Orion just before sunrise.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 14:44 Nabla Playa Orion orientation at minimum of 26ky precessional cycle.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 15:34 Nabla Playa 3 sets of outlying stones.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 22:34 Nabla Playa Bellatrix distance to within accuracy of modern data.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 22:47 Nabla Playa Betelgeuse distance to within accuracy of modern data.png
/home/bill/web/ProjMajor/Electric Universe/Hall- electric geology/images/Hall 26Aug2023 Electricity in Ancient Egypt 00:08 title.png
/home/bill/web/ProjMajor/Electric Universe/Hall- electric geology/images/
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/home/bill/web/webOther/Howell, Neil/Comox BC Legend of the Queneesh, great flood/200925 Comox Queneesh Legend - preparation, rains, flood, canoe.jpg
/home/bill/web/ProjMajor/History/Freeman: Canadas Stonehenge/images Freeman Stonehenge/Book covers: Ev Cochrane and Gordon Freeman.jpg
/home/bill/web/ProjMajor/History/Freeman: Canadas Stonehenge/images Freeman Stonehenge/Sun Cairn Ring: Majorville medicine wheel aerial view.jpg
/home/bill/web/ProjMajor/History/Freeman: Canadas Stonehenge/images Freeman Stonehenge/Stonehenge layout proper rotate.png
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt 0312dd Stonehenge.png
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/images raw/Santorini, huge volcanic eruption ended Minoans.png
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/images raw/Santorini, cliffs of eruption crater.png
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/images raw/Beni Hassan tomb, Exodus scene 1 ducks.png
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/images raw/Beni Hassan tomb, Exodus scene 3 donkey, women.png
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/images raw/Beni Hassan tomb, Exodus scene 2 antelope.png
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/images raw/Beni Hassan tomb, Exodus scene 4 bearded Semites.png
/home/bill/web/ProjMini/Mythology/???
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/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/stars & Birkeland-000000.jpg
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/2 Batman with bats.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/rift Pacific E-W off North America.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/rift Pacific E-W off South America.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/rift seafloor ages mid-Atlantic, N&S America coast.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/Mars - same as expanding Earth.png
/home/bill/web/My sports & clubs/museum- Tyrell/images/20250503_133955 Tyrrell Humble origins right rocks.jpg
/home/bill/web/My sports & clubs/museum- Tyrell/images/20250503_134003 Tyrrell Humble origins all rocks.jpg
/home/bill/web/My sports & clubs/museum- Tyrell/images/20250503_134248 Tyrrell worlds oldest macroscopic fossils Mistaken Point Newfoundland.jpg

/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/Australia - Canada ~200 MyrBP.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/Australia - Canada ~180 MyrBP.png
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/ExpandEarth_Neal_Adams_vid/Australia - Canada ~160 MyrBP.png
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/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt 2003 Petroglyphs and lab plasmas II.png
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/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt etal 2007 petroglyph Zankar river site in sunlight.png
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/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 05:24 Nabla Playa inclined rock.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 05:48 Nabla Playa central stone circle.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 06:14 Thomas Brophy: the Origin Map book cover.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 08:16 Thomas Brophy with a scale model of Nabla Playa.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 09:18 Nabla Playa summer solistice sunrise.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 10:32 Nabla Playa Orion just before sunrise.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 14:44 Nabla Playa Orion orientation at minimum of 26ky precessional cycle.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 15:34 Nabla Playa 3 sets of outlying stones.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 22:34 Nabla Playa Bellatrix distance to within accuracy of modern data.png
/home/bill/web/ProjMini/Mythology/Magical Egypt/images/MagicEgypt DVD3 22:47 Nabla Playa Betelgeuse distance to within accuracy of modern data.png
/home/bill/web/ProjMajor/Electric Universe/Hall- electric geology/images/Hall 26Aug2023 Electricity in Ancient Egypt 00:08 title.png
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#Edo Kaal's 'Structured Atom Model' (SAM)
#Edo Kaal presentation 'The Structured Atom Model', EU2017 conference
#Full video transcript
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#Conference webPage & description
#video link
#full video transcript
#Table of Contents for the presentation
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#00:20 nuclear decay processes
#00:33 decay process of U235
#00:46 alpha particle emission, problems with the standard explanations
#02:12 SAM basics: nucleus structure, electrostatic, no [neutron, strong force]
#03:42 SAM periodic table
#04:06 SAM transmutations confirmed in SAFIRE laboratory
#04:40 Inner nuclear structure dictates chemical properties
#05:11 Formation of elements doesn't require stars
#06:06 Other SAM [assumptions, advantages]
#Howell: questions about SAM
#Nomenclature, acronyms
#Definitions: nuclear [material, process, deactivate]s
#References
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
#SAFIRE, Aureon.ca
/home/bill/web/ProjMini/Sierpinski fractal tetrahedra/Sierpinski fractal tetrahedra.HtmWeb.html
https://Thunderbolts.info/
https://structuredatom.org/
https://structuredatom.org/atomizer/atom-viewer
https://structuredatom.org/atomizer/atom-viewer
https://StructuredAtom.org/
https://www.thunderbolts.info/wp/2018/03/11/edwin-kaal-the-proton-electron-atom-a-proposal-for-a-structured-atomic-model-eu2017/
https://www.youtube.com/watch?v=sJKW9VNo2BU
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal: The Proton-Electron Atom, paragraphL.txt
https://en.wikipedia.org/wiki/Platonic_solid/
https://thunderbolts.us7.list-manage.com/track/click?u=1b8e5fc5ffab70f95805dea12&id=1cd856a1a1&e=54f3bc9169
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 nuclear decay processes.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 alpha particle emission.png
https://www.thunderbolts.info/wp/?s=fulgurite
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/SAFIRE electric sun.HtmWeb.html
https://aureon.ca/
https://www.psfc.mit.edu/research/topics/high-field-pathway-fusion-power
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/Aureon.ca plasma technology.HtmWeb.html
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Max_Planck
https://www.lenr-forum.com/forum/thread/6269-sam-the-structured-atom-model-edo-kael/?pageNo=2
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/1872-2020 SP500 index, ratio of opening price to semi-log detrended price.HtmWeb.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/images/Howell: Electric Universe [time, length] multifractal.jpg
https://www.thunderbolts.info/
/home/bill/web/References/Climate/Scherer, Veizer, Shaviv etal 2006 - Interstellar-Terrestrial relations.pdf
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Birkeland rotation in galaxy - not dark matter.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html#Definitions: nuclear [material, process, deactivate]s
https://ieer.org/resource/factsheets/fissile-material-basics/
https://naturalphilosophy.org/
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#Edo Kaal's 'Structured Atom Model' (SAM)
#Edo Kaal presentation 'The Structured Atom Model', EU2017 conference
#Full video transcript
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#Conference webPage & description
#video link
#full video transcript
#Table of Contents for the presentation
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#00:20 nuclear decay processes
#00:33 decay process of U235
#00:46 alpha particle emission, problems with the standard explanations
#02:12 SAM basics: nucleus structure, electrostatic, no [neutron, strong force]
#03:42 SAM periodic table
#04:06 SAM transmutations confirmed in SAFIRE laboratory
#04:40 Inner nuclear structure dictates chemical properties
#05:11 Formation of elements doesn't require stars
#06:06 Other SAM [assumptions, advantages]
#Howell: questions about SAM
#Nomenclature, acronyms
#Definitions: nuclear [material, process, deactivate]s
#References
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
#SAFIRE, Aureon.ca
/home/bill/web/ProjMini/Sierpinski fractal tetrahedra/Sierpinski fractal tetrahedra.HtmWeb.html
https://Thunderbolts.info/
https://structuredatom.org/
https://structuredatom.org/atomizer/atom-viewer
https://structuredatom.org/atomizer/atom-viewer
https://StructuredAtom.org/
https://www.thunderbolts.info/wp/2018/03/11/edwin-kaal-the-proton-electron-atom-a-proposal-for-a-structured-atomic-model-eu2017/
https://www.youtube.com/watch?v=sJKW9VNo2BU
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal: The Proton-Electron Atom, paragraphL.txt
https://en.wikipedia.org/wiki/Platonic_solid/
https://thunderbolts.us7.list-manage.com/track/click?u=1b8e5fc5ffab70f95805dea12&id=1cd856a1a1&e=54f3bc9169
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 nuclear decay processes.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 alpha particle emission.png
https://www.thunderbolts.info/wp/?s=fulgurite
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/SAFIRE electric sun.HtmWeb.html
https://aureon.ca/
https://www.psfc.mit.edu/research/topics/high-field-pathway-fusion-power
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/Aureon.ca plasma technology.HtmWeb.html
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Max_Planck
https://www.lenr-forum.com/forum/thread/6269-sam-the-structured-atom-model-edo-kael/?pageNo=2
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/1872-2020 SP500 index, ratio of opening price to semi-log detrended price.HtmWeb.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/images/Howell: Electric Universe [time, length] multifractal.jpg
https://www.thunderbolts.info/
/home/bill/web/References/Climate/Scherer, Veizer, Shaviv etal 2006 - Interstellar-Terrestrial relations.pdf
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Birkeland rotation in galaxy - not dark matter.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html#Definitions: nuclear [material, process, deactivate]s
https://ieer.org/resource/factsheets/fissile-material-basics/
https://naturalphilosophy.org/
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/SAM: nature of atom cover atom, front cover, updated.jpg
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/team: Edo Kaal & SAM.png
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/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/240421 chemical element creation dogma [BigBang, cosmic ray, star [dying low-mass, merging neutron, exploding [white dwarf, massive]]: SeeThePattern YT.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/Aureon.ca elements created in hydrogen plasma reactor.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 SAM team periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 standard model versus Structured Atom Model.png
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/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Newlands Law of Octaves.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Mendeleev 1897 periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 basic geometrical shapes making up the nucleus.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Plantonic solids 25May2024 wikipedia.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 carbon 14 decays to nitrogen 14: densest packing at work.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 proposal for a Structured Atom Model, more postulations.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 the new neutron: not a fundamental particle.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Carl Johnson - Statistical analysis of isotope masses.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Ethereal Matters LLC animation of atom builder application.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 recurring geometric shapes in the periodic table: carbon group.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 summary of the new atomic model (SAM).png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 potential implications of Structured Atom Model (SAM).png
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/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 SAFIRE transmutations confirmed in laboratory.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 formation of elements doesnt require stars.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Howell composite: SAM suggests elements are created in-situe on planet during intense electrical events.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 origin of neutron drip line.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/SAFIRE reactor.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/SAFIRE spherical plasma Lanmuir isotherms and T.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/SAFIRE electric sun surface granules.png
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/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/Aureon.ca elements created in hydrogen plasma reactor.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/team: Aureon.ca, visitors.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/aureon_logo.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Boscovich 1763 Fig1 single Law of forces, including atomic theory.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/nkodama, LENR forum: 3 quarks in a proton.jpg
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 TradingView eyeballed trends for (1872-1926) and (1926-2020).png
/home/bill/web/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/SAM: nature of atom cover atom, front cover, updated.jpg
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/team: Edo Kaal & SAM.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/structuredAtom.org 3D rotate elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Daltons periodic table.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Newlands Law of Octaves.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Mendeleev 1897 periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 basic geometrical shapes making up the nucleus.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/240421 chemical element creation dogma [BigBang, cosmic ray, star [dying low-mass, merging neutron, exploding [white dwarf, massive]]: SeeThePattern YT.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/Aureon.ca elements created in hydrogen plasma reactor.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 SAM team periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 standard model versus Structured Atom Model.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Daltons periodic table.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Newlands Law of Octaves.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Mendeleev 1897 periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 basic geometrical shapes making up the nucleus.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Plantonic solids 25May2024 wikipedia.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 carbon 14 decays to nitrogen 14: densest packing at work.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 proposal for a Structured Atom Model, more postulations.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 the new neutron: not a fundamental particle.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Carl Johnson - Statistical analysis of isotope masses.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Ethereal Matters LLC animation of atom builder application.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 recurring geometric shapes in the periodic table: carbon group.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 summary of the new atomic model (SAM).png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 potential implications of Structured Atom Model (SAM).png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 decay process of U235.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 SAM team periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 SAFIRE transmutations confirmed in laboratory.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 formation of elements doesnt require stars.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Howell composite: SAM suggests elements are created in-situe on planet during intense electrical events.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 origin of neutron drip line.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/SAFIRE reactor.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/SAFIRE spherical plasma Lanmuir isotherms and T.png
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http://www.BillHowell.ca/Personal/Personal/Almost true tall tales.HtmWeb.html
http://www.BillHowell.ca/Personal/Challenging the dogmas.HtmWeb.html
http://www.BillHowell.ca/Personal/Crazy ideas/Crazy ideas.HtmWeb.html
http://www.BillHowell.ca/Personal/Crystal ball.HtmWeb.html
http://www.BillHowell.ca/Personal/Howell - favourite sayings.HtmWeb.html
http://www.BillHowell.ca/Personal/z_history/130726 Deer collison/Car collision with a deer.HtmWeb.html
http://www.BillHowell.ca/Personal/Lifes like this.HtmWeb.html
http://www.BillHowell.ca/webWork/pMenuTopMenu.html#personal
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http://www.BillHowell.ca/webWork/pMenuTopStatus.html#personal
http://www.BillHowell.ca/webWork/pMenuTopCopyright.html#personal
http://www.BillHowell.ca/webWork/pMenuTopHelp.html#personal
http://www.BillHowell.ca/Personal/Personal/Almost true tall tales.HtmWeb.html
http://www.BillHowell.ca/Personal/Challenging the dogmas.HtmWeb.html
http://www.BillHowell.ca/Personal/Crazy ideas/Crazy ideas.HtmWeb.html
http://www.BillHowell.ca/Personal/Crystal ball.HtmWeb.html
http://www.BillHowell.ca/Personal/Howell - favourite sayings.HtmWeb.html
http://www.BillHowell.ca/Personal/z_history/130726 Deer collison/Car collision with a deer.HtmWeb.html
http://www.BillHowell.ca/Personal/Lifes like this.HtmWeb.html
http://www.BillHowell.ca/webWork/pMenuTopMenu.html#projMaj
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http://www.BillHowell.ca/webWork/pMenuTopStatus.html#projMaj
http://www.BillHowell.ca/webWork/pMenuTopCopyright.html#projMaj
http://www.BillHowell.ca/webWork/pMenuTopHelp.html#projMaj
#Table of Contents
#Introduction
#Some historical comments
#Thunderbolts.info - the Electric Universe and Health
#Thunderbolts.info 04Oct2016 - The Sun's Influence on Consciousness
#Thunderbolts.info 31Mar2020 - Geomagnetic Effects on Earth's Biology, Electricity of Life
#Jerry Tennant 16Jun2020 - Voltage and Regeneration, Electricity of Life
#Eileen Mckusick - Mastering the Human Biofield with Tuning Forks
#Ben Davidson & Suspicious Observers - Space Weather and health
#Ben Davidson 23Feb2018 - What To Do With Space Weather Health Information
#Adrian D'Amico 28Feb2018 - Space Weather and Human Health
#The first quantitative sociology, and the second universal wave series?
#Robert Prechter - Socionomics, the first quantitative sociology?
#Stephen Puetz - Universal Wave Series
#Other health effects of the [sun, astronomy]
#Non-health effects of the [sun, astronomy]
#Cosmic/Galactic rays at historical high in summer 2019
#USA Center for Disease Control - Leading Causes of Death 2017
#Historical pandemics
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#New corona virus cases/day/population for selected countries
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Daily cases charts for countries, by region
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Spreadsheet for generating the charts
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Jumping off the cliff and into conclusions
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#COVID-19 data and models
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Corona virus models
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Cosmic/Galactic rays at historical high in summer 2019
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Is the cure worse than the disease?
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Questions, Successes, Failures
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Howells blog posts to MarketWatch etc
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Astronomical correlates of pandemics
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Howell -  USA influenza [cases, deaths] alongside [sunspots, Kp index, zero Kp bins]
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Is the effectiveness of vaccines over-rated?
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Quite apart from the issue of the benefits of vaccines
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Influenza pandemics - Tapping, Mathias, and Surkan (TMS) theory
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Rebuttals of the [solar, disease] correlation
https://www.thunderbolts.info/wp/
https://www.thunderbolts.info/wp/2016/10/04/the-suns-influence-on-consciousness-electricity-of-life/
https://www.youtube.com/watch?v=dCqQkyWAGjU
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.629.7951&rep=rep1&type=pdf
https://www.researchgate.net/publication/260681617_Stronger_geomagnetic_fields_may_be_a_risk_factor_of_male_suicides
https://www.youtube.com/watch?v=J8hmGUQTCA8
https://www.youtube.com/watch?v=s3qY5ewHAeo
https://www.biofieldtuning.com/
http://planetarydynamics.com/2017/03/24/eileen-mckusick-mastering-the-bio-magnetic-field/
http://www.suspicious0bservers.org/
http://www.suspicious0bservers.org/dl/february-23-2018/
https://suspicious0bservers.org/deeper-look/
https://en.wikipedia.org/wiki/Gamma_wave
https://www.amazon.com/Socionomics-Science-History-Social-Prediction/dp/0932750575/ref=sr_1_8?dchild=1&keywords=Robert+Prechter%2C+Socionomics&sr=8-8
https://TradingView.com
https://www.investopedia.com/articles/technical/111401.asp
https://www.investopedia.com/articles/technical/111401.asp
https://books.google.com/books?id=8vPGDwAAQBAJ&printsec=frontcover&vq=corrective#v=onepage&q&f=false
https://www.amazon.com/Unified-Cycle-Theory-Structure-Influence/dp/1432712160/ref=sr_1_3?dchild=1&keywords=Stephen+Puetz&sr=8-3
http://www.BillHowell.ca/Cool stuff/Howell - Pandemics and the sun.pdf
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/Howell - Selected pandemics & epidemics.pdf
http://www.BillHowell.ca/Cool stuff/Hoyte & Schatten year - solar influence on climate & natural systems, graphs.pdf
http://www.BillHowell.ca/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
http://www.uct-news.com/
http://www.swpc.noaa.gov/products/solar-cycle-progression
https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_06-508.pdf
https://en.wikipedia.org/wiki/Spanish_flu
https://covid19-projections.com/#view-projections
https://www.visualcapitalist.com/history-of-pandemics-deadliest/
https://www.forbes.com/sites/ericmack/2020/03/16/see-how-coronavirus-compares-to-other-pandemics-through-history/#152cfca37d1e
 https://thefederalist.com/2020/04/18/10-deadliest-pandemics-in-history-were-much-worse-than-coronavirus-so-far/
http://www.BillHowell.ca/ProjMajor/Lies, Damned Lies, and Scientists/Lies, damned lies, and scientists.HtmWeb.html
https://www.youtube.com/user/Suspicious0bservers/videos
http://www.BillHowell.ca/webWork/pMenuTopMenu.html#projMaj
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http://www.BillHowell.ca/webWork/pMenuTopStatus.html#projMaj
http://www.BillHowell.ca/webWork/pMenuTopCopyright.html#projMaj
http://www.BillHowell.ca/webWork/pMenuTopHelp.html#projMaj
#Table of Contents
#Introduction
#Some historical comments
#Thunderbolts.info - the Electric Universe and Health
#Thunderbolts.info 04Oct2016 - The Sun's Influence on Consciousness
#Thunderbolts.info 31Mar2020 - Geomagnetic Effects on Earth's Biology, Electricity of Life
#Jerry Tennant 16Jun2020 - Voltage and Regeneration, Electricity of Life
#Eileen Mckusick - Mastering the Human Biofield with Tuning Forks
#Ben Davidson & Suspicious Observers - Space Weather and health
#Ben Davidson 23Feb2018 - What To Do With Space Weather Health Information
#Adrian D'Amico 28Feb2018 - Space Weather and Human Health
#The first quantitative sociology, and the second universal wave series?
#Robert Prechter - Socionomics, the first quantitative sociology?
#Stephen Puetz - Universal Wave Series
#Other health effects of the [sun, astronomy]
#Non-health effects of the [sun, astronomy]
#Cosmic/Galactic rays at historical high in summer 2019
#USA Center for Disease Control - Leading Causes of Death 2017
#Historical pandemics
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#New corona virus cases/day/population for selected countries
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Daily cases charts for countries, by region
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Spreadsheet for generating the charts
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Jumping off the cliff and into conclusions
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#COVID-19 data and models
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Corona virus models
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Cosmic/Galactic rays at historical high in summer 2019
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Is the cure worse than the disease?
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Questions, Successes, Failures
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html#Howells blog posts to MarketWatch etc
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Astronomical correlates of pandemics
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Howell -  USA influenza [cases, deaths] alongside [sunspots, Kp index, zero Kp bins]
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Is the effectiveness of vaccines over-rated?
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Quite apart from the issue of the benefits of vaccines
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Influenza pandemics - Tapping, Mathias, and Surkan (TMS) theory
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/influenza/Howell - influenza virus.HtmWeb.html#Rebuttals of the [solar, disease] correlation
https://www.thunderbolts.info/wp/
https://www.thunderbolts.info/wp/2016/10/04/the-suns-influence-on-consciousness-electricity-of-life/
https://www.youtube.com/watch?v=dCqQkyWAGjU
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.629.7951&rep=rep1&type=pdf
https://www.researchgate.net/publication/260681617_Stronger_geomagnetic_fields_may_be_a_risk_factor_of_male_suicides
https://www.youtube.com/watch?v=J8hmGUQTCA8
https://www.youtube.com/watch?v=s3qY5ewHAeo
https://www.biofieldtuning.com/
http://planetarydynamics.com/2017/03/24/eileen-mckusick-mastering-the-bio-magnetic-field/
http://www.suspicious0bservers.org/
http://www.suspicious0bservers.org/dl/february-23-2018/
https://suspicious0bservers.org/deeper-look/
https://en.wikipedia.org/wiki/Gamma_wave
https://www.amazon.com/Socionomics-Science-History-Social-Prediction/dp/0932750575/ref=sr_1_8?dchild=1&keywords=Robert+Prechter%2C+Socionomics&sr=8-8
https://TradingView.com
https://www.investopedia.com/articles/technical/111401.asp
https://www.investopedia.com/articles/technical/111401.asp
https://books.google.com/books?id=8vPGDwAAQBAJ&printsec=frontcover&vq=corrective#v=onepage&q&f=false
https://www.amazon.com/Unified-Cycle-Theory-Structure-Influence/dp/1432712160/ref=sr_1_3?dchild=1&keywords=Stephen+Puetz&sr=8-3
http://www.BillHowell.ca/Cool stuff/Howell - Pandemics and the sun.pdf
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/Howell - Selected pandemics & epidemics.pdf
http://www.BillHowell.ca/Cool stuff/Hoyte & Schatten year - solar influence on climate & natural systems, graphs.pdf
http://www.BillHowell.ca/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
http://www.uct-news.com/
http://www.swpc.noaa.gov/products/solar-cycle-progression
https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_06-508.pdf
https://en.wikipedia.org/wiki/Spanish_flu
https://covid19-projections.com/#view-projections
https://www.visualcapitalist.com/history-of-pandemics-deadliest/
https://www.forbes.com/sites/ericmack/2020/03/16/see-how-coronavirus-compares-to-other-pandemics-through-history/#152cfca37d1e
 https://thefederalist.com/2020/04/18/10-deadliest-pandemics-in-history-were-much-worse-than-coronavirus-so-far/
http://www.BillHowell.ca/ProjMajor/Lies, Damned Lies, and Scientists/Lies, damned lies, and scientists.HtmWeb.html
https://www.youtube.com/user/Suspicious0bservers/videos
http://www.BillHowell.ca/Cool stuff/suicide - Berk, Dodd, Henry 2006 Australian female suicides with geomanetic activity 1970-2000.png
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/Jerry Tennant 16Jun2020 Healing of [bone, muscle, skin].png
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/Jerry Tennant 16Jun2020 Healing of [bone, muscle, skin].png
http://www.BillHowell.ca/Cool stuff/Davidson - Space weather health indices 23Feb2018.png
http://www.BillHowell.ca/Cool stuff/Davidson - Space weather, am I at risk 23Feb2018.png
http://www.BillHowell.ca/economics, markets/Cool stuff/vote Prechter 1999 Landslide elections & Stock Prices, 200313.jpg
http://www.BillHowell.ca/ProjMini/PuetzUWS/200214 Puetz UWS plus detrended S&P500.png
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/200316 Forbes, Eric Mack - See How Coronavirus Compares To Other Pandemics Through History.jpg
http://www.BillHowell.ca/Cool stuff/suicide - Berk, Dodd, Henry 2006 Australian female suicides with geomanetic activity 1970-2000.png
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/Jerry Tennant 16Jun2020 Healing of [bone, muscle, skin].png
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/Jerry Tennant 16Jun2020 Healing of [bone, muscle, skin].png
http://www.BillHowell.ca/Cool stuff/Davidson - Space weather health indices 23Feb2018.png
http://www.BillHowell.ca/Cool stuff/Davidson - Space weather, am I at risk 23Feb2018.png
http://www.BillHowell.ca/economics, markets/Cool stuff/vote Prechter 1999 Landslide elections & Stock Prices, 200313.jpg
http://www.BillHowell.ca/ProjMini/PuetzUWS/200214 Puetz UWS plus detrended S&P500.png
http://www.BillHowell.ca/ProjMajor/Sun pandemics, health/200316 Forbes, Eric Mack - See How Coronavirus Compares To Other Pandemics Through History.jpg
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/CI_intro/
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Programming code/
http://www.videolan.org/vlc/
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Introduction.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Wrong_theories.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_ContDrift.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_Neal_Adams_vid.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_experiment.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_wrong.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/CI_intro.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Hearing_aids.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Language_translation.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Cognition.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Conclusions.ogv
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_experiment.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_wrong.ogv
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/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Scenes/CI_intro/
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Programming code/
http://www.videolan.org/vlc/
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Introduction.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Wrong_theories.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_ContDrift.ogv
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/ExpandEarth_Neal_Adams_vid.ogv
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https://www.Thunderbolts.info
http://www.holoscience.com/
https://www.thunderbolts.info/
https://www.youtube.com/watch?v=5AUA7XS0TvA
https://www.eixdelmon.com/peratt-instabilities-plasma-columns-z-pinch/
https://www.youtube.com/watch?v=5AUA7XS0TvA
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Howell 120903 Paul Andersons Electric scarring of the Earth.pdf
/home/bill/web/ProjMajor/Electric Universe/Hall- electric geology/Andrew Halls electric geology [thunderblog, video]s.html
https://www.thunderbolts.info/tpod/2010/arch10/100125kimberlite.htm
https://web.mst.edu/om/resume.html
https://web.mst.edu/om/abstracts2005/PKKAutobiography.pdf
https://en.wikipedia.org/wiki/Discovery_of_nuclear_fission
/home/bill/web/References/Climate/Manuel 140519 BBC article 020803 Atomic plans returned to Japan, Kuroda info on Japan bomb.odt
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt Dec03 - Characteristics for the Occurence of a High-Current, Z-Pinch Aurora as Recorded in Antiquity.pdf

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https://www.eixdelmon.com/peratt-instabilities-plasma-columns-z-pinch/
https://www.youtube.com/watch?v=5AUA7XS0TvA
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Howell 120903 Paul Andersons Electric scarring of the Earth.pdf
/home/bill/web/ProjMajor/Electric Universe/Hall- electric geology/Andrew Halls electric geology [thunderblog, video]s.html
https://www.thunderbolts.info/tpod/2010/arch10/100125kimberlite.htm
https://web.mst.edu/om/resume.html
https://web.mst.edu/om/abstracts2005/PKKAutobiography.pdf
https://en.wikipedia.org/wiki/Discovery_of_nuclear_fission
/home/bill/web/References/Climate/Manuel 140519 BBC article 020803 Atomic plans returned to Japan, Kuroda info on Japan bomb.odt
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt Dec03 - Characteristics for the Occurence of a High-Current, Z-Pinch Aurora as Recorded in Antiquity.pdf

/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt etal 07mmdd Z-pinch aurora in antiquity Part II Directionality and source.pdf
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p032fig17a global sea level over last 20ky.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p101fig29 stone bearded man Cacasian facial features Monte Alban.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p111fig31 Dogu figurine from Japan 700BC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p146fig32 12th century Ponte dAmmiraglio near Palermo, spanned Oreto river.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p168tbl33 countries with predicted water shortages.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p170tbl34 global [reserves, production] of natural gas and petroleum.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p171tbl35 status of main commercial fish species in oceans.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig15 indicators of 900y climate cycle phases.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p032fig17a global sea level over last 20ky.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p168tbl33 countries with predicted water shortages.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p170tbl34 global [reserves, production] of natural gas and petroleum.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p171tbl35 status of main commercial fish species in oceans.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p032fig17a global sea level over last 20ky.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p146fig32 12th century Ponte dAmmiraglio near Palermo, spanned Oreto river.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p170tbl34 global [reserves, production] of natural gas and petroleum.jpg
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#Edo Kaal's 'Structured Atom Model' (SAM)
#Edo Kaal presentation 'The Structured Atom Model', EU2017 conference
#Full video transcript
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#Conference webPage & description
#video link
#full video transcript
#Table of Contents for the presentation
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#00:20 nuclear decay processes
#00:33 decay process of U235
#00:46 alpha particle emission, problems with the standard explanations
#02:12 SAM basics: nucleus structure, electrostatic, no [neutron, strong force]
#03:42 SAM periodic table
#04:06 SAM transmutations confirmed in SAFIRE laboratory
#04:40 Inner nuclear structure dictates chemical properties
#05:11 Formation of elements doesn't require stars
#06:06 Other SAM [assumptions, advantages]
#Howell: questions about SAM
#Nomenclature, acronyms
#Definitions: nuclear [material, process, deactivate]s
#References
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
#SAFIRE, Aureon.ca
/home/bill/web/ProjMini/Sierpinski fractal tetrahedra/Sierpinski fractal tetrahedra.HtmWeb.html
https://Thunderbolts.info/
https://structuredatom.org/
https://structuredatom.org/atomizer/atom-viewer
https://structuredatom.org/atomizer/atom-viewer
https://StructuredAtom.org/
https://www.thunderbolts.info/wp/2018/03/11/edwin-kaal-the-proton-electron-atom-a-proposal-for-a-structured-atomic-model-eu2017/
https://www.youtube.com/watch?v=sJKW9VNo2BU
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal: The Proton-Electron Atom, paragraphL.txt
https://en.wikipedia.org/wiki/Platonic_solid/
https://thunderbolts.us7.list-manage.com/track/click?u=1b8e5fc5ffab70f95805dea12&id=1cd856a1a1&e=54f3bc9169
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 nuclear decay processes.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 alpha particle emission.png
https://www.thunderbolts.info/wp/?s=fulgurite
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/SAFIRE electric sun.HtmWeb.html
https://aureon.ca/
https://www.psfc.mit.edu/research/topics/high-field-pathway-fusion-power
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/Aureon.ca plasma technology.HtmWeb.html
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Max_Planck
https://www.lenr-forum.com/forum/thread/6269-sam-the-structured-atom-model-edo-kael/?pageNo=2
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/1872-2020 SP500 index, ratio of opening price to semi-log detrended price.HtmWeb.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/images/Howell: Electric Universe [time, length] multifractal.jpg
https://www.thunderbolts.info/
/home/bill/web/References/Climate/Scherer, Veizer, Shaviv etal 2006 - Interstellar-Terrestrial relations.pdf
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Birkeland rotation in galaxy - not dark matter.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html#Definitions: nuclear [material, process, deactivate]s
https://ieer.org/resource/factsheets/fissile-material-basics/
https://naturalphilosophy.org/
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/home/bill/web/webWork/pMenuTopHelp.html#projMaj
#Edo Kaal's 'Structured Atom Model' (SAM)
#Edo Kaal presentation 'The Structured Atom Model', EU2017 conference
#Full video transcript
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#Conference webPage & description
#video link
#full video transcript
#Table of Contents for the presentation
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#00:20 nuclear decay processes
#00:33 decay process of U235
#00:46 alpha particle emission, problems with the standard explanations
#02:12 SAM basics: nucleus structure, electrostatic, no [neutron, strong force]
#03:42 SAM periodic table
#04:06 SAM transmutations confirmed in SAFIRE laboratory
#04:40 Inner nuclear structure dictates chemical properties
#05:11 Formation of elements doesn't require stars
#06:06 Other SAM [assumptions, advantages]
#Howell: questions about SAM
#Nomenclature, acronyms
#Definitions: nuclear [material, process, deactivate]s
#References
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
#SAFIRE, Aureon.ca
/home/bill/web/ProjMini/Sierpinski fractal tetrahedra/Sierpinski fractal tetrahedra.HtmWeb.html
https://Thunderbolts.info/
https://structuredatom.org/
https://structuredatom.org/atomizer/atom-viewer
https://structuredatom.org/atomizer/atom-viewer
https://StructuredAtom.org/
https://www.thunderbolts.info/wp/2018/03/11/edwin-kaal-the-proton-electron-atom-a-proposal-for-a-structured-atomic-model-eu2017/
https://www.youtube.com/watch?v=sJKW9VNo2BU
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https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
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https://en.wikipedia.org/wiki/Max_Planck
https://www.lenr-forum.com/forum/thread/6269-sam-the-structured-atom-model-edo-kael/?pageNo=2
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https://www.thunderbolts.info/
/home/bill/web/References/Climate/Scherer, Veizer, Shaviv etal 2006 - Interstellar-Terrestrial relations.pdf
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Birkeland rotation in galaxy - not dark matter.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html#Definitions: nuclear [material, process, deactivate]s
https://ieer.org/resource/factsheets/fissile-material-basics/
https://naturalphilosophy.org/
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/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 carbon 14 decays to nitrogen 14: densest packing at work.png
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p019fig13 snowshoe hares vs lynx 1845 to 1935.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig14 global vegetation last glacial ms vs today.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig15 indicators of 900y climate cycle phases.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p028fig17 absorption of radiation bands for various greenhouse gases.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p032fig17a global sea level over last 20ky.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig15 indicators of 900y climate cycle phases.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p146fig32 12th century Ponte dAmmiraglio near Palermo, spanned Oreto river.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p168tbl33 countries with predicted water shortages.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p170tbl34 global [reserves, production] of natural gas and petroleum.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p171tbl35 status of main commercial fish species in oceans.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p172tbl36 forrested land cover of continents 1990-2020.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p172tbl37 known [reserves, years of supply] for various minerals (copy).jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/historical events 1st to 2nd millenial spring, 12785-11890 BC p185.png
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/\1
/home/bill/web/webOther/Wickson, Steven/900y cycle/images- captioned/cover image.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p003fig01 ice core records [Greenland, Antarctica] last 20ky.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images- captioned/p003fig02-04 cropped composite [δ18O, Be-10 ice core, C-14 tree ring] spectra 10ky data.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p004fig03 Be-10 [Greenland, Antarctic] ice cores, C-14 tree ring.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images- captioned/p003fig02-04 cropped composite [δ18O, Be-10 ice core, C-14 tree ring] spectra 10ky data.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p005fig05 interaction [1, 2] ky cycles.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p009fig06 observed sunspots since 1985.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p011fig07 spectral analysis sunspot.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p011fig08 cumulative z-score of monthly sunspot.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p012fig09 sunspots last 400y.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p015fig10 model solar over Holocene vs Greenland ice.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p015fig11 model solar since 1500AD vs historical record.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p018fig12 summary of the phases and postulates of the proposed 900y climate cycle.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p019fig13 snowshoe hares vs lynx 1845 to 1935.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig14 global vegetation last glacial ms vs today.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig15 indicators of 900y climate cycle phases.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p027fig16 Earths solar irradiance energy budget.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p028fig17 absorption of radiation bands for various greenhouse gases.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p032fig17a global sea level over last 20ky.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p035fig18 major worldwide atmospheric circulation patterns.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p037fig19 mass balance of ice sheets in relation to temperature.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p038fig20 temperature last 800ky Antarctic ice cores.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p038fig21 ocean sediment paleotemperature lst 5My.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p040fig22 spectral analysis of delta18O ocean sediment last 900kY.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p040fig22 spectral analysis of delta18O ocean sediment last 900kY v2.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p054fig23 stone sculpture from Lepinski Vir site in Serbia 7kyBC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p060fig24 boat rock painting from Southern Algeria.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p060fig25 giraffe rock painting from Southern Algeria.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p062fig26 Vinca symbols.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p062fig27 Tartaria tablets.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p073fig28 goldfen ox, Maykop culture in Caucasus mid-4th millenium BC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p101fig29 stone bearded man Cacasian facial features Monte Alban.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p101fig30 9-ton head sculpture Olmex site of San Lorenzo.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p111fig31 Dogu figurine from Japan 700BC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p146fig32 12th century Ponte dAmmiraglio near Palermo, spanned Oreto river.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p168tbl33 countries with predicted water shortages.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p170tbl34 global [reserves, production] of natural gas and petroleum.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p171tbl35 status of main commercial fish species in oceans.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p172tbl36 forrested land cover of continents 1990-2020.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p172tbl37 known [reserves, years of supply] for various minerals (copy).jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/historical events 1st to 2nd millenial spring, 12785-11890 BC p185.png
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#Wickson
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/\1
/home/bill/web/webOther/Wickson, Steven/900y cycle/images- captioned/cover image.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p003fig01 ice core records [Greenland, Antarctica] last 20ky.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images- captioned/p003fig02-04 cropped composite [δ18O, Be-10 ice core, C-14 tree ring] spectra 10ky data.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p004fig03 Be-10 [Greenland, Antarctic] ice cores, C-14 tree ring.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images- captioned/p003fig02-04 cropped composite [δ18O, Be-10 ice core, C-14 tree ring] spectra 10ky data.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p005fig05 interaction [1, 2] ky cycles.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p009fig06 observed sunspots since 1985.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p011fig07 spectral analysis sunspot.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p011fig08 cumulative z-score of monthly sunspot.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p012fig09 sunspots last 400y.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p015fig10 model solar over Holocene vs Greenland ice.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p015fig11 model solar since 1500AD vs historical record.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p018fig12 summary of the phases and postulates of the proposed 900y climate cycle.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p019fig13 snowshoe hares vs lynx 1845 to 1935.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig14 global vegetation last glacial ms vs today.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig15 indicators of 900y climate cycle phases.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p027fig16 Earths solar irradiance energy budget.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p028fig17 absorption of radiation bands for various greenhouse gases.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p032fig17a global sea level over last 20ky.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p035fig18 major worldwide atmospheric circulation patterns.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p037fig19 mass balance of ice sheets in relation to temperature.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p038fig20 temperature last 800ky Antarctic ice cores.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p038fig21 ocean sediment paleotemperature lst 5My.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p040fig22 spectral analysis of delta18O ocean sediment last 900kY.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p040fig22 spectral analysis of delta18O ocean sediment last 900kY v2.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p054fig23 stone sculpture from Lepinski Vir site in Serbia 7kyBC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p060fig24 boat rock painting from Southern Algeria.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p060fig25 giraffe rock painting from Southern Algeria.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p062fig26 Vinca symbols.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p062fig27 Tartaria tablets.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p073fig28 goldfen ox, Maykop culture in Caucasus mid-4th millenium BC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p101fig29 stone bearded man Cacasian facial features Monte Alban.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p101fig30 9-ton head sculpture Olmex site of San Lorenzo.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p111fig31 Dogu figurine from Japan 700BC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p146fig32 12th century Ponte dAmmiraglio near Palermo, spanned Oreto river.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p168tbl33 countries with predicted water shortages.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p170tbl34 global [reserves, production] of natural gas and petroleum.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p171tbl35 status of main commercial fish species in oceans.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p172tbl36 forrested land cover of continents 1990-2020.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p172tbl37 known [reserves, years of supply] for various minerals (copy).jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/historical events 1st to 2nd millenial spring, 12785-11890 BC p185.png
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
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#Edo Kaal's 'Structured Atom Model' (SAM)
#Edo Kaal presentation 'The Structured Atom Model', EU2017 conference
#Full video transcript
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#Conference webPage & description
#video link
#full video transcript
#Table of Contents for the presentation
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#00:20 nuclear decay processes
#00:33 decay process of U235
#00:46 alpha particle emission, problems with the standard explanations
#02:12 SAM basics: nucleus structure, electrostatic, no [neutron, strong force]
#03:42 SAM periodic table
#04:06 SAM transmutations confirmed in SAFIRE laboratory
#04:40 Inner nuclear structure dictates chemical properties
#05:11 Formation of elements doesn't require stars
#06:06 Other SAM [assumptions, advantages]
#Howell: questions about SAM
#Nomenclature, acronyms
#Definitions: nuclear [material, process, deactivate]s
#References
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
#SAFIRE, Aureon.ca
/home/bill/web/ProjMini/Sierpinski fractal tetrahedra/Sierpinski fractal tetrahedra.HtmWeb.html
https://Thunderbolts.info/
https://structuredatom.org/
https://structuredatom.org/atomizer/atom-viewer
https://structuredatom.org/atomizer/atom-viewer
https://StructuredAtom.org/
https://www.thunderbolts.info/wp/2018/03/11/edwin-kaal-the-proton-electron-atom-a-proposal-for-a-structured-atomic-model-eu2017/
https://www.youtube.com/watch?v=sJKW9VNo2BU
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal: The Proton-Electron Atom, paragraphL.txt
https://en.wikipedia.org/wiki/Platonic_solid/
https://thunderbolts.us7.list-manage.com/track/click?u=1b8e5fc5ffab70f95805dea12&id=1cd856a1a1&e=54f3bc9169
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 nuclear decay processes.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 alpha particle emission.png
https://www.thunderbolts.info/wp/?s=fulgurite
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/SAFIRE electric sun.HtmWeb.html
https://aureon.ca/
https://www.psfc.mit.edu/research/topics/high-field-pathway-fusion-power
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/Aureon.ca plasma technology.HtmWeb.html
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Max_Planck
https://www.lenr-forum.com/forum/thread/6269-sam-the-structured-atom-model-edo-kael/?pageNo=2
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/1872-2020 SP500 index, ratio of opening price to semi-log detrended price.HtmWeb.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/images/Howell: Electric Universe [time, length] multifractal.jpg
https://www.thunderbolts.info/
/home/bill/web/References/Climate/Scherer, Veizer, Shaviv etal 2006 - Interstellar-Terrestrial relations.pdf
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Birkeland rotation in galaxy - not dark matter.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html#Definitions: nuclear [material, process, deactivate]s
https://ieer.org/resource/factsheets/fissile-material-basics/
https://naturalphilosophy.org/
/home/bill/web/webWork/pMenuTopMenu.html#projMaj
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/home/bill/web/webWork/pMenuTopStatus.html#projMaj
/home/bill/web/webWork/pMenuTopCopyright.html#projMaj
/home/bill/web/webWork/pMenuTopHelp.html#projMaj
#Edo Kaal's 'Structured Atom Model' (SAM)
#Edo Kaal presentation 'The Structured Atom Model', EU2017 conference
#Full video transcript
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#Conference webPage & description
#video link
#full video transcript
#Table of Contents for the presentation
#Gareth Samuel 03Jul2021 The Structured Atom Model | Thunderbolts
#00:20 nuclear decay processes
#00:33 decay process of U235
#00:46 alpha particle emission, problems with the standard explanations
#02:12 SAM basics: nucleus structure, electrostatic, no [neutron, strong force]
#03:42 SAM periodic table
#04:06 SAM transmutations confirmed in SAFIRE laboratory
#04:40 Inner nuclear structure dictates chemical properties
#05:11 Formation of elements doesn't require stars
#06:06 Other SAM [assumptions, advantages]
#Howell: questions about SAM
#Nomenclature, acronyms
#Definitions: nuclear [material, process, deactivate]s
#References
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
#SAFIRE, Aureon.ca
/home/bill/web/ProjMini/Sierpinski fractal tetrahedra/Sierpinski fractal tetrahedra.HtmWeb.html
https://Thunderbolts.info/
https://structuredatom.org/
https://structuredatom.org/atomizer/atom-viewer
https://structuredatom.org/atomizer/atom-viewer
https://StructuredAtom.org/
https://www.thunderbolts.info/wp/2018/03/11/edwin-kaal-the-proton-electron-atom-a-proposal-for-a-structured-atomic-model-eu2017/
https://www.youtube.com/watch?v=sJKW9VNo2BU
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal: The Proton-Electron Atom, paragraphL.txt
https://en.wikipedia.org/wiki/Platonic_solid/
https://thunderbolts.us7.list-manage.com/track/click?u=1b8e5fc5ffab70f95805dea12&id=1cd856a1a1&e=54f3bc9169
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 nuclear decay processes.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 alpha particle emission.png
https://www.thunderbolts.info/wp/?s=fulgurite
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/SAFIRE electric sun.HtmWeb.html
https://aureon.ca/
https://www.psfc.mit.edu/research/topics/high-field-pathway-fusion-power
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/Aureon.ca plasma technology.HtmWeb.html
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
https://en.wikipedia.org/wiki/Max_Planck
https://www.lenr-forum.com/forum/thread/6269-sam-the-structured-atom-model-edo-kael/?pageNo=2
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/1872-2020 SP500 index, ratio of opening price to semi-log detrended price.HtmWeb.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length] multifractal.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/images/Howell: Electric Universe [time, length] multifractal.jpg
https://www.thunderbolts.info/
/home/bill/web/References/Climate/Scherer, Veizer, Shaviv etal 2006 - Interstellar-Terrestrial relations.pdf
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Birkeland rotation in galaxy - not dark matter.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal Structured Atom Model vs Quantum Mechanics.HtmWeb.html#Definitions: nuclear [material, process, deactivate]s
https://ieer.org/resource/factsheets/fissile-material-basics/
https://naturalphilosophy.org/
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/SAM: nature of atom cover atom, front cover, updated.jpg
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/team: Edo Kaal & SAM.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/structuredAtom.org 3D rotate elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Daltons periodic table.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Newlands Law of Octaves.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Mendeleev 1897 periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 basic geometrical shapes making up the nucleus.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/240421 chemical element creation dogma [BigBang, cosmic ray, star [dying low-mass, merging neutron, exploding [white dwarf, massive]]: SeeThePattern YT.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/Aureon.ca elements created in hydrogen plasma reactor.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 SAM team periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 standard model versus Structured Atom Model.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Daltons periodic table.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Newlands Law of Octaves.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Mendeleev 1897 periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 basic geometrical shapes making up the nucleus.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Plantonic solids 25May2024 wikipedia.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 carbon 14 decays to nitrogen 14: densest packing at work.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 proposal for a Structured Atom Model, more postulations.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 the new neutron: not a fundamental particle.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Carl Johnson - Statistical analysis of isotope masses.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 Ethereal Matters LLC animation of atom builder application.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 recurring geometric shapes in the periodic table: carbon group.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 summary of the new atomic model (SAM).png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Kaal EU2017 potential implications of Structured Atom Model (SAM).png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 decay process of U235.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 SAM team periodic table of the elements.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 SAFIRE transmutations confirmed in laboratory.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 formation of elements doesnt require stars.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Howell composite: SAM suggests elements are created in-situe on planet during intense electrical events.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Gareth Samuel 03Jul2021 origin of neutron drip line.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/SAFIRE reactor.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/SAFIRE spherical plasma Lanmuir isotherms and T.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/SAFIRE electric sun surface granules.png
/home/bill/web/My sports & clubs/z_History/natural- FECC Fusion/220928 FECC AGM/220928 FECC Whyte - engineeering problems, image of spark.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/team: SAFIRE [core, consulting].png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/Aureon.ca elements created in hydrogen plasma reactor.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/team: Aureon.ca, visitors.png
/home/bill/web/ProjMajor/Electric Universe/Aureon-SAFIRE/images/aureon_logo.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/Boscovich 1763 Fig1 single Law of forces, including atomic theory.png
/home/bill/web/ProjMajor/Electric Universe/Kaal SAM nucleus/images/nkodama, LENR forum: 3 quarks in a proton.jpg
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 TradingView eyeballed trends for (1872-1926) and (1926-2020).png
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p054fig23 stone sculpture from Lepinski Vir site in Serbia 7kyBC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p060fig24 boat rock painting from Southern Algeria.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p060fig25 giraffe rock painting from Southern Algeria.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p062fig26 Vinca symbols.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p062fig27 Tartaria tablets.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p073fig28 goldfen ox, Maykop culture in Caucasus mid-4th millenium BC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p101fig29 stone bearded man Cacasian facial features Monte Alban.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p101fig30 9-ton head sculpture Olmex site of San Lorenzo.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p111fig31 Dogu figurine from Japan 700BC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p146fig32 12th century Ponte dAmmiraglio near Palermo, spanned Oreto river.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p168tbl33 countries with predicted water shortages.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p170tbl34 global [reserves, production] of natural gas and petroleum.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p171tbl35 status of main commercial fish species in oceans.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p172tbl36 forrested land cover of continents 1990-2020.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images- captioned/p003fig02-04 cropped composite [δ18O, Be-10 ice core, C-14 tree ring] spectra 10ky data.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p004fig03 Be-10 [Greenland, Antarctic] ice cores, C-14 tree ring.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images- captioned/p003fig02-04 cropped composite [δ18O, Be-10 ice core, C-14 tree ring] spectra 10ky data.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p005fig05 interaction [1, 2] ky cycles.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p009fig06 observed sunspots since 1985.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p011fig07 spectral analysis sunspot.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p011fig08 cumulative z-score of monthly sunspot.jpg
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/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p015fig10 model solar over Holocene vs Greenland ice.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p015fig11 model solar since 1500AD vs historical record.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p018fig12 summary of the phases and postulates of the proposed 900y climate cycle.png
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p019fig13 snowshoe hares vs lynx 1845 to 1935.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig14 global vegetation last glacial ms vs today.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p020fig15 indicators of 900y climate cycle phases.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p027fig16 Earths solar irradiance energy budget.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p028fig17 absorption of radiation bands for various greenhouse gases.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p032fig17a global sea level over last 20ky.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p035fig18 major worldwide atmospheric circulation patterns.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p037fig19 mass balance of ice sheets in relation to temperature.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p038fig20 temperature last 800ky Antarctic ice cores.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p038fig21 ocean sediment paleotemperature lst 5My.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p040fig22 spectral analysis of delta18O ocean sediment last 900kY.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p040fig22 spectral analysis of delta18O ocean sediment last 900kY v2.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p054fig23 stone sculpture from Lepinski Vir site in Serbia 7kyBC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p060fig24 boat rock painting from Southern Algeria.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p060fig25 giraffe rock painting from Southern Algeria.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p062fig26 Vinca symbols.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p062fig27 Tartaria tablets.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p073fig28 goldfen ox, Maykop culture in Caucasus mid-4th millenium BC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p101fig29 stone bearded man Cacasian facial features Monte Alban.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p101fig30 9-ton head sculpture Olmex site of San Lorenzo.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p111fig31 Dogu figurine from Japan 700BC.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p146fig32 12th century Ponte dAmmiraglio near Palermo, spanned Oreto river.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p168tbl33 countries with predicted water shortages.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p170tbl34 global [reserves, production] of natural gas and petroleum.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p171tbl35 status of main commercial fish species in oceans.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p172tbl36 forrested land cover of continents 1990-2020.jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/p172tbl37 known [reserves, years of supply] for various minerals (copy).jpg
/home/bill/web/webOther/Wickson, Steven/900y cycle/images/historical events 1st to 2nd millenial spring, 12785-11890 BC p185.png
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#???
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/home/bill/web/webWork/fileops run webSite general.sh
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#Transcription notes : 
#Tools to [download, analyse, etc] * [webPages, blogs, speech]
#Howell's blog analysis problems:
#Blogs: [deep, broad]er themes
#[rapid, complete] flips of people's thinking (eg in science)
#Blog explosion of [diversity, dimensionality]
#The [blessing, curse] of dimensionality
#Consensus is poison.  Politics needs this poison.
#Cooperative-competitive, top down - bottom-up
#[motherhood, day car, kindergarten] constraints of modern intellectualism
#Programming the programmers
#The [blessing, curse] of dimensionality
#Conscious mind, resonant brain
https://www.youtube.com/watch?v=XObk07uabLI

https://cloud.google.com/speech-to-text/docs/async-recognize
https://youtubecommentsdownloader.com/dashboard
https://chatgpt.com/g/g-kDiRb035C-comments-analyzer
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 blog comments.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html



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/home/bill/web/webWork/pMenuTopHelp.html#projMaj
#Transcription notes : 
#Tools to [download, analyse, etc] * [webPages, blogs, speech]
#Howell's blog analysis problems:
#Blogs: [deep, broad]er themes
#[rapid, complete] flips of people's thinking (eg in science)
#Blog explosion of [diversity, dimensionality]
#The [blessing, curse] of dimensionality
#Consensus is poison.  Politics needs this poison.
#Cooperative-competitive, top down - bottom-up
#[motherhood, day car, kindergarten] constraints of modern intellectualism
#Programming the programmers
#The [blessing, curse] of dimensionality
#Conscious mind, resonant brain
https://www.youtube.com/watch?v=XObk07uabLI

https://cloud.google.com/speech-to-text/docs/async-recognize
https://youtubecommentsdownloader.com/dashboard
https://chatgpt.com/g/g-kDiRb035C-comments-analyzer
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 blog comments.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html



/home/bill/web/webWork/pMenuTopMenu.html#projMaj
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/home/bill/web/webWork/pMenuTopStatus.html#projMaj
/home/bill/web/webWork/pMenuTopCopyright.html#projMaj
/home/bill/web/webWork/pMenuTopHelp.html#projMaj
#Transcription notes : 
#Tools to [download, analyse, etc] * [webPages, blogs, speech]
#Howell's blog analysis problems:
#Blogs: [deep, broad]er themes
#[rapid, complete] flips of people's thinking (eg in science)
#Blog explosion of [diversity, dimensionality]
#The [blessing, curse] of dimensionality
#Consensus is poison.  Politics needs this poison.
#Cooperative-competitive, top down - bottom-up
#[motherhood, day car, kindergarten] constraints of modern intellectualism
#Programming the programmers
#The [blessing, curse] of dimensionality
#Conscious mind, resonant brain
https://www.youtube.com/watch?v=XObk07uabLI

https://cloud.google.com/speech-to-text/docs/async-recognize
https://youtubecommentsdownloader.com/dashboard
https://chatgpt.com/g/g-kDiRb035C-comments-analyzer
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 blog comments.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html



/home/bill/web/webWork/pMenuTopMenu.html#projMaj
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/home/bill/web/webWork/pMenuTopStatus.html#projMaj
/home/bill/web/webWork/pMenuTopCopyright.html#projMaj
/home/bill/web/webWork/pMenuTopHelp.html#projMaj
#Transcription notes : 
#Tools to [download, analyse, etc] * [webPages, blogs, speech]
#Howell's blog analysis problems:
#Blogs: [deep, broad]er themes
#[rapid, complete] flips of people's thinking (eg in science)
#Blog explosion of [diversity, dimensionality]
#The [blessing, curse] of dimensionality
#Consensus is poison.  Politics needs this poison.
#Cooperative-competitive, top down - bottom-up
#[motherhood, day car, kindergarten] constraints of modern intellectualism
#Programming the programmers
#The [blessing, curse] of dimensionality
#Conscious mind, resonant brain
https://www.youtube.com/watch?v=XObk07uabLI

https://cloud.google.com/speech-to-text/docs/async-recognize
https://youtubecommentsdownloader.com/dashboard
https://chatgpt.com/g/g-kDiRb035C-comments-analyzer
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 blog comments.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html



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#???
#???
/home/bill/web/bin/fileops.sh
/home/bill/web/bin/webSite update notes.txt
/home/bill/web/bin/0_test/fileops/povrL_pStrP_fixLinks/
/home/bill/web/webWork/fileops run webSite general.sh
/home/bill/web/webWork/fileops run commentary.html
/home/bill/web/bin/fileops.sh
/home/bill/web/bin/fileops notes.txt
/home/bill/web/bin/fileops [nomenclature, docs].html
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#???
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#???
#???
/home/bill/web/CompLangs/complexity, abstraction/con-joint current code.html
/home/bill/web/CompLangs/complexity, abstraction/con-joint previous code that I remembered.html
/home/bill/web/CompLangs/complexity, abstraction/con-joint forgotten bash code.html
/home/bill/web/CompLangs/complexity, abstraction/con-joint old QNial code.html
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#???
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#???
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/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript of priceTimeFractals.HtmWeb.html
/home/bill/web/CompLangs/PineScript/0_PineScript notes.txt
/home/bill/web/CompLangs/PineScript/1_PineScript errors.txt
/home/bill/web/ProjMini/PuetzUWS/Howell - References related to Puetz [H]UWS.html
/home/bill/web/CompLangs/PineScript/Kivanc Ozbilgics Turtle Trade PineScript - documention.txt
/home/bill/web/CompLangs/PineScript/Kivanc Ozbilgics Turtle Trade PineScript, plus 8-year detrended SP500.txt
/home/bill/web/CompLangs/PineScript/RicardoSantos, Function Polynomial Regression.txt
/home/bill/web/CompLangs/PineScript/sickojacko maximum [,relative] drawdown calculating functions.txt
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/home/bill/web/CompLangs/PineScript/0_PineScript notes.txt
/home/bill/web/CompLangs/PineScript/1_PineScript errors.txt
/home/bill/web/ProjMini/PuetzUWS/Howell - References related to Puetz [H]UWS.html
/home/bill/web/CompLangs/PineScript/Kivanc Ozbilgics Turtle Trade PineScript - documention.txt
/home/bill/web/CompLangs/PineScript/Kivanc Ozbilgics Turtle Trade PineScript, plus 8-year detrended SP500.txt
/home/bill/web/CompLangs/PineScript/RicardoSantos, Function Polynomial Regression.txt
/home/bill/web/CompLangs/PineScript/sickojacko maximum [,relative] drawdown calculating functions.txt
/home/bill/web/CompLangs/PineScript/TradingView auto fib extension.txt
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/home/bill/web/webWork/pMenuTopHelp.html#computer
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript of priceTimeFractals.HtmWeb.html
/home/bill/web/CompLangs/PineScript/0_PineScript notes.txt
/home/bill/web/CompLangs/PineScript/1_PineScript errors.txt
/home/bill/web/ProjMini/PuetzUWS/Howell - References related to Puetz [H]UWS.html
/home/bill/web/CompLangs/PineScript/Kivanc Ozbilgics Turtle Trade PineScript - documention.txt
/home/bill/web/CompLangs/PineScript/Kivanc Ozbilgics Turtle Trade PineScript, plus 8-year detrended SP500.txt
/home/bill/web/CompLangs/PineScript/RicardoSantos, Function Polynomial Regression.txt
/home/bill/web/CompLangs/PineScript/sickojacko maximum [,relative] drawdown calculating functions.txt
/home/bill/web/CompLangs/PineScript/TradingView auto fib extension.txt
/home/bill/web/webWork/pMenuTopMenu.html#computer
./
/home/bill/web/webWork/pMenuTopStatus.html#computer
/home/bill/web/webWork/pMenuTopCopyright.html#computer
/home/bill/web/webWork/pMenuTopHelp.html#computer
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript of priceTimeFractals.HtmWeb.html
/home/bill/web/CompLangs/PineScript/0_PineScript notes.txt
/home/bill/web/CompLangs/PineScript/1_PineScript errors.txt
/home/bill/web/ProjMini/PuetzUWS/Howell - References related to Puetz [H]UWS.html
/home/bill/web/CompLangs/PineScript/Kivanc Ozbilgics Turtle Trade PineScript - documention.txt
/home/bill/web/CompLangs/PineScript/Kivanc Ozbilgics Turtle Trade PineScript, plus 8-year detrended SP500.txt
/home/bill/web/CompLangs/PineScript/RicardoSantos, Function Polynomial Regression.txt
/home/bill/web/CompLangs/PineScript/sickojacko maximum [,relative] drawdown calculating functions.txt
/home/bill/web/CompLangs/PineScript/TradingView auto fib extension.txt
/home/bill/web/webWork/pMenuTopMenu.html#computer
./
/home/bill/web/webWork/pMenuTopStatus.html#computer
/home/bill/web/webWork/pMenuTopCopyright.html#computer
/home/bill/web/webWork/pMenuTopHelp.html#computer
#Initial setup
#So, what does the user have to do to [get, adjust] output?
#What CAN the user change?
#Initial observations
#Initial questions
#Adapt my PineScript program to other [symbol, application]s
#PineScript, SP500USD chart - multi-fractal on-chart comments
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html

https://www.tradingview.com/chart/Dpe2yXH0/
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView, 1928-2020 yahoo finance.ods
https://in.tradingview.com/script/qQd2lucH-SP500USD-time-price-multi-fractals/
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html
/home/bill/web/ProjMini/PuetzUWS/Howell - References related to Puetz [H]UWS.html
https://www.tradingview.com/u/bill_howell/
/home/bill/web/webWork/pMenuTopMenu.html#computer
./
/home/bill/web/webWork/pMenuTopStatus.html#computer
/home/bill/web/webWork/pMenuTopCopyright.html#computer
/home/bill/web/webWork/pMenuTopHelp.html#computer
#Initial setup
#So, what does the user have to do to [get, adjust] output?
#What CAN the user change?
#Initial observations
#Initial questions
#Adapt my PineScript program to other [symbol, application]s
#PineScript, SP500USD chart - multi-fractal on-chart comments
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html

https://www.tradingview.com/chart/Dpe2yXH0/
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView, 1928-2020 yahoo finance.ods
https://in.tradingview.com/script/qQd2lucH-SP500USD-time-price-multi-fractals/
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html
/home/bill/web/ProjMini/PuetzUWS/Howell - References related to Puetz [H]UWS.html
https://www.tradingview.com/u/bill_howell/
/home/bill/web/webWork/pMenuTopMenu.html#computer
./
/home/bill/web/webWork/pMenuTopStatus.html#computer
/home/bill/web/webWork/pMenuTopCopyright.html#computer
/home/bill/web/webWork/pMenuTopHelp.html#computer
#Initial setup
#So, what does the user have to do to [get, adjust] output?
#What CAN the user change?
#Initial observations
#Initial questions
#Adapt my PineScript program to other [symbol, application]s
#PineScript, SP500USD chart - multi-fractal on-chart comments
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html

https://www.tradingview.com/chart/Dpe2yXH0/
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView, 1928-2020 yahoo finance.ods
https://in.tradingview.com/script/qQd2lucH-SP500USD-time-price-multi-fractals/
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html
/home/bill/web/ProjMini/PuetzUWS/Howell - References related to Puetz [H]UWS.html
https://www.tradingview.com/u/bill_howell/
/home/bill/web/webWork/pMenuTopMenu.html#computer
./
/home/bill/web/webWork/pMenuTopStatus.html#computer
/home/bill/web/webWork/pMenuTopCopyright.html#computer
/home/bill/web/webWork/pMenuTopHelp.html#computer
#Initial setup
#So, what does the user have to do to [get, adjust] output?
#What CAN the user change?
#Initial observations
#Initial questions
#Adapt my PineScript program to other [symbol, application]s
#PineScript, SP500USD chart - multi-fractal on-chart comments
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html

https://www.tradingview.com/chart/Dpe2yXH0/
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView, 1928-2020 yahoo finance.ods
https://in.tradingview.com/script/qQd2lucH-SP500USD-time-price-multi-fractals/
/home/bill/web/CompLangs/PineScript/Howell - TradingView PineScript [description, problem, debug].html
/home/bill/web/ProjMini/PuetzUWS/Howell - References related to Puetz [H]UWS.html
https://www.tradingview.com/u/bill_howell/
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Special comments
#83year detrended SP500
#Oncolytics - possible bull wedge formation?
#Regular [1,6] month market views
#International market indexes [SP500, NASDAQ, SHCOMP, 10y T-bill]
#Interest rates, currency [DXY,CNYUSD]
#crypto [BTC,ETH,COIN], 10y T-bill
#USOIL, Canadian [XEG ETF,CNQ,IMO,SU], 10y T-bill
#pharma [CRSP,NTLA,BEAM,BLUE,EDIT,ONC]
https://tr.tradingview.com/script/pB5nv16J/?utm_source=notification_email&utm_medium=email&utm_campaign=notification_pubscript_update
https://www.tradingview.com/script/12M8Jqu6-Function-Polynomial-Regression/
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Special comments
#83year detrended SP500
#Oncolytics - possible bull wedge formation?
#Regular [1,6] month market views
#International market indexes [SP500, NASDAQ, SHCOMP, 10y T-bill]
#Interest rates, currency [DXY,CNYUSD]
#crypto [BTC,ETH,COIN], 10y T-bill
#USOIL, Canadian [XEG ETF,CNQ,IMO,SU], 10y T-bill
#pharma [CRSP,NTLA,BEAM,BLUE,EDIT,ONC]
https://tr.tradingview.com/script/pB5nv16J/?utm_source=notification_email&utm_medium=email&utm_campaign=notification_pubscript_update
https://www.tradingview.com/script/12M8Jqu6-Function-Polynomial-Regression/
/home/bill/web/economics, markets/0_BillHowell market news/210421 SP500 83y detrend, 10y T-bill, Chinese Yuan per USD, Oil, Gold, BitCoin.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 pharma ONC, 10yT-bill 5year.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 SP500, NASDAQ, SHCOMP, 10y T-bill 2004-2021.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 SP500, NASDAQ, SHCOMP, 10y T-bill 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 SP500, NASDAQ, SHCOMP, 10y T-bill 6Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 Interest rates, currency [DXY,CNYUSD] 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 Interest rates, currency [DXY,CNYUSD] 6Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 crypto [BTC,ETH,COIN], 10y T-bill 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 crypto [BTC,ETH,COIN], 10y T-bill 6Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 USOIL, Canadian [XEG ETF,CNQ,IMO,SU], 10y T-bill 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 USOIL, Canadian [XEG ETF,CNQ,IMO,SU], 10y T-bill 6Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 pharma [CRSP,NTLA,BEAM,BLUE,EDIT,ONC] 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 pharma [CRSP,NTLA,BEAM,BLUE,EDIT,ONC] 6Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 SP500 83y detrend, 10y T-bill, Chinese Yuan per USD, Oil, Gold, BitCoin.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 pharma ONC, 10yT-bill 5year.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 SP500, NASDAQ, SHCOMP, 10y T-bill 2004-2021.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 SP500, NASDAQ, SHCOMP, 10y T-bill 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 SP500, NASDAQ, SHCOMP, 10y T-bill 6Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 Interest rates, currency [DXY,CNYUSD] 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 Interest rates, currency [DXY,CNYUSD] 6Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 crypto [BTC,ETH,COIN], 10y T-bill 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 crypto [BTC,ETH,COIN], 10y T-bill 6Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 USOIL, Canadian [XEG ETF,CNQ,IMO,SU], 10y T-bill 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 USOIL, Canadian [XEG ETF,CNQ,IMO,SU], 10y T-bill 6Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 pharma [CRSP,NTLA,BEAM,BLUE,EDIT,ONC] 1Month.png
/home/bill/web/economics, markets/0_BillHowell market news/210421 pharma [CRSP,NTLA,BEAM,BLUE,EDIT,ONC] 6Month.png
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
https://www.tradingview.com/chart/DXY/8FoOGfc1-Currency-crypto-over-5-years-1-year-1-month-5-days/
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
https://www.tradingview.com/chart/DXY/8FoOGfc1-Currency-crypto-over-5-years-1-year-1-month-5-days/
/home/bill/web/economics, markets/currency-crypto/Currency-crypto 5 year 09Jan2017-16Dec2020.png
/home/bill/web/economics, markets/currency-crypto/Currency-crypto 1 year 26Feb-16Dec2020.png
/home/bill/web/economics, markets/currency-crypto/Currency-crypto 1 month 23Nov-16Dec2020.png
/home/bill/web/economics, markets/currency-crypto/Currency-crypto 5 day 11-16Dec2020.png
/home/bill/web/economics, markets/currency-crypto/crypto versus 10 year [interest rate, bonds], 5 year 14Dec2015-17Dec2020.png
/home/bill/web/economics, markets/currency-crypto/crypto versus 10 year [interest rate, bonds], 1 year 06No2019-17Dec2020.png
/home/bill/web/economics, markets/currency-crypto/Currency-crypto 5 year 09Jan2017-16Dec2020.png
/home/bill/web/economics, markets/currency-crypto/Currency-crypto 1 year 26Feb-16Dec2020.png
/home/bill/web/economics, markets/currency-crypto/Currency-crypto 1 month 23Nov-16Dec2020.png
/home/bill/web/economics, markets/currency-crypto/Currency-crypto 5 day 11-16Dec2020.png
/home/bill/web/economics, markets/currency-crypto/crypto versus 10 year [interest rate, bonds], 5 year 14Dec2015-17Dec2020.png
/home/bill/web/economics, markets/currency-crypto/crypto versus 10 year [interest rate, bonds], 1 year 06No2019-17Dec2020.png
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Model results
#SP500 & DJIA indexes back to 1872
#What's [wrong with, missing from] the current model?
#Are [political, economic, philosophical, psychological, sociological] theories unimportant?
#And for the future?
#???
#Was 2007-2009 the modern equivalent to the 1929 crash?
#How can the Great Pricing Waves be correlated with 'dead' systems?
#Inspirations
#References
#Data, Methodolgy, Applications, Software
#Data sources
#Methodolgy
#Photograph paper graphs
#GNU Image Manipulation Program (gimp) - Image preparation for each curve of each graph
#gimp - [rotate, crop] graphs from each cell-photo
#gimp - remove [adjacent, cross-over] curves for each curve-file : 
#gimp :  curve, make [continuous, mono-color]
#Digitize graph curves using https://www.digitizeit.xyz/ software
#IMPORTANT issues with digitization of curves
#Warehousing graphical data
#Set timePoints for transitions between price [revolutions, equilibria]
#Do single-linear-regressions of price [revolution, equilibrium] segments
#GNUplot - create graph of nominal data
#[calculate, plot] detrended results for each time segment
#Simple template for working on a dataset
#Software tools - general comments
http://www.uct-news.com/
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 line segments for GNUplots.dat
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/Qnial/MY_NDFS/fit_linearRegress.ndf
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.dat
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.plt
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993 detrended.plt
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1850 BC to 2020 AD prices detrended.plt
https://www.digitizeit.xyz/
https://www.gimp.org
http://www.gnuplot.info/
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 linear regression raw data.dat
/home/bill/web/ProjMajor/Sun civilisations/_Civilisations and the sun.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Model results
#SP500 & DJIA indexes back to 1872
#What's [wrong with, missing from] the current model?
#Are [political, economic, philosophical, psychological, sociological] theories unimportant?
#And for the future?
#???
#Was 2007-2009 the modern equivalent to the 1929 crash?
#How can the Great Pricing Waves be correlated with 'dead' systems?
#Inspirations
#References
#Data, Methodolgy, Applications, Software
#Data sources
#Methodolgy
#Photograph paper graphs
#GNU Image Manipulation Program (gimp) - Image preparation for each curve of each graph
#gimp - [rotate, crop] graphs from each cell-photo
#gimp - remove [adjacent, cross-over] curves for each curve-file : 
#gimp :  curve, make [continuous, mono-color]
#Digitize graph curves using https://www.digitizeit.xyz/ software
#IMPORTANT issues with digitization of curves
#Warehousing graphical data
#Set timePoints for transitions between price [revolutions, equilibria]
#Do single-linear-regressions of price [revolution, equilibrium] segments
#GNUplot - create graph of nominal data
#[calculate, plot] detrended results for each time segment
#Simple template for working on a dataset
#Software tools - general comments
http://www.uct-news.com/
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 line segments for GNUplots.dat
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/Qnial/MY_NDFS/fit_linearRegress.ndf
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.dat
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.plt
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993 detrended.plt
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1850 BC to 2020 AD prices detrended.plt
https://www.digitizeit.xyz/
https://www.gimp.org
http://www.gnuplot.info/
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 linear regression raw data.dat
/home/bill/web/ProjMajor/Sun civilisations/_Civilisations and the sun.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Model results
#SP500 & DJIA indexes back to 1872
#What's [wrong with, missing from] the current model?
#Are [political, economic, philosophical, psychological, sociological] theories unimportant?
#And for the future?
#???
#Was 2007-2009 the modern equivalent to the 1929 crash?
#How can the Great Pricing Waves be correlated with 'dead' systems?
#Inspirations
#References
#Data, Methodolgy, Applications, Software
#Data sources
#Methodolgy
#Photograph paper graphs
#GNU Image Manipulation Program (gimp) - Image preparation for each curve of each graph
#gimp - [rotate, crop] graphs from each cell-photo
#gimp - remove [adjacent, cross-over] curves for each curve-file : 
#gimp :  curve, make [continuous, mono-color]
#Digitize graph curves using https://www.digitizeit.xyz/ software
#IMPORTANT issues with digitization of curves
#Warehousing graphical data
#Set timePoints for transitions between price [revolutions, equilibria]
#Do single-linear-regressions of price [revolution, equilibrium] segments
#GNUplot - create graph of nominal data
#[calculate, plot] detrended results for each time segment
#Simple template for working on a dataset
#Software tools - general comments
http://www.uct-news.com/
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 line segments for GNUplots.dat
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/Qnial/MY_NDFS/fit_linearRegress.ndf
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.dat
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.plt
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993 detrended.plt
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1850 BC to 2020 AD prices detrended.plt
https://www.digitizeit.xyz/
https://www.gimp.org
http://www.gnuplot.info/
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 linear regression raw data.dat
/home/bill/web/ProjMajor/Sun civilisations/_Civilisations and the sun.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Model results
#SP500 & DJIA indexes back to 1872
#What's [wrong with, missing from] the current model?
#Are [political, economic, philosophical, psychological, sociological] theories unimportant?
#And for the future?
#???
#Was 2007-2009 the modern equivalent to the 1929 crash?
#How can the Great Pricing Waves be correlated with 'dead' systems?
#Inspirations
#References
#Data, Methodolgy, Applications, Software
#Data sources
#Methodolgy
#Photograph paper graphs
#GNU Image Manipulation Program (gimp) - Image preparation for each curve of each graph
#gimp - [rotate, crop] graphs from each cell-photo
#gimp - remove [adjacent, cross-over] curves for each curve-file : 
#gimp :  curve, make [continuous, mono-color]
#Digitize graph curves using https://www.digitizeit.xyz/ software
#IMPORTANT issues with digitization of curves
#Warehousing graphical data
#Set timePoints for transitions between price [revolutions, equilibria]
#Do single-linear-regressions of price [revolution, equilibrium] segments
#GNUplot - create graph of nominal data
#[calculate, plot] detrended results for each time segment
#Simple template for working on a dataset
#Software tools - general comments
http://www.uct-news.com/
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 line segments for GNUplots.dat
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1_Fischer 1200-2020.ods
/home/bill/web/Qnial/MY_NDFS/fit_linearRegress.ndf
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.dat
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.plt
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993 detrended.plt
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/1850 BC to 2020 AD prices detrended.plt
https://www.digitizeit.xyz/
https://www.gimp.org
http://www.gnuplot.info/
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 linear regression raw data.dat
/home/bill/web/ProjMajor/Sun civilisations/_Civilisations and the sun.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
/home/bill/web/webWork/link garbage.html
/home/bill/web/pubinfo.html#EIR
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
/home/bill/web/webWork/link garbage.html
/home/bill/web/pubinfo.html#EIR
/home/bill/web/webWork/link lost.html
/home/bill/web/webWork/link garbage.html
/home/bill/web/webWork/link lost.html
/home/bill/web/webWork/link garbage.html
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Key [results, comments]
#Play with the [time, mind]-bending perspective yourself
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Key [results, comments]
#Play with the [time, mind]-bending perspective yourself
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 TradingView eyeballed trends for (1872-1926) and (1926-2020).png
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 TradingView eyeballed trends for (1872-1926) and (1926-2020).png
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
date.html#582
0583.html
0581.html
0576.html
mailto:jjain@cisco.com
http://www.yardeni.com/public/sp52_c.pdf
http://stats.bls.gov:80/cgi-bin/surveymost?wp
index.html#582
subject.html#582
author.html#582
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
date.html#582
0583.html
0581.html
0576.html
mailto:jjain@cisco.com
http://www.yardeni.com/public/sp52_c.pdf
http://stats.bls.gov:80/cgi-bin/surveymost?wp
index.html#582
subject.html#582
author.html#582
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
date.html#582
0583.html
0581.html
0576.html
mailto:jjain@cisco.com
http://www.yardeni.com/public/sp52_c.pdf
http://stats.bls.gov:80/cgi-bin/surveymost?wp
index.html#582
subject.html#582
author.html#582
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
date.html#582
0583.html
0581.html
0576.html
mailto:jjain@cisco.com
http://www.yardeni.com/public/sp52_c.pdf
http://stats.bls.gov:80/cgi-bin/surveymost?wp
index.html#582
subject.html#582
author.html#582
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Key [results, comments]
#Play with the [time, mind]-bending perspective yourself
#Ratio of actual to semi-log detrended data : [advantages, disadvantages]
#Future related work
#Comparison of [TradingView, Yahoo finance] data
#[data, software] cart [description, links]
https://www.tradingview.com/chart/SPX500/oQp9l9V5-S-P-500-compared-with-2009/?utm_source=notification_email&utm_medium=email&utm_campaign=notification_follower_idea_update
https://www.gimp.org/
/home/bill/web/ProjMajor/Sun civilisations/Howell - radioisotopes and history.jpg
http://www.uct-news.com/
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView download.dat
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView, 1928-2020 yahoo finance.ods
https://www.tradingview.com/pine-script-docs/en/v4/index.html
http://gnuplot.info/
https://www.gimp.org/
/home/bill/web/bin/gnuplot.sh
https://github.com/vrthra/qnial
https://www.tradingview.com/u/TexasWestCapital/
/home/bill/web/economics, markets/market data/detrend/200913/1872-2020 SP500 index semi-log detrended 1871-1926 & 1926-2020, TradingView.xcf
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView, 1928-2020 yahoo finance.ods
/home/bill/web/economics, markets/market data/detrend/
/home/bill/web/economics, markets/market data/detrend/200913/1872-2020 SP500 index semi-log detrended 1871-1926 & 1926-2020, TradingView.xcf
/home/bill/web/bin/
/home/bill/web/Qnial/MY_NDFS/semi-log formula.ndf
https://harrydent.com/
/home/bill/web/Qnial/MY_NDFS/
/home/bill/web/webWork/pMenuTopMenu.html#market
./
/home/bill/web/webWork/pMenuTopStatus.html#market
/home/bill/web/webWork/pMenuTopCopyright.html#market
/home/bill/web/webWork/pMenuTopHelp.html#market
#Key [results, comments]
#Play with the [time, mind]-bending perspective yourself
#Ratio of actual to semi-log detrended data : [advantages, disadvantages]
#Future related work
#Comparison of [TradingView, Yahoo finance] data
#[data, software] cart [description, links]
https://www.tradingview.com/chart/SPX500/oQp9l9V5-S-P-500-compared-with-2009/?utm_source=notification_email&utm_medium=email&utm_campaign=notification_follower_idea_update
https://www.gimp.org/
/home/bill/web/ProjMajor/Sun civilisations/Howell - radioisotopes and history.jpg
http://www.uct-news.com/
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView download.dat
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView, 1928-2020 yahoo finance.ods
https://www.tradingview.com/pine-script-docs/en/v4/index.html
http://gnuplot.info/
https://www.gimp.org/
/home/bill/web/bin/gnuplot.sh
https://github.com/vrthra/qnial
https://www.tradingview.com/u/TexasWestCapital/
/home/bill/web/economics, markets/market data/detrend/200913/1872-2020 SP500 index semi-log detrended 1871-1926 & 1926-2020, TradingView.xcf
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 1872-2020 TradingView, 1928-2020 yahoo finance.ods
/home/bill/web/economics, markets/market data/detrend/
/home/bill/web/economics, markets/market data/detrend/200913/1872-2020 SP500 index semi-log detrended 1871-1926 & 1926-2020, TradingView.xcf
/home/bill/web/bin/
/home/bill/web/Qnial/MY_NDFS/semi-log formula.ndf
https://harrydent.com/
/home/bill/web/Qnial/MY_NDFS/
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 TradingView eyeballed trends for (1872-1926) and (1926-2020).png
/home/bill/web/economics, markets/market data/detrend/200913/1872-2020 SP500 index semi-log detrended 1871-1926 & 1926-2020, TradingView.png
/home/bill/web/economics, markets/market data/detrend/200913/1872-2020 SP500 index semi-log detrended 1871-1926 & 1926-2020, TradingView - [time, mind] bend.jpg
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/1872-2020 SP500 index using TradingView & yahoo finance.png
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/1872-2020 SP500 index using TradingView & 2 times yahoo finance.png
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/SP500 TradingView eyeballed trends for (1872-1926) and (1926-2020).png
/home/bill/web/economics, markets/market data/detrend/200913/1872-2020 SP500 index semi-log detrended 1871-1926 & 1926-2020, TradingView.png
/home/bill/web/economics, markets/market data/detrend/200913/1872-2020 SP500 index semi-log detrended 1871-1926 & 1926-2020, TradingView - [time, mind] bend.jpg
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/1872-2020 SP500 index using TradingView & yahoo finance.png
/home/bill/web/economics, markets/market data/SLregress/200912 semi-log/1872-2020 SP500 index using TradingView & 2 times yahoo finance.png
/home/bill/web/webWork/pMenuTopMenu.html#projMin
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/home/bill/web/webWork/pMenuTopCopyright.html#projMin
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/home/bill/web/economics, markets/PE Schiller forward vs 10yr Tbills/Howell - SP500 PE Shiller ratios versus 10 year Treasury bond yields, with earnings growth & discount factors.ods
https://www.multpl.com/s-p-500-real-earnings-growth
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https://www.marketwatch.com/story/the-stock-market-will-lose-crucial-support-as-millennials-tap-401ks-for-cash-2020-04-09?mod=newsviewer_click10Apr2020
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http://www.BillHowell.ca/economics, markets/market data/SLregress/200912 semi-log/SP500 TradingView eyeballed trends for (1872-1926) and (1926-2020).png
http://www.BillHowell.ca/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
http://www.BillHowell.ca/economics, markets/Cool stuff/house Hulbert, Philadelphia Fed: Seven decades of no capital gains.png
https://www.marketwatch.com/story/investing-in-real-estate-isnt-all-its-cracked-up-to-be-f8f97e8b?mod=mark-hulbert
http://www.BillHowell.ca/
http://www.BillHowell.ca/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.png
http://www.BillHowell.ca/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993 detrended.png
http://www.BillHowell.ca/economics, markets/market data/SLregress/200912 semi-log/SP500 TradingView eyeballed trends for (1872-1926) and (1926-2020).png
http://www.BillHowell.ca/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
http://www.BillHowell.ca/economics, markets/Cool stuff/house Hulbert, Philadelphia Fed: Seven decades of no capital gains.png
https://www.marketwatch.com/story/investing-in-real-estate-isnt-all-its-cracked-up-to-be-f8f97e8b?mod=mark-hulbert
http://www.BillHowell.ca/Personal/24115 IDtheft/Howell IDtheft.HtmWeb.html
http://www.BillHowell.ca/Personal/24115 IDtheft/Howell IDtheft.HtmWeb.html
http://www.BillHowell.ca/Personal/24115 IDtheft/Howell IDtheft.HtmWeb.html
http://www.BillHowell.ca/Personal/24115 IDtheft/Howell IDtheft.HtmWeb.html
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https://www.youtube.com/watch?v=eAH59Wh-Ymo&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=2
https://www.youtube.com/watch?v=LQJs8BHtTXg&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=3
https://www.youtube.com/watch?v=eAH59Wh-Ymo&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=2
https://www.youtube.com/watch?v=7n5Dq-c0_Zw&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=5
https://www.youtube.com/watch?v=x-Veqz6JpQY&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=6
https://www.youtube.com/watch?v=TSZ9wPPcszo&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=7
https://www.youtube.com/watch?v=7n5Dq-c0_Zw&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=8
https://www.youtube.com/watch?v=P8cP1ChW3io&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=9
https://www.youtube.com/watch?v=7YT8bfZI8fs&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=10
https://www.youtube.com/watch?v=eGwQ0aqrQ8c&t=0s
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https://www.youtube.com/watch?v=Aga-fA6iUm4&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=12
https://www.youtube.com/watch?v=f5cPzD2a_Ao&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=13
https://www.youtube.com/watch?v=laXhTcko-lg&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=14
https://www.youtube.com/watch?v=glehaMc-w98&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=15
https://www.youtube.com/watch?v=kJQ_FsmU19g&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=16
https://www.youtube.com/watch?v=OhBVWX0IkIk&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=17
https://www.youtube.com/watch?v=Je3Qu-xMuxM&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=18
https://www.youtube.com/watch?v=50Y5r9HeR2w&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=19
https://www.youtube.com/watch?v=yUFYnVXbLoY&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=20
https://www.youtube.com/watch?v=ZCYMAs4cqRU&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=21
https://www.youtube.com/watch?v=4TQIiVlHYxY&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=22
https://www.youtube.com/watch?v=YpZKwbdbsJ4&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=23
https://www.youtube.com/watch?v=_CEyRrYzPVk&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=23&pp=iAQB
https://www.youtube.com/watch?v=YaaGvq2mcR0&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=25
https://www.youtube.com/watch?v=NQflFk5WAO0&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=26
https://www.youtube.com/watch?v=sfRcl5rmydI&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=27
https://www.youtube.com/watch?v=q9Nuy7mFIsE&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=28
https://www.youtube.com/watch?v=E76kpPjxMcw&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=29
https://www.youtube.com/watch?v=x2BQyZorSQc&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=30
https://www.youtube.com/watch?v=a9hwXoCrEUs&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=31
https://www.youtube.com/watch?v=rih9-80p0Ec&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=32
https://www.youtube.com/watch?v=1g-wANyGiR0&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=33
https://www.youtube.com/watch?v=CFNvij1f0Nk&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=34
https://www.youtube.com/watch?v=r8p44wQMtNE&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=35
https://www.youtube.com/watch?v=-HxKHUEwnug&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=36
https://www.youtube.com/watch?v=9kUromZCP9M&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=37
https://www.youtube.com/watch?v=XLhMi6YEhsQ&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=38
https://www.youtube.com/watch?v=AazxDk1ODVo&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=39
https://www.youtube.com/watch?v=8L6M2mRcux4&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=40
https://www.youtube.com/watch?v=_nGePkIW3Ac&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=41
https://www.youtube.com/watch?v=4yEkCSyTTxg&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=42
https://www.youtube.com/watch?v=n84o7Ggw2Fw&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=43
https://www.youtube.com/watch?v=XWqh9F4pjHg&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=44
https://www.youtube.com/watch?v=tgqAt6YN1jE&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=45
https://www.youtube.com/watch?v=xxZLBncNRXs&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=46
https://www.youtube.com/watch?v=IQz7innxnms&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=47
https://www.youtube.com/watch?v=-1p0mvfzPS8&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=48
https://www.youtube.com/watch?v=4XP_m5-ULnA&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=49
https://www.youtube.com/watch?v=vZBRMcUkqNA&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=50
https://www.youtube.com/watch?v=DJuxIDyHZCw&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=51
https://www.youtube.com/watch?v=-ZRwlYtAMps&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=52
https://www.youtube.com/watch?v=EieUJvKrjo4&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=53
https://www.youtube.com/watch?v=purPO8UE-9g&list=PLaEJLf99gDO7KwKQuq4mGRJJYrwHqRyIM&index=54
  "$d_web"
  "$d_web"
  "$d_web"
  "$d_web"
  "$d_web"
  "$d_web"
/home/bill/web/webWork/pMenuTopMenu.html#neural
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/home/bill/web/webWork/pMenuTopStatus.html#neural
/home/bill/web/webWork/pMenuTopCopyright.html#neural
/home/bill/web/webWork/pMenuTopHelp.html#neural
#Introduction
#Detailed [description, specification]
#what is currently ignored by callerID-SNNs?
#mRNA program code causes neurons to fire?
#random thoughts
#Is there any biological plausibility?
#Future objectives
#tie-in with Grossberg's 2021 'Conscious Mind, Resonant Brain'
#fractal [dendrite, axon]s
#Links to my related work
/home/bill/web/Neural nets/MindCode/MindCode/MindCode.HtmWeb.html
/home/bill/web/Neural nets/MindCode/16 Introduction - Conceptual pseudo-basis for MindCode.odt
/home/bill/web/Neural nets/MindCode/MindCode/MindCode.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/CompLangs/Fractals/0_Fractal notes.txt
/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
/home/bill/web/Neural nets/MindCode/Howell 060716 Genetic specification of recurrent neural networks, Initial thoughts, WCCI 2006 paper 1341.pdf
https://en.wikipedia.org/wiki/Juyang_Weng
/home/bill/web/webWork/pMenuTopMenu.html#neural
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/home/bill/web/webWork/pMenuTopStatus.html#neural
/home/bill/web/webWork/pMenuTopCopyright.html#neural
/home/bill/web/webWork/pMenuTopHelp.html#neural
#Introduction
#Detailed [description, specification]
#what is currently ignored by callerID-SNNs?
#mRNA program code causes neurons to fire?
#random thoughts
#Is there any biological plausibility?
#Future objectives
#tie-in with Grossberg's 2021 'Conscious Mind, Resonant Brain'
#fractal [dendrite, axon]s
#Links to my related work
/home/bill/web/Neural nets/MindCode/MindCode/MindCode.HtmWeb.html
/home/bill/web/Neural nets/MindCode/16 Introduction - Conceptual pseudo-basis for MindCode.odt
/home/bill/web/Neural nets/MindCode/MindCode/MindCode.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/CompLangs/Fractals/0_Fractal notes.txt
/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
/home/bill/web/Neural nets/MindCode/Howell 060716 Genetic specification of recurrent neural networks, Initial thoughts, WCCI 2006 paper 1341.pdf
https://en.wikipedia.org/wiki/Juyang_Weng
/home/bill/web/Neural nets/callerID-SNNs/images/Asl, Valizadeh, Tass 2017 Fig 1 Possible synaptic modifications of two interconnected neurons fire nearly inphase in the presence of dendritic and axonal propagation delays.png
/home/bill/web/Neural nets/callerID-SNNs/images/Asl, Valizadeh, Tass 2017 Fig 1 Possible synaptic modifications of two interconnected neurons fire nearly inphase in the presence of dendritic and axonal propagation delays.png
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/home/bill/web/Neural nets/Howell 2006 - Presentation, Genetic Specification of Recurrent Neural Networks Initial Thoughts.ppt
/home/bill/web/Neural nets/Howell 2006 - Genetic specification of neural networks, draft concepts and implications.pdf
/home/bill/web/Neural nets/Howell 2005 - Presentation, Junk DNA and Neural Networks conjecture on directions and implications.ppt
2005 IJCNN website - official/
2006 WCCI website - official/
2007 IJCNN Orlando website/
Holidays - neural networks and genomics.html
Howell 2005 - Presentation, Junk DNA and Neural Networks conjecture on directions and implications.ppt
Howell 2006 - Genetic specification of neural networks, draft concepts and implications.pdf
Howell 2006 - Presentation, Genetic Specification of Recurrent Neural Networks Initial Thoughts.ppt
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/home/bill/web/Neural nets/Howell 2006 - Presentation, Genetic Specification of Recurrent Neural Networks Initial Thoughts.ppt
/home/bill/web/Neural nets/Howell 2006 - Genetic specification of neural networks, draft concepts and implications.pdf
/home/bill/web/Neural nets/Howell 2005 - Presentation, Junk DNA and Neural Networks conjecture on directions and implications.ppt
2005 IJCNN website - official/
2006 WCCI website - official/
2007 IJCNN Orlando website/
Holidays - neural networks and genomics.html
Howell 2005 - Presentation, Junk DNA and Neural Networks conjecture on directions and implications.ppt
Howell 2006 - Genetic specification of neural networks, draft concepts and implications.pdf
Howell 2006 - Presentation, Genetic Specification of Recurrent Neural Networks Initial Thoughts.ppt
/home/bill/web/webWork/pMenuTopMenu.html#neural
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/home/bill/web/webWork/pMenuTopStatus.html#neural
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/home/bill/web/Neural nets/Howell 2006 - Presentation, Genetic Specification of Recurrent Neural Networks Initial Thoughts.ppt
/home/bill/web/Neural nets/Howell 2006 - Genetic specification of neural networks, draft concepts and implications.pdf
/home/bill/web/Neural nets/Howell 2005 - Presentation, Junk DNA and Neural Networks conjecture on directions and implications.ppt
2005 IJCNN website - official/
2006 WCCI website - official/
2007 IJCNN Orlando website/
Holidays - neural networks and genomics.html
Howell 2005 - Presentation, Junk DNA and Neural Networks conjecture on directions and implications.ppt
Howell 2006 - Genetic specification of neural networks, draft concepts and implications.pdf
Howell 2006 - Presentation, Genetic Specification of Recurrent Neural Networks Initial Thoughts.ppt
/home/bill/web/webWork/pMenuTopMenu.html#neural
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/home/bill/web/webWork/pMenuTopStatus.html#neural
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/home/bill/web/webWork/pMenuTopHelp.html#neural
/home/bill/web/Neural nets/Howell 2006 - Presentation, Genetic Specification of Recurrent Neural Networks Initial Thoughts.ppt
/home/bill/web/Neural nets/Howell 2006 - Genetic specification of neural networks, draft concepts and implications.pdf
/home/bill/web/Neural nets/Howell 2005 - Presentation, Junk DNA and Neural Networks conjecture on directions and implications.ppt
2005 IJCNN website - official/
2006 WCCI website - official/
2007 IJCNN Orlando website/
Holidays - neural networks and genomics.html
Howell 2005 - Presentation, Junk DNA and Neural Networks conjecture on directions and implications.ppt
Howell 2006 - Genetic specification of neural networks, draft concepts and implications.pdf
Howell 2006 - Presentation, Genetic Specification of Recurrent Neural Networks Initial Thoughts.ppt
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/home/bill/web/Neural nets/webWork/pMenuTopHelp.html#consciousness
https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
#Introduction
#Definitions of consciousness
#Sentience
#Definitions of sentience
#Philosophy and sentience
#Alleged sentience of artificial intelligence
#'Multiple Conflicting Hypothesis' for consciousness: 
#Stephen Grossberg 2021 Conscious Mind, Resonant Brain
#John Taylor 2006 The Mind: A users manual
#Brain regions, neural networks, resonance
#Llinas 1998 Recurrent thalamo-cortical resonance
#Min 2010 Thalamic reticular networking
#Modern [philosophical, logical] models of consciousness
#Dehaene–Changeux 1986 global neuronal workspace model
#Bernard Baars 1988 global workspace model
#Crick-Koch 1990 Towards a neurobiological theory of consciousness
#Neural correlates of consciousness
#Giulio Tononi 2004 Integrated information theory
#Multiple drafts
#Functionalism
#Undefined neuron structures, Universal Function Approximators, Magic science
#Electromagnetic theories of consciousness
#Quantum consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Selected_models_of_consciousness:
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html#Questions: Grossberg's c-ART, Transformer NNs, and consciousness?
https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
https://en.wikipedia.org/wiki/Sentience#sentience
https://en.wikipedia.org/wiki/Sentience#Philosophy_and_sentience
https://en.wikipedia.org/wiki/Sentience#Digital_sentience
/home/bill/web/Neural nets/Grossberg/why is cART unknown.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Consciousness
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#ART - Adaptive Resonance Theory
/home/bill/web/Neural nets/Grossberg/references- Grossberg.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#[bio, neuro, psycho]logy data
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#ART - Adaptive Resonance Theory
https://en.wikipedia.org/wiki/Recurrent_thalamo-cortical_resonance
https://tbiomed.biomedcentral.com/track/pdf/10.1186/1742-4682-7-10
https://en.wikipedia.org/wiki/Models_of_consciousness#Thalamic_reticular_networking_model_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness#Dehaene–Changeux model
https://en.wikipedia.org/wiki/Global_workspace_theory
https://en.wikipedia.org/wiki/Models_of_consciousness#Neural_correlates_of_consciousness
https://en.wikipedia.org/wiki/Consciousness#Neural_correlates
/home/bill/web/Neural nets/Grossberg/Grossbergs quoted text.HtmWeb.html
https://iep.utm.edu/integrated-information-theory-of-consciousness/
https://en.wikipedia.org/wiki/Integrated information theory
https://en.wikipedia.org/wiki/Integrated_information_theory#Criticism
https://en.wikipedia.org/wiki/Models_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness#Sociology
https://en.wikipedia.org/wiki/Models_of_consciousness#Multiple_drafts_model
https://en.wikipedia.org/wiki/Multiple_drafts_model
https://en.wikipedia.org/wiki/Models_of_consciousness#Functionalism
https://en.wikipedia.org/wiki/Models_of_consciousness#Electromagnetic_theories_of_consciousness
https://en.wikipedia.org/wiki/Electromagnetic_theories_of_consciousness#Objections
https://en.wikipedia.org/wiki/Models_of_consciousness#Orchestrated_objective_reduction
https://rationalwiki.org/wiki/Quantum_consciousness
/home/bill/web/Neural nets/Grossberg/webWork/bash script: thematic, list of txt files to search.txt
https://en.wikipedia.org/wiki/Neural_correlates_of_consciousness
https://en.wikipedia.org/wiki/Sociology_of_human_consciousness
https://en.wikipedia.org/wiki/Multiple_drafts_model
https://en.wikipedia.org/wiki/Functionalism_(philosophy_of_mind)
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https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
#Introduction
#Definitions of consciousness
#Sentience
#Definitions of sentience
#Philosophy and sentience
#Alleged sentience of artificial intelligence
#'Multiple Conflicting Hypothesis' for consciousness: 
#Stephen Grossberg 2021 Conscious Mind, Resonant Brain
#John Taylor 2006 The Mind: A users manual
#Brain regions, neural networks, resonance
#Llinas 1998 Recurrent thalamo-cortical resonance
#Min 2010 Thalamic reticular networking
#Modern [philosophical, logical] models of consciousness
#Dehaene–Changeux 1986 global neuronal workspace model
#Bernard Baars 1988 global workspace model
#Crick-Koch 1990 Towards a neurobiological theory of consciousness
#Neural correlates of consciousness
#Giulio Tononi 2004 Integrated information theory
#Multiple drafts
#Functionalism
#Undefined neuron structures, Universal Function Approximators, Magic science
#Electromagnetic theories of consciousness
#Quantum consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Selected_models_of_consciousness:
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html#Questions: Grossberg's c-ART, Transformer NNs, and consciousness?
https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
https://en.wikipedia.org/wiki/Sentience#sentience
https://en.wikipedia.org/wiki/Sentience#Philosophy_and_sentience
https://en.wikipedia.org/wiki/Sentience#Digital_sentience
/home/bill/web/Neural nets/Grossberg/why is cART unknown.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Consciousness
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/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#[bio, neuro, psycho]logy data
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#ART - Adaptive Resonance Theory
https://en.wikipedia.org/wiki/Recurrent_thalamo-cortical_resonance
https://tbiomed.biomedcentral.com/track/pdf/10.1186/1742-4682-7-10
https://en.wikipedia.org/wiki/Models_of_consciousness#Thalamic_reticular_networking_model_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness#Dehaene–Changeux model
https://en.wikipedia.org/wiki/Global_workspace_theory
https://en.wikipedia.org/wiki/Models_of_consciousness#Neural_correlates_of_consciousness
https://en.wikipedia.org/wiki/Consciousness#Neural_correlates
/home/bill/web/Neural nets/Grossberg/Grossbergs quoted text.HtmWeb.html
https://iep.utm.edu/integrated-information-theory-of-consciousness/
https://en.wikipedia.org/wiki/Integrated information theory
https://en.wikipedia.org/wiki/Integrated_information_theory#Criticism
https://en.wikipedia.org/wiki/Models_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness#Sociology
https://en.wikipedia.org/wiki/Models_of_consciousness#Multiple_drafts_model
https://en.wikipedia.org/wiki/Multiple_drafts_model
https://en.wikipedia.org/wiki/Models_of_consciousness#Functionalism
https://en.wikipedia.org/wiki/Models_of_consciousness#Electromagnetic_theories_of_consciousness
https://en.wikipedia.org/wiki/Electromagnetic_theories_of_consciousness#Objections
https://en.wikipedia.org/wiki/Models_of_consciousness#Orchestrated_objective_reduction
https://rationalwiki.org/wiki/Quantum_consciousness
/home/bill/web/Neural nets/Grossberg/webWork/bash script: thematic, list of txt files to search.txt
https://en.wikipedia.org/wiki/Neural_correlates_of_consciousness
https://en.wikipedia.org/wiki/Sociology_of_human_consciousness
https://en.wikipedia.org/wiki/Multiple_drafts_model
https://en.wikipedia.org/wiki/Functionalism_(philosophy_of_mind)
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https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
#Introduction
#Definitions of consciousness
#Sentience
#Definitions of sentience
#Philosophy and sentience
#Alleged sentience of artificial intelligence
#'Multiple Conflicting Hypothesis' for consciousness: 
#Stephen Grossberg 2021 Conscious Mind, Resonant Brain
#John Taylor 2006 The Mind: A users manual
#Brain regions, neural networks, resonance
#Llinas 1998 Recurrent thalamo-cortical resonance
#Min 2010 Thalamic reticular networking
#Modern [philosophical, logical] models of consciousness
#Dehaene–Changeux 1986 global neuronal workspace model
#Bernard Baars 1988 global workspace model
#Crick-Koch 1990 Towards a neurobiological theory of consciousness
#Neural correlates of consciousness
#Giulio Tononi 2004 Integrated information theory
#Multiple drafts
#Functionalism
#Undefined neuron structures, Universal Function Approximators, Magic science
#Electromagnetic theories of consciousness
#Quantum consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Selected_models_of_consciousness:
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html#Questions: Grossberg's c-ART, Transformer NNs, and consciousness?
https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
https://en.wikipedia.org/wiki/Sentience#sentience
https://en.wikipedia.org/wiki/Sentience#Philosophy_and_sentience
https://en.wikipedia.org/wiki/Sentience#Digital_sentience
/home/bill/web/Neural nets/Grossberg/why is cART unknown.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Consciousness
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/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#[bio, neuro, psycho]logy data
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/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#ART - Adaptive Resonance Theory
https://en.wikipedia.org/wiki/Recurrent_thalamo-cortical_resonance
https://tbiomed.biomedcentral.com/track/pdf/10.1186/1742-4682-7-10
https://en.wikipedia.org/wiki/Models_of_consciousness#Thalamic_reticular_networking_model_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness#Dehaene–Changeux model
https://en.wikipedia.org/wiki/Global_workspace_theory
https://en.wikipedia.org/wiki/Models_of_consciousness#Neural_correlates_of_consciousness
https://en.wikipedia.org/wiki/Consciousness#Neural_correlates
/home/bill/web/Neural nets/Grossberg/Grossbergs quoted text.HtmWeb.html
https://iep.utm.edu/integrated-information-theory-of-consciousness/
https://en.wikipedia.org/wiki/Integrated information theory
https://en.wikipedia.org/wiki/Integrated_information_theory#Criticism
https://en.wikipedia.org/wiki/Models_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness#Sociology
https://en.wikipedia.org/wiki/Models_of_consciousness#Multiple_drafts_model
https://en.wikipedia.org/wiki/Multiple_drafts_model
https://en.wikipedia.org/wiki/Models_of_consciousness#Functionalism
https://en.wikipedia.org/wiki/Models_of_consciousness#Electromagnetic_theories_of_consciousness
https://en.wikipedia.org/wiki/Electromagnetic_theories_of_consciousness#Objections
https://en.wikipedia.org/wiki/Models_of_consciousness#Orchestrated_objective_reduction
https://rationalwiki.org/wiki/Quantum_consciousness
/home/bill/web/Neural nets/Grossberg/webWork/bash script: thematic, list of txt files to search.txt
https://en.wikipedia.org/wiki/Neural_correlates_of_consciousness
https://en.wikipedia.org/wiki/Sociology_of_human_consciousness
https://en.wikipedia.org/wiki/Multiple_drafts_model
https://en.wikipedia.org/wiki/Functionalism_(philosophy_of_mind)
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https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
#Introduction
#Definitions of consciousness
#Sentience
#Definitions of sentience
#Philosophy and sentience
#Alleged sentience of artificial intelligence
#'Multiple Conflicting Hypothesis' for consciousness: 
#Stephen Grossberg 2021 Conscious Mind, Resonant Brain
#John Taylor 2006 The Mind: A users manual
#Brain regions, neural networks, resonance
#Llinas 1998 Recurrent thalamo-cortical resonance
#Min 2010 Thalamic reticular networking
#Modern [philosophical, logical] models of consciousness
#Dehaene–Changeux 1986 global neuronal workspace model
#Bernard Baars 1988 global workspace model
#Crick-Koch 1990 Towards a neurobiological theory of consciousness
#Neural correlates of consciousness
#Giulio Tononi 2004 Integrated information theory
#Multiple drafts
#Functionalism
#Undefined neuron structures, Universal Function Approximators, Magic science
#Electromagnetic theories of consciousness
#Quantum consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Selected_models_of_consciousness:
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html#Questions: Grossberg's c-ART, Transformer NNs, and consciousness?
https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
https://en.wikipedia.org/wiki/Consciousness#The_problem_of_definition
https://en.wikipedia.org/wiki/Sentience#sentience
https://en.wikipedia.org/wiki/Sentience#Philosophy_and_sentience
https://en.wikipedia.org/wiki/Sentience#Digital_sentience
/home/bill/web/Neural nets/Grossberg/why is cART unknown.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Consciousness
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/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#[bio, neuro, psycho]logy data
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#ART - Adaptive Resonance Theory
https://en.wikipedia.org/wiki/Recurrent_thalamo-cortical_resonance
https://tbiomed.biomedcentral.com/track/pdf/10.1186/1742-4682-7-10
https://en.wikipedia.org/wiki/Models_of_consciousness#Thalamic_reticular_networking_model_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness#Dehaene–Changeux model
https://en.wikipedia.org/wiki/Global_workspace_theory
https://en.wikipedia.org/wiki/Models_of_consciousness#Neural_correlates_of_consciousness
https://en.wikipedia.org/wiki/Consciousness#Neural_correlates
/home/bill/web/Neural nets/Grossberg/Grossbergs quoted text.HtmWeb.html
https://iep.utm.edu/integrated-information-theory-of-consciousness/
https://en.wikipedia.org/wiki/Integrated information theory
https://en.wikipedia.org/wiki/Integrated_information_theory#Criticism
https://en.wikipedia.org/wiki/Models_of_consciousness
https://en.wikipedia.org/wiki/Models_of_consciousness#Sociology
https://en.wikipedia.org/wiki/Models_of_consciousness#Multiple_drafts_model
https://en.wikipedia.org/wiki/Multiple_drafts_model
https://en.wikipedia.org/wiki/Models_of_consciousness#Functionalism
https://en.wikipedia.org/wiki/Models_of_consciousness#Electromagnetic_theories_of_consciousness
https://en.wikipedia.org/wiki/Electromagnetic_theories_of_consciousness#Objections
https://en.wikipedia.org/wiki/Models_of_consciousness#Orchestrated_objective_reduction
https://rationalwiki.org/wiki/Quantum_consciousness
/home/bill/web/Neural nets/Grossberg/webWork/bash script: thematic, list of txt files to search.txt
https://en.wikipedia.org/wiki/Neural_correlates_of_consciousness
https://en.wikipedia.org/wiki/Sociology_of_human_consciousness
https://en.wikipedia.org/wiki/Multiple_drafts_model
https://en.wikipedia.org/wiki/Functionalism_(philosophy_of_mind)
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#???
#???
https://direct.mit.edu/neco/issue
/home/bill/web/Neural nets/consciousness/opinions- Blake Lemoine, others.HtmWeb.html#Blake Lemoine: Is LaMDA Sentient?
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#???
#???
https://direct.mit.edu/neco/issue
/home/bill/web/Neural nets/consciousness/opinions- Blake Lemoine, others.HtmWeb.html#Blake Lemoine: Is LaMDA Sentient?
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#???
#???
https://direct.mit.edu/neco/issue
/home/bill/web/Neural nets/consciousness/opinions- Blake Lemoine, others.HtmWeb.html#Blake Lemoine: Is LaMDA Sentient?
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#???
#???
https://direct.mit.edu/neco/issue
/home/bill/web/Neural nets/consciousness/opinions- Blake Lemoine, others.HtmWeb.html#Blake Lemoine: Is LaMDA Sentient?
/home/bill/web/webWork/pMenuTopMenu.html#consciousness
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/home/bill/web/webWork/pMenuTopHelp.html#consciousness
#Questions: Grossberg's c-ART, Transformer NNs, and consciousness?
#Grossberg: why ART is relevant to consciousness in Transformer NNs
#A workable [definition, context, model] for consciousness
#non-[Grossberg, TrNN] topics
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#The underlying basis in [bio, psycho]logical data
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html#Consciousness: table of comparisons
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/consciousness/machine consciousness, the need.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Taylors consciousness.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Taylors consciousness.HtmWeb.html
/home/bill/web/Neural nets/consciousness/opinions- Blake Lemoine, others.HtmWeb.html
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html
/home/bill/web/Neural nets/Grossberg/references- Grossberg.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/references- non-Grossberg.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#[bio, neuro, psycho]logy data
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#Questions: Grossberg's c-ART, Transformer NNs, and consciousness?
#Grossberg: why ART is relevant to consciousness in Transformer NNs
#A workable [definition, context, model] for consciousness
#non-[Grossberg, TrNN] topics
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#The underlying basis in [bio, psycho]logical data
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html#Consciousness: table of comparisons
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/consciousness/machine consciousness, the need.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Taylors consciousness.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Taylors consciousness.HtmWeb.html
/home/bill/web/Neural nets/consciousness/opinions- Blake Lemoine, others.HtmWeb.html
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html
/home/bill/web/Neural nets/Grossberg/references- Grossberg.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/references- non-Grossberg.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#[bio, neuro, psycho]logy data
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#Introduction
#Grossberg 2021: cellular evolution and top-down-bottom-up mechanisms
#Howell 2011: the need for machine consciousness
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Social media/Howell 111230 - Social graphs, social sets, and social media.doc
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#Introduction
#Grossberg 2021: cellular evolution and top-down-bottom-up mechanisms
#Howell 2011: the need for machine consciousness
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Social media/Howell 111230 - Social graphs, social sets, and social media.doc
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#Introduction
#Grossberg 2021: cellular evolution and top-down-bottom-up mechanisms
#Howell 2011: the need for machine consciousness
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Social media/Howell 111230 - Social graphs, social sets, and social media.doc
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#Introduction
#Grossberg 2021: cellular evolution and top-down-bottom-up mechanisms
#Howell 2011: the need for machine consciousness
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Social media/Howell 111230 - Social graphs, social sets, and social media.doc
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https://cajundiscordian.medium.com
#Introduction
#Blake Lemoine: Is LaMDA Sentient?
#Is LaMDA Sentient? — an Interview
#We’re All Different and That’s Okay
#What is LaMDA and What Does it Want?
#What is sentience and why does it matter?
#Terry Sejnowski: Is machine intelligence debatable?
#Gary Marcus: Current LLMs do NOT possess 'Artitifial General Intelligence' (AGI)
#Do these comments have anything to do with consciousness?
#Tae Kim: AI Chatbots Keep Making Up Facts. Nvidia Has an Answer
https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917
https://cajundiscordian.medium.com/were-all-different-and-that-s-okay-79096fafa1cd
https://cajundiscordian.medium.com/what-is-lamda-and-what-does-it-want-688632134489
https://cajundiscordian.medium.com/what-is-sentience-and-why-does-it-mater-2c28f4882cb9
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.HtmWeb.html
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https://cajundiscordian.medium.com
#Introduction
#Blake Lemoine: Is LaMDA Sentient?
#Is LaMDA Sentient? — an Interview
#We’re All Different and That’s Okay
#What is LaMDA and What Does it Want?
#What is sentience and why does it matter?
#Terry Sejnowski: Is machine intelligence debatable?
#Gary Marcus: Current LLMs do NOT possess 'Artitifial General Intelligence' (AGI)
#Do these comments have anything to do with consciousness?
#Tae Kim: AI Chatbots Keep Making Up Facts. Nvidia Has an Answer
https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917
https://cajundiscordian.medium.com/were-all-different-and-that-s-okay-79096fafa1cd
https://cajundiscordian.medium.com/what-is-lamda-and-what-does-it-want-688632134489
https://cajundiscordian.medium.com/what-is-sentience-and-why-does-it-mater-2c28f4882cb9
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.HtmWeb.html
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/home/bill/web/webWork/pMenuTopStatus.html#consciousness
/home/bill/web/webWork/pMenuTopCopyright.html#consciousness
/home/bill/web/webWork/pMenuTopHelp.html#consciousness
https://cajundiscordian.medium.com
#Introduction
#Blake Lemoine: Is LaMDA Sentient?
#Is LaMDA Sentient? — an Interview
#We’re All Different and That’s Okay
#What is LaMDA and What Does it Want?
#What is sentience and why does it matter?
#Terry Sejnowski: Is machine intelligence debatable?
#Gary Marcus: Current LLMs do NOT possess 'Artitifial General Intelligence' (AGI)
#Do these comments have anything to do with consciousness?
#Tae Kim: AI Chatbots Keep Making Up Facts. Nvidia Has an Answer
https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917
https://cajundiscordian.medium.com/were-all-different-and-that-s-okay-79096fafa1cd
https://cajundiscordian.medium.com/what-is-lamda-and-what-does-it-want-688632134489
https://cajundiscordian.medium.com/what-is-sentience-and-why-does-it-mater-2c28f4882cb9
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#consciousness
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/home/bill/web/webWork/pMenuTopStatus.html#consciousness
/home/bill/web/webWork/pMenuTopCopyright.html#consciousness
/home/bill/web/webWork/pMenuTopHelp.html#consciousness
https://cajundiscordian.medium.com
#Introduction
#Blake Lemoine: Is LaMDA Sentient?
#Is LaMDA Sentient? — an Interview
#We’re All Different and That’s Okay
#What is LaMDA and What Does it Want?
#What is sentience and why does it matter?
#Terry Sejnowski: Is machine intelligence debatable?
#Gary Marcus: Current LLMs do NOT possess 'Artitifial General Intelligence' (AGI)
#Do these comments have anything to do with consciousness?
#Tae Kim: AI Chatbots Keep Making Up Facts. Nvidia Has an Answer
https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917
https://cajundiscordian.medium.com/were-all-different-and-that-s-okay-79096fafa1cd
https://cajundiscordian.medium.com/what-is-lamda-and-what-does-it-want-688632134489
https://cajundiscordian.medium.com/what-is-sentience-and-why-does-it-mater-2c28f4882cb9
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.HtmWeb.html
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#Proceeding Table of Contents
#Eccles keynote, Pribram post-conference viewpoint
#Section I. The dendritic microprocess
#Section II. Quantum neurodynamics
#Section III. Nanoneurology
#Section IV. Perceptual Processing
#Afterword, list of authors
#Quotes
#Historical thinking about quantum [neurophysiology, consciousness]
#Howells questions about 1993 conference proceedings
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Historical thinking about consciousness
/home/bill/web/Neural nets/consciousness/Quantum consciousness.HtmWeb.html#Historical thinking about quantum [neurophysiology, consciousness]
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#Proceeding Table of Contents
#Eccles keynote, Pribram post-conference viewpoint
#Section I. The dendritic microprocess
#Section II. Quantum neurodynamics
#Section III. Nanoneurology
#Section IV. Perceptual Processing
#Afterword, list of authors
#Quotes
#Historical thinking about quantum [neurophysiology, consciousness]
#Howells questions about 1993 conference proceedings
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Historical thinking about consciousness
/home/bill/web/Neural nets/consciousness/Quantum consciousness.HtmWeb.html#Historical thinking about quantum [neurophysiology, consciousness]
/home/bill/web/Neural nets/consciousness/Pribram quantum consciousness cover 6pct.png
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#Introduction: what does quantum physics add to our understanding to consciousness?
#Historical thinking about quantum [neurophysiology, consciousness]
#Quantum [concept, approach, context]s
#Quantum concepts
#Quantum approaches to applications
#Quantum contexts for consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Historical thinking about consciousness
/home/bill/web/Neural nets/consciousness/Pribram 1993 quantum fields and consciousness proceedings.HtmWeb.html#Historical thinking about quantum [neurophysiology, consciousness]
/home/bill/web/Neural nets/consciousness/Pribram 1993 quantum fields and consciousness proceedings.HtmWeb.html
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#Introduction: what does quantum physics add to our understanding to consciousness?
#Historical thinking about quantum [neurophysiology, consciousness]
#Quantum [concept, approach, context]s
#Quantum concepts
#Quantum approaches to applications
#Quantum contexts for consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Historical thinking about consciousness
/home/bill/web/Neural nets/consciousness/Pribram 1993 quantum fields and consciousness proceedings.HtmWeb.html#Historical thinking about quantum [neurophysiology, consciousness]
/home/bill/web/Neural nets/consciousness/Pribram 1993 quantum fields and consciousness proceedings.HtmWeb.html
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#Introduction: what does quantum physics add to our understanding to consciousness?
#Historical thinking about quantum [neurophysiology, consciousness]
#Quantum [concept, approach, context]s
#Quantum concepts
#Quantum approaches to applications
#Quantum contexts for consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Historical thinking about consciousness
/home/bill/web/Neural nets/consciousness/Pribram 1993 quantum fields and consciousness proceedings.HtmWeb.html#Historical thinking about quantum [neurophysiology, consciousness]
/home/bill/web/Neural nets/consciousness/Pribram 1993 quantum fields and consciousness proceedings.HtmWeb.html
/home/bill/web/Neural nets/consciousness/Pribram 1993 quantum fields and consciousness proceedings.HtmWeb.html#Howells questions about 1993 conference proceedings
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#Introduction: what does quantum physics add to our understanding to consciousness?
#Historical thinking about quantum [neurophysiology, consciousness]
#Quantum [concept, approach, context]s
#Quantum concepts
#Quantum approaches to applications
#Quantum contexts for consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Historical thinking about consciousness
/home/bill/web/Neural nets/consciousness/Pribram 1993 quantum fields and consciousness proceedings.HtmWeb.html#Historical thinking about quantum [neurophysiology, consciousness]
/home/bill/web/Neural nets/consciousness/Pribram 1993 quantum fields and consciousness proceedings.HtmWeb.html
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link
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/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#ART - Adaptive Resonance Theory
/home/bill/web/Neural nets/Grossberg/references- Grossberg.HtmWeb.html
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/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#ART - Adaptive Resonance Theory
/home/bill/web/Neural nets/Grossberg/why is cART unknown.HtmWeb.html
missing link
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
https://arxiv.org/abs/1706.03762
missing link
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Antonio Damasio 1999 Body and Emotion in the making of consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Llinas 1998 Recurrent thalamo-cortical resonance
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Thalamic reticular networking
https://tbiomed.biomedcentral.com/track/pdf/10.1186/1742-4682-7-10
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Thalamic reticular networking
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Crick-Koch model
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Bernard Baars 1988 global workspace model
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Dehaene–Changeux model
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Stanislas Dehaene 2014 neural global workspace model
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/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Giulio Tononi 2004 Integrated information theory
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Multiple drafts
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/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Electromagnetic theories of consciousness
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https://arxiv.org/abs/1706.03762
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/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Thalamic reticular networking
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Crick-Koch model
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Bernard Baars 1988 global workspace model
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/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Stanislas Dehaene 2014 neural global workspace model
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https://arxiv.org/abs/1706.03762
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/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Antonio Damasio 1999 Body and Emotion in the making of consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Llinas 1998 Recurrent thalamo-cortical resonance
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https://tbiomed.biomedcentral.com/track/pdf/10.1186/1742-4682-7-10
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/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Crick-Koch model
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/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Stanislas Dehaene 2014 neural global workspace model
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/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Functionalism
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#Electromagnetic theories of consciousness
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html#quantum processes in neuron microtubules
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#ART - Adaptive Resonance Theory
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#ART - Adaptive Resonance Theory
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#Introduction
#principles, architecture, function, process
#Principles, Principia
#equations of the [brain, mind]
#modules & modal architectures ([micro, macro]-circuits)
#CogEM Cognitive-Emotional-Motor model
#[intra, inter]-cellular process
#informational noise suppression
#[stable, robust, adaptive] learning
#on-center off-surround
#top-down bottom-up
#cooperative-competitive
#complementary computing
#laminar computing
#computing with cellular patterns
#family of ART.....base, linguistic
#ART - Adaptive Resonance Theory
#LAMINART vison, speech, cognition
#[, n]START learning & memory consolidation
#ARTMAP associate learned categories across ART networks
#[, c]ARTWORD word perception cycle
#LIST PARSE [linguistic, spatial, motor] working memory
#family of ART ....visual, auditory
#[, d, p]ARTSCAN attentional shroud, binocular rivalry
#ARTSCENE classification of scenic properties
#SMART synchronous matching ART, mismatch triggering
#ARTPHONE [gain control, working] memory
#ARTSTREAM auditory streaming, SPINET sound spectra
#consciousness
#What is consciousness?
#conscious vs non-conscious
#Grossberg: other consciousness theories
#random fun themes
#art (painting etc)
#biology, evolution, paleontology
#brain disorders and disease
#hippocampus IS a cognitive map!
#auditory continuity illusion
#see-reach to hear-speak
#neurotransmitter
#learning and development
#Why are there hexagonal grid cell receptive fields?
#Strange themes
#AI, machine intelligence, etc
#Brain is NOT Bayesian?
#brain rythms & Schuman resonances
#Explainable AI
#logic vs connectionist
#Successful [datamodelling, applications] showing effectiveness of cART etc
#[bio, neuro, psycho]logy data
#[software, engineering, other] applications
/home/bill/web/webWork/pMenuTopHelp.html#Navigation: [menu, link, directory]s
/home/bill/web/webWork/pMenuTopHelp.html#Theme webPage generation by bash script
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/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.05 Effects of intertrial and intratrial intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.10 STM Shunting Model, mass action in membrane equations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.11 MTM habituative transmitter gate; LTM gated steepest descent learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p074fig02.23 Shunting on-center off-surround network: no saturation! infinite dynamical range, conserve total activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p079fig02.32 Substrate of resonance: match (in phase) of BU and TD input patterns amplifies matched pattern due to automatic gain control by shunting terms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.17 Geometry of choice and learning, trains the closest LTM vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.26 Transmitter y [accumulation, release]:  y restored < infinite rate, evolution has exploited this.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.33 Novelty reset- rebound to arousal onset: equilibrate to [I, J]; keep phasic input J fixed; interpret this equation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p580fig16.05 GridPlaceMap hierarchy of SOMs with identical equations: learns 2D [grid, place] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.16 Grid-to-place SOM: formation of place cell fields via grid-to-place cell learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.25 Spectral Spacing Model equations for [STM, MTM, LTM].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.44 main [boundary, surface] formation stages: LGN-> V1-> V2-> V4.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p346fig09.16 macrocircuit of 13 brain regions used to move the eyes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p474fig12.70 macrocircuit model to explain lexical decision task data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p481fig13.01 Cognitive-Emotional-Motor (CogEM): macrocircuit of [function, anatomy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p580fig16.05 GridPlaceMap hierarchy of SOMs with identical equations: learns 2D [grid, place] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p599fig16.35 anatomically overlapping grid cell modules: effects of [different modules in one animal, DV location, response rate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p612fig16.42 Macrocircuit of the main SOVEREIGN subsystems: visual, motor.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.16 Cognitive-Emotional-Motor (CogEM) model: sensory cortex, amygdala, PFC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p481fig13.01 Cognitive-Emotional-Motor (CogEM): macrocircuit of [function, anatomy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.03 CogEM: supported by anatomical connections [[sensory, orbitofrontal] cortices, amygdala].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p487fig13.11 CogEM : three main properties to explain how attentional blocking occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p489fig13.13 CogEM and conditioning: positive ISI; inverted-U vs ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.19 CogEM: secondary conditioning of [arousal, response], multiple [drive, input]s, motivational sets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p514fig13.44 Analog of the COgEM model maps of [object X, proto-self], assembly of second-order map.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.33 How do noise suppression signals arise: symmetry-breaking during morphogenesis, opposites attract rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p012fig01.08 Sigmoidal signal: a hybrid of [same, slower, faster]-than-linear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.30 Noise suppression: attenuate zero spatial frequency patterns- no information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.31 Noise suppression -> pattern matching: mismatch (out of phase) suppressed, match (in phase) amplifies pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.33 How do noise suppression signals arise: symmetry-breaking during morphogenesis, opposites attract rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.34 Symmetry-breaking: dynamics and anatomy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.36 [Noise suppression, contour detection]: uniform patterns are suppressed, contrasts are selectively enhanced, contours are detected.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.39 Learn net dipole output pattern: [shunting competition, informational noise suppression] in affective gated dipoles, back-propagation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p009fig01.06 Primacy gradient of activity in a recurrent shunting OC-OS network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p024fig01.15 READ circuit: REcurrent Associative Dipole.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.22 Computing with patterns: how to compute the pattern-sensitive variable.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p074fig02.23 Shunting on-center off-surround network: no saturation! infinite dynamical range, conserve total activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.25 Weber law, adaptation, and shift property, convert to logarithmic coordinates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.26 Mudpuppy retina neurophysiology, adaptation- sensitivity shifts for different backgrounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.27 Mechanism: cooperative-competitive dynamics, subtractive lateral inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.34 Symmetry-breaking: dynamics and anatomy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.35 Ratio contrast detector: reflectance processing, contrast normalization, discount illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.36 [Noise suppression, contour detection]: uniform patterns are suppressed, contrasts are selectively enhanced, contours are detected.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p106fig03.24 Do these ideas work on hard problems: Synthetic Aperture Radar [discount illuminant, filling-in, boundaries].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p176fig04.53 OC-OS [within position, across depth]: brighter Kanizsas look closer.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.05 LGN [ON, OFF] cells respond differently to [side, end]s of lines.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.12 Single flash: Gaussian receptive fields, recurrent OC-OS winner-take-all.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.07 Log polar remapping of optic flow:  [expansion, circular] motion maps to single direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p345fig09.15 MT double opponent directional fields: relative motions [objects, backgrounds].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.06 Why another layer 6-to-4 signal: on-center off-surround.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p362fig10.11 Final grouping: folded feedback, strongest enhanced on-center, weaker suppressed off-surround, interlaminar functional columns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.14 Explanation: grouping and attention share the same modulatory decision circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.15 Attention protects target from masking stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p448fig12.46 Masking Field working memory is a multiple-scale self-similar recurrent shunting on-center off-surround network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p564fig15.35 GO signal to recurrent shunting OC-OS networks: control of the [fore, hind] limbs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p567fig15.38 Gated Pacemaker of [diurnal, nocturnal] circadian rythms: whether phasic light turns the pacemaker on or off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.16 Grid-to-place SOM: formation of place cell fields via grid-to-place cell learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p627tbl17.01 Homologs between [reaction-diffusion, recurrent shunting cellular network] models of development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.11 BU-TD mismatch -> orienting system -> nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p052fig02.02 Adaptive Resonance reactivation: features bottom-up; categories top-down.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.31 Noise suppression -> pattern matching: mismatch (out of phase) suppressed, match (in phase) amplifies pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p079fig02.32 Substrate of resonance: match (in phase) of BU and TD input patterns amplifies matched pattern due to automatic gain control by shunting terms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p091fig03.04 blind spot and veins can occlude light to the retina.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.08 LGN [activation, inhibition], with[, out] top-down feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p199fig05.11 Instar learning: bottom-up adaptive filter for feature patterns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.20 [PN match, N200 mismatch] computationally complementary potentials.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.24 Learning top-down expectations occurs during bottom-up learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.25 Error correction: [learn, compare] TD-BU inputs, Processing Negativity ERP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.26 Mismatch triggers nonspecific arousal, N200 ERP from orienting system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p220fig05.29 Vigilance [excitation: search better match, inhibition: resonance & learning].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.30 predictive error -> vigilance increase just enough -> minimax learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p252fig06.01 surface-shroud resonance forms as objects bid for spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.07 Top-down attentional spotlight becomes a shroud.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.04 Illusory contours persist longer than real contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.05 Illusory contours inhibited by OFF cell rebounds, propagate to center.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.52 Correct motion directions after the peak shift top-down expectation acts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.54 Motion directions of a single dot moving slowly along a cycloid curve through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.05 Activation of V1 by direct excitatory signals from LGN to layer 4 of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.06 Why another layer 6-to-4 signal: on-center off-surround.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.14 Explanation: grouping and attention share the same modulatory decision circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p441fig12.38 LTM Invariance Principle: new events do not change the relative activities of past event sequences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p449fig12.47 Masking Field self-similar [recurrent inhibitory, top-down excitatory] signals to the item chunk working memory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.50 ARTWORD perception cycle: sequences-> chunks-> compete-> top-down expectations-> item working memory->  develops item-list resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.44 SOVEREIGN[target position, difference] vectors, volitional GO computations] to control decision-making and action.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p009fig01.06 Primacy gradient of activity in a recurrent shunting OC-OS network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.27 Mechanism: cooperative-competitive dynamics, subtractive lateral inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.15 orientations: [transient before, equilibrium after] choice.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p146fig04.25 How are end cuts created: two stages of short-range competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p148fig04.26 End cut during neon color spreading via 2 stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p149fig04.27 Bipole cells boundary completion: long cooperation & short inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p161fig04.37 Boundary Contour System model: analog-sensitive boundary completion Kanizsas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p198fig05.10 Competitive learning and Self-Organized Maps (SOMs).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p205fig05.18 catastrophic forgetting due to [competition, associative] learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.07 Persistence increases with distance, due to weaker spatial competition in hypercomplex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.33 Working memory models: item and order, or competitive queuing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p488fig13.12 Motivational feedback and blocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.39 Learn net dipole output pattern: [shunting competition, informational noise suppression] in affective gated dipoles, back-propagation.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 1 - Overview
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 8 - How we see and recognize object motion
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 9 - Target tracking, navigation, and decision-making
/home/bill/web/Neural nets/Grossberg/images- captioned/p029tbl01.01 complementary streams [visual boundary, what-where, perception & recognition, object tracking, motor target].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030tbl01.02 complementary streams: What- [rapid, stable] learn invariant object categories, Where- [labile spatial, action] actions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p094fig03.07 Complementary properties of boundaries and surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p176fig04.53 OC-OS [within position, across depth]: brighter Kanizsas look closer.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.20 [PN match, N200 mismatch] computationally complementary potentials.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p267fig06.14 Perceptual consistency and figure-ground separation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p314fig08.34 VISTARS navigation model: FORMOTION front end for navigational circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 10 - Laminar computing by cerebral cortex
#Chapter 10 - Laminar computing by cerebral cortex
/home/bill/web/Neural nets/Grossberg/images- captioned/p030fig01.20 neo-cortex 6 layers: same canonical laminar design cart [vision, speech, cognition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.19 Laminar cortical circuit for complex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p182fig04.58 LAMINART explains many percepts of transparency.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p362fig10.11 Final grouping: folded feedback, strongest enhanced on-center, weaker suppressed off-surround, interlaminar functional columns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p363fig10.12 V2 repeats V1 circuitry at larger spatial scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p378fig11.12 3D LAMINART: V2 monocular+binocular line of sight inputs -> depth perception.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p395fig11.34 3D LAMINART vs 7 other rivalry models: stable vision and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.41 3D LAMINART: [angle, disparity-gradient] cells learn 3D representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.44 3D LAMINART disparities [5, 6, 8, 10, 11, 14]: images of objects in common depth planes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p254fig06.03 ARTSCAN Search model learns to recognize and name invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p316fig08.36 Motion capture by directional grouping feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p473fig12.69 ART Matching Rule properties explain error rate and mean reaction time (RT) data from lexical decision experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p182fig04.58 LAMINART explains many percepts of transparency.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p378fig11.12 3D LAMINART: V2 monocular+binocular line of sight inputs -> depth perception.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p395fig11.34 3D LAMINART vs 7 other rivalry models: stable vision and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.41 3D LAMINART: [angle, disparity-gradient] cells learn 3D representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.44 3D LAMINART disparities [5, 6, 8, 10, 11, 14]: images of objects in common depth planes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.33 Some early ARTMAP benchmark studies (no image - link instead).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.34 ARTMAP learned maps of natural terrains better than AI expert systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p242fig05.46 Distributed ARTMAP variants learn the 5-4 category structure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.49 ARTWORD: acoustic cues, phonetic [features, WM], Masking Field unitized lists, gain control.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.50 ARTWORD perception cycle: sequences-> chunks-> compete-> top-down expectations-> item working memory->  develops item-list resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p455fig12.52 cARTWORD dynamics 1-2-3: resonant activity in item and feature layers corresponds to conscious speech percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.53 cARTWORD dynamics 1-silence-3: Gap in resonant activity of 1-silence-3 in [item, feature] layers corresponds to perceived silence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.54 cARTWORD dynamics: 1-noise-3: Resonance of 1-2-3 in [item, feature] layers  restores item 2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p457fig12.55 cARTWORD dynamics 1-noise-5: Figures 12.[54, 55] future context can disambiguate past noisy sequences that are otherwise identical.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p447fig12.45 LIST PARSE: order errors vs serial position with extended pauses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p254fig06.03 ARTSCAN Search model learns to recognize and name invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p255fig06.04 The ARTSCAN Search for a desired target object in a scene: Wheres Waldo.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p259fig06.08 dARTSCN spatial attention hierarchy [Fast Where, Slow What] stream.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p271fig06.17 Persistent activity in IT to [view, position, size]-invariant category learning by positional ARTSCAN.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.18 pARTSCAN: positionally-invariant object learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p273fig06.20 pARTSCAN simulation of Li & DiCarlo IT cell swapping data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.21 pARTSCAN [position invariance, selectivity] trade-off of Zoccolan etal 2007.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.22 pARTSCAN: IT cortex processes image morphs with high vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.06 ARTSCENE: classification of scenic properties as texture categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.07 ARTSCENE voting achieves even better prediction of scene type.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p533fig14.09 ARTSCENE search [data, simulation]s for 6 pairs of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.26 Mismatch triggers nonspecific arousal, N200 ERP from orienting system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p468fig12.65 [PHONET, ARTPHONE] linguistic properties: creates rate-invariant representations for variable-rate speech, paradoxical VC-CV category boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p422fig12.19 ARTSTREAM: derive streams from [pitch, source direction].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.21 Pitch shifts with component shifts, pitch vs lowest harmonic number.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p431fig12.27 ARTSTREAM & NormNet strip maps: variants of occular dominance columns in visual cortex.png
#Conscious mind, resonant brain: Table of Contents
#Conscious mind, resonant brain: sub-section list
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 1 - Overview
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 15 - Adaptively timed learning
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/images- captioned/p039tbl01.03 [consciousness, movement] links: visual, auditory, emotional.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p042tbl01.04 six main resonances which support different kinds of conscious awareness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p270fig06.16 Predictive remapping of eye movements, from V3A to LIP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.27 knowing vs seeing resonances: What [knowing, feature-prototype], Where [seeing, surface-shroud].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p355fig10.02 seeing vs knowing distinction is difficult because they interact so strongly.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p369fig10.19 Unconscious learning of motion direction, without [extra-foveal attention, awareness] of stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p396fig11.35 Three properties of bipole boundary grouping in V2: boundaries oscillate with rivalry-inducing stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p455fig12.52 cARTWORD dynamics 1-2-3: resonant activity in item and feature layers corresponds to conscious speech percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
#Grossberg's comments for some well-known consciousness theories
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p514fig13.44 Analog of the COgEM model maps of [object X, proto-self], assembly of second-order map.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.23 pointillist painting: Georges Seurat, A Sunday on la Grande Jatte.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.25 Matisse, The Roofs of Collioure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.27 Matisse: Open Window, Collioure, [continuously, sparsely] indiced surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.32 Claude Monet painting: Poppies Near Argenteuil.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.43 Four paintings by Monet of the Rouen cathedral under different lighting conditions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.44 Rouen Cathedral at sunset (Monet 1892-1894): equiluminant, obscured and less depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.46 Rouen Cathedral full sunlight (Monet 1892-1894): T-junctions greater depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.49 DaVinci stereopsis and occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p377fig11.11 DaVinci stereopsis: monocular information and depth percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p381fig11.15 DaVinci stereopsis variant of (Gillam, Blackburn, Nakayama 1999): same mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p382fig11.16 DaVinci stereopsis of [3 narrow, one thick] rectangles: same explanation.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 17 - A universal development code
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.19 Computing in a bounded activity domain, Gedanken experiment.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.26 Transmitter y [accumulation, release]:  y restored < infinite rate, evolution has exploited this.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.26 Brain [region, process]s that contribute to autistic behavioral symptoms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p189fig05.04 hippocampus & several brain regions [learn, remember] throughout life.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p233fig05.42 Evidence for the [gamma, beta] prediction in 3 parts of the brain.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.06 Circuit between [dentate granule, CA1 hippocampal pyramid] cells seems to compute spectrally timed responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.19 Conditioning, Attention, and Timing circuit: Hippocampus spectrum-> Amgdala orienting system-> neocortex motivational attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.26 Brain [region, process]s that contribute to autistic behavioral symptoms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p573fig16.01 Hippocampal place cells: discovery from rat [experimental chamber, neurophysiological recordings].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p575fig16.03 Rat navigation: firing patterns of [hippocampal place, entrorhinal grid] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p578fig16.04 Cross-sections of the hippocampal regions and the inputs to them.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.10 GRIDSmap embedded into hierarchy of SOMs: [angular head velocity, linear velocity] signals to place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p602fig16.37 Hippocampal inactivation by muscimol disrupts grid cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p603fig16.38 Role of hippocampal feedback in maintaining grid fields, muscimol inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p617fig16.50 Spectral Spacing models of [perirhinal what, parahippocampal where] inputs, fused in the hippocampus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 12 - From seeing and reaching to hearing and speaking
#Chapter 12 - From seeing and reaching to hearing and speaking
/home/bill/web/Neural nets/Grossberg/images- captioned/p030tbl01.02 complementary streams: What- [rapid, stable] learn invariant object categories, Where- [labile spatial, action] actions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p032fig01.21 [Retina, LGNs, V[1,2,3,4], MT] to What & Where areas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p039tbl01.03 [consciousness, movement] links: visual, auditory, emotional.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.20 G-wave theorem 2: [speed-up, scale] independent of [distance, scale size].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.21 Equal half-time property: multiple scales generate motion percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.61 RT task behavioural data: reach time (ms) vs % coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.22 negative Gaussian of an obstacle: avoid obstacle without losing sight of goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.25 M+ computes global motion estimate from noisy local motion estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p414fig12.11 [data, simulation]s from cortical areas 4 and 5 during a reach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.13 DIRECT model: Endogenous Random Generator learns volitional reaches.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.14 DIRECT reaches [unconstrained, with TOOL, elbow@140°, blind].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p417fig12.15 From Seeing & Reaching (DIRECT) to Hearing & Speaking (DIVA): homologous circular reactions, [tool use, coarticulation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p428fig12.25 ARTSPEECH: auditory-articulatory feedback loop & imitative map, [auditory, motor] dimensionally consistent, motor theory of speech.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p430fig12.26 NormNet: speaker normalization via specializations of mechanisms for auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p461fig12.58 lisTELOS shows [BG nigro-[thalamic, collicular], FEF, ITa, PFC, PNR-THAL, PPC, SEF, SC, V1, V4-ITp, Visual Cortex input] and [GABA].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p524fig14.04 Basal ganglia circuit for dopaminergic Now Print signals from the substantia nigra pars compacta in response to unexpected rewards.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.27 Spectrally timed SNc learning: brain [region, process]s release of dopaminergic signals, unexpected reinforcing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.29 Excitatory pathways that support activation of the SNc by a US and the conditioning of a CS to the US.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.30 Inhibitory pathway: striosomal cells predict [timing, magnitude] of reward signal to cancel it.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.32 Inhibitory pathway expectation magnitude: is a negative feedback control system for learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p569fig15.40 GO and STOP movement signals: control by [direct, indirect] basal ganglia circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.06 Binocular fusion by obligate cells in V1-3B when =[left,right] contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.09 V2 disparity filter solves correspondence problem: false matches suppressed by line-of-sight inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p581fig16.06 Trigonometry of spatial navigation: coactivation of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.09 GRIDSmap: stripe cells for rat trajectories, self-organizing map learned hexagonal grid cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.11 GRIDSmap learning of hexagonal grid fields, multiple phases per scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.13 Hexagonal grid cell receptive fields: somewhat insensitive to [number, directional selectivities] of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.14 GRIDSmap: Superimposed firing of stripe cells supports learning hexagonal grid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.15 Why is a hexagonal grid favored: stripe cells at intervals of 45 degrees, GRIDSmap does not learn, oscillatory interference does.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p587fig16.17 A refined analysis: SOM amplifies most frequent and energetic coactivations, stripe fields separated by [90°, 60°].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p588fig16.18 GridPlaceMap hierarchy of SOMs: coordinated learning of [grid, place, inomodal] cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 10 - Laminar computing by cerebral cortex
/home/bill/web/Neural nets/Grossberg/images- captioned/p011fig01.07 signal function determines how initial activity pattern is transformed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p233fig05.42 Evidence for the [gamma, beta] prediction in 3 parts of the brain.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.07 continuous motion illusions: [Beta with, Phi without] percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.08 Delta motion when [luminance, contrast] of flash 2 is larger than flash 1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.07 Log polar remapping of optic flow:  [expansion, circular] motion maps to single direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.34 spiking GridPlaceMap: generates theta-modulated place and grid cell firing, unlike the rate-based model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 17 - A universal development code
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.16 Cognitive-Emotional-Motor (CogEM) model: sensory cortex, amygdala, PFC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p058fig02.04 Learning a global arrow in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.05 Effects of intertrial and intratrial intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.06 Bow due to backward effect in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.16 Noise-Saturation Dilemma:sensitivity to ratios of inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.19 Computing in a bounded activity domain, Gedanken experiment.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p085fig02.38 Modelling method and cycle, technological applications: at each stage [behavioural data, design principles, neural data, math model and analysis].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p134fig04.14 Experiments on filling-in: in-the-act; simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.23 A perceptual disaster in the feature contour system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.29 bipole cells in cortical area V2: first neurophysiological evidence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.30 anatomy: horizontal connections in V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p152fig04.31 Bipoles through the ages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.36 Graffiti art by Banksy: amodal boundary completion; spatial impenetrability.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p162fig04.38 Cooperation and competition during grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p165fig04.41 Kanizsa-Minguzzi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p252fig06.01 surface-shroud resonance forms as objects bid for spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p257fig06.05 Spatial attention flows along object boundaries: Macaque V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.06 Neurophysiological data & simulation: attention can flow along a curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p265fig06.13 basal ganglia gate perceptual, cognitive, emotional, etc through parallel loops.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p267fig06.14 Perceptual consistency and figure-ground separation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p271fig06.17 Persistent activity in IT to [view, position, size]-invariant category learning by positional ARTSCAN.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p273fig06.20 pARTSCAN simulation of Li & DiCarlo IT cell swapping data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.21 pARTSCAN [position invariance, selectivity] trade-off of Zoccolan etal 2007.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.23 IT responses to image morphs, data vs model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p284fig07.02 Persistence decreases with flash illuminance & duration [data, simulations].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p285fig07.03 Persistence decrease: rebound to input offset inhibits bipole cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.04 Illusory contours persist longer than real contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.05 Illusory contours inhibited by OFF cell rebounds, propagate to center.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.06 Persistence: [less, more] as adaptation orientation [same, orthogonal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.07 Persistence increases with distance, due to weaker spatial competition in hypercomplex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.08 Delta motion when [luminance, contrast] of flash 2 is larger than flash 1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.09 motion in opposite directions perceived when 2 later flashes on either side of 1st flash.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.10 motion speed-up perceived when flash duration decreases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.22 Korte Laws: ISIs in the hundreds of milliseconds can cause apparent motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.30 3D FORMOTION model: feature tracking [get directional, inhibit inconsistent] signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p319fig08.38 Solving the aperture problem takes time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p333fig08.58 LIP responses during RT task correct trials: coherence and [activation, inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.59 LIP responses for FD task: predictiveness decreases with increasing coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.60 [RT, FD] task behavioral data: more coherence in the motion causes more accurate decisions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.61 RT task behavioural data: reach time (ms) vs % coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.62 LIP encodes not only where, but also when, to move the eyes - No Bayes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.11 importance of efference copy in real movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.13 How do our brains figure out which views belong to which pear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.20 Steering from optic flow: goals are attractors, obstacles are repellers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p352fig09.26 heading direction final stage: beautiful optic flow, accuracy matches humans.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.09 Grouping starts in layer 2-3: long-range horizontal excitation, short-range inhibition of target pyramidal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p361fig10.10 Bipole property controls perceptual grouping: inputs [excitatory sum, inhibitory normalize].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.15 Attention protects target from masking stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.16 Flankers can enhance or suppress targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.17 Attention has greater effect on low contrast targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.18 Texture reduces response to a bar: [iso-orientation, perpendicular] suppression.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p369fig10.19 Unconscious learning of motion direction, without [extra-foveal attention, awareness] of stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.31 LIGHTSHAFT model: determining depth-from-texture percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.39 Simulation of the eye rivalry data of (Lee, Blake 1999).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.43 A pair of disparate images of a scene from the University of Tsukuba.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p407fig12.03 Motor cortical cells: vectors for [direction, length] of commanded movement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p409fig12.04 VITE simulations: difference vector emergent from network interactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.06 Monkeys transform movement: 2 -> 10 o'clock target, 50 or 100 msec after activation of 2 o'clock target.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p414fig12.11 [data, simulation]s from cortical areas 4 and 5 during a reach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.21 Pitch shifts with component shifts, pitch vs lowest harmonic number.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p428fig12.25 ARTSPEECH: auditory-articulatory feedback loop & imitative map, [auditory, motor] dimensionally consistent, motor theory of speech.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p432fig12.28 SpaN: spatial representations of numerical quantities in the parietal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.32 Free recall bowed serial position curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.33 Working memory models: item and order, or competitive queuing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p443fig12.41 Neurophysiology of sequential copying: [primacy gradient, self-inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p447fig12.45 LIST PARSE: order errors vs serial position with extended pauses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p452fig12.48 Perceptual integration of acoustic cues: [silence vs noise] durations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.49 ARTWORD: acoustic cues, phonetic [features, WM], Masking Field unitized lists, gain control.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p465fig12.63 lisTELOS and data: microstimulation biases selection so saccade trajectories converge toward a single location in space.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p468fig12.65 [PHONET, ARTPHONE] linguistic properties: creates rate-invariant representations for variable-rate speech, paradoxical VC-CV category boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.66 PHONET: relative duration of [consonant, vowel] pairs can [preserve, change] a percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p473fig12.69 ART Matching Rule properties explain error rate and mean reaction time (RT) data from lexical decision experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p474fig12.70 macrocircuit model to explain lexical decision task data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p476fig12.71 Word frequency data model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.06 Classical conditioning: inverted-U vs InterStimulus Interval (ISI).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.31 Behavioral contrast rebounds: decrease [food-> negative Frustration, shock-> positive Relief] reinforcers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p533fig14.09 ARTSCENE search [data, simulation]s for 6 pairs of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p542fig15.04 blinks of [nictitating membrane, eyelid] are adaptively timed: closure occurs at arrival of the US following the CS, obeys Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.05 Learning with two ISIs: each peak obeys Weber Law, strong evidence for spectral learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p556fig15.24 Cerebellar cortex responses: [data, model] short latency responses after lesioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.28 Neurophysiological data and simulations of SNc responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p573fig16.01 Hippocampal place cells: discovery from rat [experimental chamber, neurophysiological recordings].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p574fig16.02 Neurophysiological recordings of 18 different place cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p575fig16.03 Rat navigation: firing patterns of [hippocampal place, entrorhinal grid] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.08 Evidence for stripe-like cells: entorhinal cortex data, Band Cells position from grid cell oscillatory interference.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p589fig16.19 How does the spatial scale increase along the MEC dorsoventral axis.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.26 Gradient of grid spacing along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p594fig16.27 Gradient of field width along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p595fig16.28 Peak and mean rates at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.29 Subthreshold membrane mV oscillations: decreasing Hz at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.30 Spatial phases of learned grid and place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.31 Multimodal place cell firing in large spaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.32 Model fits data about grid cell development in juvenile rats:  grid [score increases, spacing flat].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.33 Model fits [place, grid, directional] cell data about grid cell development in juvenile rats:  [spatial information, inter-trial stability] vs postnatal day.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p599fig16.35 anatomically overlapping grid cell modules: effects of [different modules in one animal, DV location, response rate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p602fig16.37 Hippocampal inactivation by muscimol disrupts grid cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p603fig16.38 Role of hippocampal feedback in maintaining grid fields, muscimol inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p607fig16.40 Effects of medial septum (MS) inactivation on grid cells: data, simulations, gridness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p085fig02.38 Modelling method and cycle, technological applications: at each stage [behavioural data, design principles, neural data, math model and analysis].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.33 Some early ARTMAP benchmark studies (no image - link instead).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.33 MOTIVATOR: thalamocortical loops through basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.34 Distinct basal ganglia zones for each loop.png
images- Grossberg 2021/p057fig02.03 neuron basic [anatomy, physiology].png
images- Grossberg 2021/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
images- Grossberg 2021/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
images- Grossberg 2021/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
images- Grossberg 2021/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#Early ARTMAP benchmark studies
images- Grossberg 2021/p107fig03.26 drawing directly in color leads to colored surface representations.png
images- Grossberg 2021/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#Early ARTMAP benchmark studies
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
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#Introduction
#principles, architecture, function, process
#Principles, Principia
#equations of the [brain, mind]
#modules & modal architectures ([micro, macro]-circuits)
#CogEM Cognitive-Emotional-Motor model
#[intra, inter]-cellular process
#informational noise suppression
#[stable, robust, adaptive] learning
#on-center off-surround
#top-down bottom-up
#cooperative-competitive
#complementary computing
#laminar computing
#computing with cellular patterns
#family of ART.....base, linguistic
#ART - Adaptive Resonance Theory
#LAMINART vison, speech, cognition
#[, n]START learning & memory consolidation
#ARTMAP associate learned categories across ART networks
#[, c]ARTWORD word perception cycle
#LIST PARSE [linguistic, spatial, motor] working memory
#family of ART ....visual, auditory
#[, d, p]ARTSCAN attentional shroud, binocular rivalry
#ARTSCENE classification of scenic properties
#SMART synchronous matching ART, mismatch triggering
#ARTPHONE [gain control, working] memory
#ARTSTREAM auditory streaming, SPINET sound spectra
#consciousness
#What is consciousness?
#conscious vs non-conscious
#Grossberg: other consciousness theories
#random fun themes
#art (painting etc)
#biology, evolution, paleontology
#brain disorders and disease
#hippocampus IS a cognitive map!
#auditory continuity illusion
#see-reach to hear-speak
#neurotransmitter
#learning and development
#Why are there hexagonal grid cell receptive fields?
#Strange themes
#AI, machine intelligence, etc
#Brain is NOT Bayesian?
#brain rythms & Schuman resonances
#Explainable AI
#logic vs connectionist
#Successful [datamodelling, applications] showing effectiveness of cART etc
#[bio, neuro, psycho]logy data
#[software, engineering, other] applications
/home/bill/web/webWork/pMenuTopHelp.html#Navigation: [menu, link, directory]s
/home/bill/web/webWork/pMenuTopHelp.html#Theme webPage generation by bash script
/home/bill/web/webWork/pMenuTopHelp.html#Notation for [chapter, section, figure, table, index, note]s
/home/bill/web/webWork/pMenuTopHelp.html#incorporate reader questions into theme webPages
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.05 Effects of intertrial and intratrial intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.10 STM Shunting Model, mass action in membrane equations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.11 MTM habituative transmitter gate; LTM gated steepest descent learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p074fig02.23 Shunting on-center off-surround network: no saturation! infinite dynamical range, conserve total activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p079fig02.32 Substrate of resonance: match (in phase) of BU and TD input patterns amplifies matched pattern due to automatic gain control by shunting terms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.17 Geometry of choice and learning, trains the closest LTM vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.26 Transmitter y [accumulation, release]:  y restored < infinite rate, evolution has exploited this.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.33 Novelty reset- rebound to arousal onset: equilibrate to [I, J]; keep phasic input J fixed; interpret this equation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p580fig16.05 GridPlaceMap hierarchy of SOMs with identical equations: learns 2D [grid, place] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.16 Grid-to-place SOM: formation of place cell fields via grid-to-place cell learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.25 Spectral Spacing Model equations for [STM, MTM, LTM].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.44 main [boundary, surface] formation stages: LGN-> V1-> V2-> V4.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p346fig09.16 macrocircuit of 13 brain regions used to move the eyes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p474fig12.70 macrocircuit model to explain lexical decision task data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p481fig13.01 Cognitive-Emotional-Motor (CogEM): macrocircuit of [function, anatomy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p580fig16.05 GridPlaceMap hierarchy of SOMs with identical equations: learns 2D [grid, place] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p599fig16.35 anatomically overlapping grid cell modules: effects of [different modules in one animal, DV location, response rate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p612fig16.42 Macrocircuit of the main SOVEREIGN subsystems: visual, motor.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.16 Cognitive-Emotional-Motor (CogEM) model: sensory cortex, amygdala, PFC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p481fig13.01 Cognitive-Emotional-Motor (CogEM): macrocircuit of [function, anatomy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.03 CogEM: supported by anatomical connections [[sensory, orbitofrontal] cortices, amygdala].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p487fig13.11 CogEM : three main properties to explain how attentional blocking occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p489fig13.13 CogEM and conditioning: positive ISI; inverted-U vs ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.19 CogEM: secondary conditioning of [arousal, response], multiple [drive, input]s, motivational sets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p514fig13.44 Analog of the COgEM model maps of [object X, proto-self], assembly of second-order map.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.33 How do noise suppression signals arise: symmetry-breaking during morphogenesis, opposites attract rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p012fig01.08 Sigmoidal signal: a hybrid of [same, slower, faster]-than-linear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.30 Noise suppression: attenuate zero spatial frequency patterns- no information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.31 Noise suppression -> pattern matching: mismatch (out of phase) suppressed, match (in phase) amplifies pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.33 How do noise suppression signals arise: symmetry-breaking during morphogenesis, opposites attract rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.34 Symmetry-breaking: dynamics and anatomy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.36 [Noise suppression, contour detection]: uniform patterns are suppressed, contrasts are selectively enhanced, contours are detected.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.39 Learn net dipole output pattern: [shunting competition, informational noise suppression] in affective gated dipoles, back-propagation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p009fig01.06 Primacy gradient of activity in a recurrent shunting OC-OS network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p024fig01.15 READ circuit: REcurrent Associative Dipole.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.22 Computing with patterns: how to compute the pattern-sensitive variable.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p074fig02.23 Shunting on-center off-surround network: no saturation! infinite dynamical range, conserve total activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.25 Weber law, adaptation, and shift property, convert to logarithmic coordinates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.26 Mudpuppy retina neurophysiology, adaptation- sensitivity shifts for different backgrounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.27 Mechanism: cooperative-competitive dynamics, subtractive lateral inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.34 Symmetry-breaking: dynamics and anatomy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.35 Ratio contrast detector: reflectance processing, contrast normalization, discount illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.36 [Noise suppression, contour detection]: uniform patterns are suppressed, contrasts are selectively enhanced, contours are detected.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p106fig03.24 Do these ideas work on hard problems: Synthetic Aperture Radar [discount illuminant, filling-in, boundaries].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p176fig04.53 OC-OS [within position, across depth]: brighter Kanizsas look closer.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.05 LGN [ON, OFF] cells respond differently to [side, end]s of lines.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.12 Single flash: Gaussian receptive fields, recurrent OC-OS winner-take-all.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.07 Log polar remapping of optic flow:  [expansion, circular] motion maps to single direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p345fig09.15 MT double opponent directional fields: relative motions [objects, backgrounds].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.06 Why another layer 6-to-4 signal: on-center off-surround.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p362fig10.11 Final grouping: folded feedback, strongest enhanced on-center, weaker suppressed off-surround, interlaminar functional columns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.14 Explanation: grouping and attention share the same modulatory decision circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.15 Attention protects target from masking stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p448fig12.46 Masking Field working memory is a multiple-scale self-similar recurrent shunting on-center off-surround network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p564fig15.35 GO signal to recurrent shunting OC-OS networks: control of the [fore, hind] limbs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p567fig15.38 Gated Pacemaker of [diurnal, nocturnal] circadian rythms: whether phasic light turns the pacemaker on or off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.16 Grid-to-place SOM: formation of place cell fields via grid-to-place cell learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p627tbl17.01 Homologs between [reaction-diffusion, recurrent shunting cellular network] models of development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.11 BU-TD mismatch -> orienting system -> nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p052fig02.02 Adaptive Resonance reactivation: features bottom-up; categories top-down.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.31 Noise suppression -> pattern matching: mismatch (out of phase) suppressed, match (in phase) amplifies pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p079fig02.32 Substrate of resonance: match (in phase) of BU and TD input patterns amplifies matched pattern due to automatic gain control by shunting terms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p091fig03.04 blind spot and veins can occlude light to the retina.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.08 LGN [activation, inhibition], with[, out] top-down feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p199fig05.11 Instar learning: bottom-up adaptive filter for feature patterns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.20 [PN match, N200 mismatch] computationally complementary potentials.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.24 Learning top-down expectations occurs during bottom-up learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.25 Error correction: [learn, compare] TD-BU inputs, Processing Negativity ERP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.26 Mismatch triggers nonspecific arousal, N200 ERP from orienting system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p220fig05.29 Vigilance [excitation: search better match, inhibition: resonance & learning].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.30 predictive error -> vigilance increase just enough -> minimax learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p252fig06.01 surface-shroud resonance forms as objects bid for spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.07 Top-down attentional spotlight becomes a shroud.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.04 Illusory contours persist longer than real contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.05 Illusory contours inhibited by OFF cell rebounds, propagate to center.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.52 Correct motion directions after the peak shift top-down expectation acts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.54 Motion directions of a single dot moving slowly along a cycloid curve through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.05 Activation of V1 by direct excitatory signals from LGN to layer 4 of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.06 Why another layer 6-to-4 signal: on-center off-surround.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.14 Explanation: grouping and attention share the same modulatory decision circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p441fig12.38 LTM Invariance Principle: new events do not change the relative activities of past event sequences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p449fig12.47 Masking Field self-similar [recurrent inhibitory, top-down excitatory] signals to the item chunk working memory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.50 ARTWORD perception cycle: sequences-> chunks-> compete-> top-down expectations-> item working memory->  develops item-list resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.44 SOVEREIGN[target position, difference] vectors, volitional GO computations] to control decision-making and action.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p009fig01.06 Primacy gradient of activity in a recurrent shunting OC-OS network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.27 Mechanism: cooperative-competitive dynamics, subtractive lateral inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.15 orientations: [transient before, equilibrium after] choice.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p146fig04.25 How are end cuts created: two stages of short-range competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p148fig04.26 End cut during neon color spreading via 2 stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p149fig04.27 Bipole cells boundary completion: long cooperation & short inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p161fig04.37 Boundary Contour System model: analog-sensitive boundary completion Kanizsas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p198fig05.10 Competitive learning and Self-Organized Maps (SOMs).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p205fig05.18 catastrophic forgetting due to [competition, associative] learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.07 Persistence increases with distance, due to weaker spatial competition in hypercomplex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.33 Working memory models: item and order, or competitive queuing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p488fig13.12 Motivational feedback and blocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.39 Learn net dipole output pattern: [shunting competition, informational noise suppression] in affective gated dipoles, back-propagation.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 1 - Overview
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 8 - How we see and recognize object motion
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 9 - Target tracking, navigation, and decision-making
/home/bill/web/Neural nets/Grossberg/images- captioned/p029tbl01.01 complementary streams [visual boundary, what-where, perception & recognition, object tracking, motor target].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030tbl01.02 complementary streams: What- [rapid, stable] learn invariant object categories, Where- [labile spatial, action] actions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p094fig03.07 Complementary properties of boundaries and surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p176fig04.53 OC-OS [within position, across depth]: brighter Kanizsas look closer.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.20 [PN match, N200 mismatch] computationally complementary potentials.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p267fig06.14 Perceptual consistency and figure-ground separation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p314fig08.34 VISTARS navigation model: FORMOTION front end for navigational circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 10 - Laminar computing by cerebral cortex
#Chapter 10 - Laminar computing by cerebral cortex
/home/bill/web/Neural nets/Grossberg/images- captioned/p030fig01.20 neo-cortex 6 layers: same canonical laminar design cart [vision, speech, cognition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.19 Laminar cortical circuit for complex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p182fig04.58 LAMINART explains many percepts of transparency.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p362fig10.11 Final grouping: folded feedback, strongest enhanced on-center, weaker suppressed off-surround, interlaminar functional columns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p363fig10.12 V2 repeats V1 circuitry at larger spatial scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p378fig11.12 3D LAMINART: V2 monocular+binocular line of sight inputs -> depth perception.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p395fig11.34 3D LAMINART vs 7 other rivalry models: stable vision and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.41 3D LAMINART: [angle, disparity-gradient] cells learn 3D representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.44 3D LAMINART disparities [5, 6, 8, 10, 11, 14]: images of objects in common depth planes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p254fig06.03 ARTSCAN Search model learns to recognize and name invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p316fig08.36 Motion capture by directional grouping feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p473fig12.69 ART Matching Rule properties explain error rate and mean reaction time (RT) data from lexical decision experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p182fig04.58 LAMINART explains many percepts of transparency.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p378fig11.12 3D LAMINART: V2 monocular+binocular line of sight inputs -> depth perception.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p395fig11.34 3D LAMINART vs 7 other rivalry models: stable vision and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.41 3D LAMINART: [angle, disparity-gradient] cells learn 3D representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.44 3D LAMINART disparities [5, 6, 8, 10, 11, 14]: images of objects in common depth planes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.33 Some early ARTMAP benchmark studies (no image - link instead).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.34 ARTMAP learned maps of natural terrains better than AI expert systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p242fig05.46 Distributed ARTMAP variants learn the 5-4 category structure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.49 ARTWORD: acoustic cues, phonetic [features, WM], Masking Field unitized lists, gain control.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.50 ARTWORD perception cycle: sequences-> chunks-> compete-> top-down expectations-> item working memory->  develops item-list resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p455fig12.52 cARTWORD dynamics 1-2-3: resonant activity in item and feature layers corresponds to conscious speech percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.53 cARTWORD dynamics 1-silence-3: Gap in resonant activity of 1-silence-3 in [item, feature] layers corresponds to perceived silence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.54 cARTWORD dynamics: 1-noise-3: Resonance of 1-2-3 in [item, feature] layers  restores item 2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p457fig12.55 cARTWORD dynamics 1-noise-5: Figures 12.[54, 55] future context can disambiguate past noisy sequences that are otherwise identical.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p447fig12.45 LIST PARSE: order errors vs serial position with extended pauses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p254fig06.03 ARTSCAN Search model learns to recognize and name invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p255fig06.04 The ARTSCAN Search for a desired target object in a scene: Wheres Waldo.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p259fig06.08 dARTSCN spatial attention hierarchy [Fast Where, Slow What] stream.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p271fig06.17 Persistent activity in IT to [view, position, size]-invariant category learning by positional ARTSCAN.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.18 pARTSCAN: positionally-invariant object learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p273fig06.20 pARTSCAN simulation of Li & DiCarlo IT cell swapping data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.21 pARTSCAN [position invariance, selectivity] trade-off of Zoccolan etal 2007.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.22 pARTSCAN: IT cortex processes image morphs with high vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.06 ARTSCENE: classification of scenic properties as texture categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.07 ARTSCENE voting achieves even better prediction of scene type.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p533fig14.09 ARTSCENE search [data, simulation]s for 6 pairs of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.26 Mismatch triggers nonspecific arousal, N200 ERP from orienting system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p468fig12.65 [PHONET, ARTPHONE] linguistic properties: creates rate-invariant representations for variable-rate speech, paradoxical VC-CV category boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p422fig12.19 ARTSTREAM: derive streams from [pitch, source direction].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.21 Pitch shifts with component shifts, pitch vs lowest harmonic number.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p431fig12.27 ARTSTREAM & NormNet strip maps: variants of occular dominance columns in visual cortex.png
#Conscious mind, resonant brain: Table of Contents
#Conscious mind, resonant brain: sub-section list
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 1 - Overview
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 15 - Adaptively timed learning
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/images- captioned/p039tbl01.03 [consciousness, movement] links: visual, auditory, emotional.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p042tbl01.04 six main resonances which support different kinds of conscious awareness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p270fig06.16 Predictive remapping of eye movements, from V3A to LIP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.27 knowing vs seeing resonances: What [knowing, feature-prototype], Where [seeing, surface-shroud].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p355fig10.02 seeing vs knowing distinction is difficult because they interact so strongly.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p369fig10.19 Unconscious learning of motion direction, without [extra-foveal attention, awareness] of stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p396fig11.35 Three properties of bipole boundary grouping in V2: boundaries oscillate with rivalry-inducing stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p455fig12.52 cARTWORD dynamics 1-2-3: resonant activity in item and feature layers corresponds to conscious speech percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
#Grossberg's comments for some well-known consciousness theories
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p514fig13.44 Analog of the COgEM model maps of [object X, proto-self], assembly of second-order map.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.23 pointillist painting: Georges Seurat, A Sunday on la Grande Jatte.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.25 Matisse, The Roofs of Collioure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.27 Matisse: Open Window, Collioure, [continuously, sparsely] indiced surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.32 Claude Monet painting: Poppies Near Argenteuil.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.43 Four paintings by Monet of the Rouen cathedral under different lighting conditions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.44 Rouen Cathedral at sunset (Monet 1892-1894): equiluminant, obscured and less depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.46 Rouen Cathedral full sunlight (Monet 1892-1894): T-junctions greater depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.49 DaVinci stereopsis and occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p377fig11.11 DaVinci stereopsis: monocular information and depth percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p381fig11.15 DaVinci stereopsis variant of (Gillam, Blackburn, Nakayama 1999): same mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p382fig11.16 DaVinci stereopsis of [3 narrow, one thick] rectangles: same explanation.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 17 - A universal development code
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.19 Computing in a bounded activity domain, Gedanken experiment.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.26 Transmitter y [accumulation, release]:  y restored < infinite rate, evolution has exploited this.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.26 Brain [region, process]s that contribute to autistic behavioral symptoms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p189fig05.04 hippocampus & several brain regions [learn, remember] throughout life.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p233fig05.42 Evidence for the [gamma, beta] prediction in 3 parts of the brain.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.06 Circuit between [dentate granule, CA1 hippocampal pyramid] cells seems to compute spectrally timed responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.19 Conditioning, Attention, and Timing circuit: Hippocampus spectrum-> Amgdala orienting system-> neocortex motivational attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.26 Brain [region, process]s that contribute to autistic behavioral symptoms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p573fig16.01 Hippocampal place cells: discovery from rat [experimental chamber, neurophysiological recordings].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p575fig16.03 Rat navigation: firing patterns of [hippocampal place, entrorhinal grid] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p578fig16.04 Cross-sections of the hippocampal regions and the inputs to them.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.10 GRIDSmap embedded into hierarchy of SOMs: [angular head velocity, linear velocity] signals to place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p602fig16.37 Hippocampal inactivation by muscimol disrupts grid cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p603fig16.38 Role of hippocampal feedback in maintaining grid fields, muscimol inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p617fig16.50 Spectral Spacing models of [perirhinal what, parahippocampal where] inputs, fused in the hippocampus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 12 - From seeing and reaching to hearing and speaking
#Chapter 12 - From seeing and reaching to hearing and speaking
/home/bill/web/Neural nets/Grossberg/images- captioned/p030tbl01.02 complementary streams: What- [rapid, stable] learn invariant object categories, Where- [labile spatial, action] actions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p032fig01.21 [Retina, LGNs, V[1,2,3,4], MT] to What & Where areas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p039tbl01.03 [consciousness, movement] links: visual, auditory, emotional.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.20 G-wave theorem 2: [speed-up, scale] independent of [distance, scale size].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.21 Equal half-time property: multiple scales generate motion percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.61 RT task behavioural data: reach time (ms) vs % coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.22 negative Gaussian of an obstacle: avoid obstacle without losing sight of goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.25 M+ computes global motion estimate from noisy local motion estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p414fig12.11 [data, simulation]s from cortical areas 4 and 5 during a reach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.13 DIRECT model: Endogenous Random Generator learns volitional reaches.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.14 DIRECT reaches [unconstrained, with TOOL, elbow@140°, blind].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p417fig12.15 From Seeing & Reaching (DIRECT) to Hearing & Speaking (DIVA): homologous circular reactions, [tool use, coarticulation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p428fig12.25 ARTSPEECH: auditory-articulatory feedback loop & imitative map, [auditory, motor] dimensionally consistent, motor theory of speech.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p430fig12.26 NormNet: speaker normalization via specializations of mechanisms for auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p461fig12.58 lisTELOS shows [BG nigro-[thalamic, collicular], FEF, ITa, PFC, PNR-THAL, PPC, SEF, SC, V1, V4-ITp, Visual Cortex input] and [GABA].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p524fig14.04 Basal ganglia circuit for dopaminergic Now Print signals from the substantia nigra pars compacta in response to unexpected rewards.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.27 Spectrally timed SNc learning: brain [region, process]s release of dopaminergic signals, unexpected reinforcing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.29 Excitatory pathways that support activation of the SNc by a US and the conditioning of a CS to the US.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.30 Inhibitory pathway: striosomal cells predict [timing, magnitude] of reward signal to cancel it.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.32 Inhibitory pathway expectation magnitude: is a negative feedback control system for learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p569fig15.40 GO and STOP movement signals: control by [direct, indirect] basal ganglia circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.06 Binocular fusion by obligate cells in V1-3B when =[left,right] contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.09 V2 disparity filter solves correspondence problem: false matches suppressed by line-of-sight inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p581fig16.06 Trigonometry of spatial navigation: coactivation of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.09 GRIDSmap: stripe cells for rat trajectories, self-organizing map learned hexagonal grid cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.11 GRIDSmap learning of hexagonal grid fields, multiple phases per scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.13 Hexagonal grid cell receptive fields: somewhat insensitive to [number, directional selectivities] of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.14 GRIDSmap: Superimposed firing of stripe cells supports learning hexagonal grid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.15 Why is a hexagonal grid favored: stripe cells at intervals of 45 degrees, GRIDSmap does not learn, oscillatory interference does.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p587fig16.17 A refined analysis: SOM amplifies most frequent and energetic coactivations, stripe fields separated by [90°, 60°].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p588fig16.18 GridPlaceMap hierarchy of SOMs: coordinated learning of [grid, place, inomodal] cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 10 - Laminar computing by cerebral cortex
/home/bill/web/Neural nets/Grossberg/images- captioned/p011fig01.07 signal function determines how initial activity pattern is transformed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p233fig05.42 Evidence for the [gamma, beta] prediction in 3 parts of the brain.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.07 continuous motion illusions: [Beta with, Phi without] percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.08 Delta motion when [luminance, contrast] of flash 2 is larger than flash 1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.07 Log polar remapping of optic flow:  [expansion, circular] motion maps to single direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.34 spiking GridPlaceMap: generates theta-modulated place and grid cell firing, unlike the rate-based model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 17 - A universal development code
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.16 Cognitive-Emotional-Motor (CogEM) model: sensory cortex, amygdala, PFC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p058fig02.04 Learning a global arrow in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.05 Effects of intertrial and intratrial intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.06 Bow due to backward effect in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.16 Noise-Saturation Dilemma:sensitivity to ratios of inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.19 Computing in a bounded activity domain, Gedanken experiment.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p085fig02.38 Modelling method and cycle, technological applications: at each stage [behavioural data, design principles, neural data, math model and analysis].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p134fig04.14 Experiments on filling-in: in-the-act; simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.23 A perceptual disaster in the feature contour system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.29 bipole cells in cortical area V2: first neurophysiological evidence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.30 anatomy: horizontal connections in V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p152fig04.31 Bipoles through the ages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.36 Graffiti art by Banksy: amodal boundary completion; spatial impenetrability.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p162fig04.38 Cooperation and competition during grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p165fig04.41 Kanizsa-Minguzzi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p252fig06.01 surface-shroud resonance forms as objects bid for spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p257fig06.05 Spatial attention flows along object boundaries: Macaque V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.06 Neurophysiological data & simulation: attention can flow along a curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p265fig06.13 basal ganglia gate perceptual, cognitive, emotional, etc through parallel loops.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p267fig06.14 Perceptual consistency and figure-ground separation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p271fig06.17 Persistent activity in IT to [view, position, size]-invariant category learning by positional ARTSCAN.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p273fig06.20 pARTSCAN simulation of Li & DiCarlo IT cell swapping data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.21 pARTSCAN [position invariance, selectivity] trade-off of Zoccolan etal 2007.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.23 IT responses to image morphs, data vs model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p284fig07.02 Persistence decreases with flash illuminance & duration [data, simulations].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p285fig07.03 Persistence decrease: rebound to input offset inhibits bipole cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.04 Illusory contours persist longer than real contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.05 Illusory contours inhibited by OFF cell rebounds, propagate to center.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.06 Persistence: [less, more] as adaptation orientation [same, orthogonal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.07 Persistence increases with distance, due to weaker spatial competition in hypercomplex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.08 Delta motion when [luminance, contrast] of flash 2 is larger than flash 1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.09 motion in opposite directions perceived when 2 later flashes on either side of 1st flash.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.10 motion speed-up perceived when flash duration decreases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.22 Korte Laws: ISIs in the hundreds of milliseconds can cause apparent motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.30 3D FORMOTION model: feature tracking [get directional, inhibit inconsistent] signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p319fig08.38 Solving the aperture problem takes time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p333fig08.58 LIP responses during RT task correct trials: coherence and [activation, inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.59 LIP responses for FD task: predictiveness decreases with increasing coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.60 [RT, FD] task behavioral data: more coherence in the motion causes more accurate decisions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.61 RT task behavioural data: reach time (ms) vs % coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.62 LIP encodes not only where, but also when, to move the eyes - No Bayes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.11 importance of efference copy in real movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.13 How do our brains figure out which views belong to which pear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.20 Steering from optic flow: goals are attractors, obstacles are repellers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p352fig09.26 heading direction final stage: beautiful optic flow, accuracy matches humans.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.09 Grouping starts in layer 2-3: long-range horizontal excitation, short-range inhibition of target pyramidal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p361fig10.10 Bipole property controls perceptual grouping: inputs [excitatory sum, inhibitory normalize].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.15 Attention protects target from masking stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.16 Flankers can enhance or suppress targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.17 Attention has greater effect on low contrast targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.18 Texture reduces response to a bar: [iso-orientation, perpendicular] suppression.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p369fig10.19 Unconscious learning of motion direction, without [extra-foveal attention, awareness] of stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.31 LIGHTSHAFT model: determining depth-from-texture percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.39 Simulation of the eye rivalry data of (Lee, Blake 1999).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.43 A pair of disparate images of a scene from the University of Tsukuba.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p407fig12.03 Motor cortical cells: vectors for [direction, length] of commanded movement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p409fig12.04 VITE simulations: difference vector emergent from network interactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.06 Monkeys transform movement: 2 -> 10 o'clock target, 50 or 100 msec after activation of 2 o'clock target.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p414fig12.11 [data, simulation]s from cortical areas 4 and 5 during a reach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.21 Pitch shifts with component shifts, pitch vs lowest harmonic number.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p428fig12.25 ARTSPEECH: auditory-articulatory feedback loop & imitative map, [auditory, motor] dimensionally consistent, motor theory of speech.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p432fig12.28 SpaN: spatial representations of numerical quantities in the parietal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.32 Free recall bowed serial position curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.33 Working memory models: item and order, or competitive queuing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p443fig12.41 Neurophysiology of sequential copying: [primacy gradient, self-inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p447fig12.45 LIST PARSE: order errors vs serial position with extended pauses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p452fig12.48 Perceptual integration of acoustic cues: [silence vs noise] durations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.49 ARTWORD: acoustic cues, phonetic [features, WM], Masking Field unitized lists, gain control.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p465fig12.63 lisTELOS and data: microstimulation biases selection so saccade trajectories converge toward a single location in space.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p468fig12.65 [PHONET, ARTPHONE] linguistic properties: creates rate-invariant representations for variable-rate speech, paradoxical VC-CV category boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.66 PHONET: relative duration of [consonant, vowel] pairs can [preserve, change] a percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p473fig12.69 ART Matching Rule properties explain error rate and mean reaction time (RT) data from lexical decision experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p474fig12.70 macrocircuit model to explain lexical decision task data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p476fig12.71 Word frequency data model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.06 Classical conditioning: inverted-U vs InterStimulus Interval (ISI).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.31 Behavioral contrast rebounds: decrease [food-> negative Frustration, shock-> positive Relief] reinforcers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p533fig14.09 ARTSCENE search [data, simulation]s for 6 pairs of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p542fig15.04 blinks of [nictitating membrane, eyelid] are adaptively timed: closure occurs at arrival of the US following the CS, obeys Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.05 Learning with two ISIs: each peak obeys Weber Law, strong evidence for spectral learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p556fig15.24 Cerebellar cortex responses: [data, model] short latency responses after lesioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.28 Neurophysiological data and simulations of SNc responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p573fig16.01 Hippocampal place cells: discovery from rat [experimental chamber, neurophysiological recordings].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p574fig16.02 Neurophysiological recordings of 18 different place cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p575fig16.03 Rat navigation: firing patterns of [hippocampal place, entrorhinal grid] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.08 Evidence for stripe-like cells: entorhinal cortex data, Band Cells position from grid cell oscillatory interference.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p589fig16.19 How does the spatial scale increase along the MEC dorsoventral axis.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.26 Gradient of grid spacing along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p594fig16.27 Gradient of field width along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p595fig16.28 Peak and mean rates at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.29 Subthreshold membrane mV oscillations: decreasing Hz at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.30 Spatial phases of learned grid and place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.31 Multimodal place cell firing in large spaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.32 Model fits data about grid cell development in juvenile rats:  grid [score increases, spacing flat].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.33 Model fits [place, grid, directional] cell data about grid cell development in juvenile rats:  [spatial information, inter-trial stability] vs postnatal day.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p599fig16.35 anatomically overlapping grid cell modules: effects of [different modules in one animal, DV location, response rate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p602fig16.37 Hippocampal inactivation by muscimol disrupts grid cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p603fig16.38 Role of hippocampal feedback in maintaining grid fields, muscimol inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p607fig16.40 Effects of medial septum (MS) inactivation on grid cells: data, simulations, gridness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p085fig02.38 Modelling method and cycle, technological applications: at each stage [behavioural data, design principles, neural data, math model and analysis].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.33 Some early ARTMAP benchmark studies (no image - link instead).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.33 MOTIVATOR: thalamocortical loops through basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.34 Distinct basal ganglia zones for each loop.png
images- Grossberg 2021/p057fig02.03 neuron basic [anatomy, physiology].png
images- Grossberg 2021/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
images- Grossberg 2021/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
images- Grossberg 2021/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
images- Grossberg 2021/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#Early ARTMAP benchmark studies
images- Grossberg 2021/p107fig03.26 drawing directly in color leads to colored surface representations.png
images- Grossberg 2021/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#Early ARTMAP benchmark studies
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/cover image.png
/home/bill/web/Neural nets/Grossberg/images- captioned/cover image.png
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#Introduction
#Conscious mind, resonant brain: Table of Contents
#Conscious mind, resonant brain: sub-section list
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Preface
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 1 - Overview
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 2 - How a brain makes a mind
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 3 - How a brain sees: Constructing reality
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 4 - How a brain sees: Neural mechanisms
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 5 - Learning to attend, recognize, and predict the world
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 7 - How do we see a changing world?
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 8 - How we see and recognize object motion
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 9 - Target tracking, navigation, and decision-making
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 10 - Laminar computing by cerebral cortex
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 11 - How we see the world in depth
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 12 - From seeing and reaching to hearing and speaking
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 13 - From knowing to feeling
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 14 - How prefrontal cortex works
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 15 - Adaptively timed learning
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 16 - Learning maps to navigate space
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 17 - A universal development code
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#Introduction
#Conscious mind, resonant brain: Table of Contents
#Conscious mind, resonant brain: sub-section list
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Preface
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 1 - Overview
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 2 - How a brain makes a mind
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 3 - How a brain sees: Constructing reality
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 4 - How a brain sees: Neural mechanisms
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 5 - Learning to attend, recognize, and predict the world
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 7 - How do we see a changing world?
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 8 - How we see and recognize object motion
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 9 - Target tracking, navigation, and decision-making
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 10 - Laminar computing by cerebral cortex
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 11 - How we see the world in depth
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 12 - From seeing and reaching to hearing and speaking
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 13 - From knowing to feeling
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 14 - How prefrontal cortex works
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 15 - Adaptively timed learning
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 16 - Learning maps to navigate space
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 17 - A universal development code
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#Introduction
#Conscious mind, resonant brain: Table of Contents
#Conscious mind, resonant brain: sub-section list
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Preface
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 1 - Overview
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 2 - How a brain makes a mind
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 3 - How a brain sees: Constructing reality
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 4 - How a brain sees: Neural mechanisms
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 5 - Learning to attend, recognize, and predict the world
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 7 - How do we see a changing world?
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 8 - How we see and recognize object motion
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 9 - Target tracking, navigation, and decision-making
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 10 - Laminar computing by cerebral cortex
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 11 - How we see the world in depth
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 12 - From seeing and reaching to hearing and speaking
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 13 - From knowing to feeling
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 14 - How prefrontal cortex works
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 15 - Adaptively timed learning
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 16 - Learning maps to navigate space
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 17 - A universal development code
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#Introduction
#Conscious mind, resonant brain: Table of Contents
#Conscious mind, resonant brain: sub-section list
/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Preface
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 1 - Overview
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 2 - How a brain makes a mind
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 3 - How a brain sees: Constructing reality
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 4 - How a brain sees: Neural mechanisms
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 5 - Learning to attend, recognize, and predict the world
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 6 - Conscious seeing and invariant recognition
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 7 - How do we see a changing world?
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 8 - How we see and recognize object motion
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 9 - Target tracking, navigation, and decision-making
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 10 - Laminar computing by cerebral cortex
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 11 - How we see the world in depth
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 12 - From seeing and reaching to hearing and speaking
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 13 - From knowing to feeling
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 14 - How prefrontal cortex works
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 15 - Adaptively timed learning
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 16 - Learning maps to navigate space
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [chapter, section]s.html#Chapter 17 - A universal development code
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#Instructions
#Preface
#Chapter 1 - Overview
#Chapter 2 - How a brain makes a mind
#Chapter 3 - How a brain sees: Constructing reality
#Chapter 4 - How a brain sees: Neural mechanisms
#Chapter 5 - Learning to attend, recognize, and predict the world
#Chapter 6 - Conscious seeing and invariant recognition
#Chapter 7 - How do we see a changing world?
#Chapter 8 - How we see and recognize object motion
#Chapter 9 - Target tracking, navigation, and decision-making
#Chapter 10 - Laminar computing by cerebral cortex
#Chapter 11 - How we see the world in depth
#Chapter 12 - From seeing and reaching to hearing and speaking
#Chapter 13 - From knowing to feeling
#Chapter 14 - How prefrontal cortex works
#Chapter 15 - Adaptively timed learning
#Chapter 16 - Learning maps to navigate space
#Chapter 17 - A universal development code
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p002fig01.01 Seeing an object vs knowing what it is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p002fig01.02 Dalmation in snow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p003fig01.03 Amodal completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p004fig01.04 Kanizsa stratification: transparency images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p008fig01.05 Noise-saturation dilemma: cell activity; current activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p009fig01.06 Primacy gradient of activity in a recurrent shunting OC-OS network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p011fig01.07 signal function determines how initial activity pattern is transformed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p012fig01.08 Sigmoidal signal: a hybrid of [same, slower, faster]-than-linear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p013fig01.09 sigmoid signal: quenching threshold; contrast enhancement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.10 Minimal adaptive prediction: blocking and unblocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.11 BU-TD mismatch -> orienting system -> nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p018fig01.12 Peak shift and behavioural contrast: prefer new experiences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p019fig01.13 Affective circuits are organized into opponent channels.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p023fig01.14 gated dipole opponent process: sustained on-response; transient off-response.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p024fig01.15 READ circuit: REcurrent Associative Dipole.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.16 Cognitive-Emotional-Motor (CogEM) model: sensory cortex, amygdala, PFC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.17 Sensory-drive heterarchy vs drive hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p026fig01.18 Inverted-U behaviour vs arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p027fig01.19 ventral What [percept, class], dorsal Where [spatial represent, action].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p029tbl01.01 complementary streams [visual boundary, what-where, perception & recognition, object tracking, motor target].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030tbl01.02 complementary streams: What- [rapid, stable] learn invariant object categories, Where- [labile spatial, action] actions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030fig01.20 neo-cortex 6 layers: same canonical laminar design cart [vision, speech, cognition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p032fig01.21 [Retina, LGNs, V[1,2,3,4], MT] to What & Where areas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p035fig01.22 Presentation [normal, silence, noise replaced].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p036fig01.23 working memory [longer list, bigger chunk]s.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p037fig01.24 sentence [learn, store, class] via 3 streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p039tbl01.03 [consciousness, movement] links: visual, auditory, emotional.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p042tbl01.04 six main resonances which support different kinds of conscious awareness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p051fig02.01 laterial inhibition: darker appears darker; lighter appears lighter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p052fig02.02 Adaptive Resonance reactivation: features bottom-up; categories top-down.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p057fig02.03 neuron basic [anatomy, physiology].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p058fig02.04 Learning a global arrow in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.05 Effects of intertrial and intratrial intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.06 Bow due to backward effect in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p060fig02.07 Error gradients depend on list position.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p061fig02.08 neural networks can learn forward and backward associations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p063fig02.09 Short Term Memory (STM): Additive Model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.10 STM Shunting Model, mass action in membrane equations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.11 MTM habituative transmitter gate; LTM gated steepest descent learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p065fig02.12 Three sources of neural network research: [binary, linear, nonlinear].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.13 Hartline: lateral inhibition in limulus retina of horseshoe crab.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.15 Noise-Saturation Dilemma: functional unit is a spatial activity pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.16 Noise-Saturation Dilemma:sensitivity to ratios of inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p072fig02.17 Vision: brightness constancy, contrast normalization.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p072fig02.18 Vision: brightness contrast, conserve a total quantity, total activity normalization.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.19 Computing in a bounded activity domain, Gedanken experiment.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.20 Shunting saturation occurs when inputs get larger to non-interacting cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.21 Shunting saturation: how shunting saturation turns on all of a cells excitable sites as input intensity increases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.22 Computing with patterns: how to compute the pattern-sensitive variable.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p074fig02.23 Shunting on-center off-surround network: no saturation! infinite dynamical range, conserve total activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.25 Weber law, adaptation, and shift property, convert to logarithmic coordinates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.26 Mudpuppy retina neurophysiology, adaptation- sensitivity shifts for different backgrounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.27 Mechanism: cooperative-competitive dynamics, subtractive lateral inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.28 Weber Law and adaptation level: hyperpolarization vs silent inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.29 Weber Law and adaptation level: adaptation level theory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.30 Noise suppression: attenuate zero spatial frequency patterns- no information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.31 Noise suppression -> pattern matching: mismatch (out of phase) suppressed, match (in phase) amplifies pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p079fig02.32 Substrate of resonance: match (in phase) of BU and TD input patterns amplifies matched pattern due to automatic gain control by shunting terms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.33 How do noise suppression signals arise: symmetry-breaking during morphogenesis, opposites attract rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.34 Symmetry-breaking: dynamics and anatomy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.35 Ratio contrast detector: reflectance processing, contrast normalization, discount illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.36 [Noise suppression, contour detection]: uniform patterns are suppressed, contrasts are selectively enhanced, contours are detected.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p085fig02.38 Modelling method and cycle, technological applications: at each stage [behavioural data, design principles, neural data, math model and analysis].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p089fig03.02 What do you think lies under the two grey disks (on a checkers board).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p090fig03.03 Kanizsa square and reverse-contrast Kanizsa square precepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p091fig03.04 blind spot and veins can occlude light to the retina.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p093fig03.06 Every line is an illusion!: boundary completion, surface filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p094fig03.07 Complementary properties of boundaries and surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p095fig03.08 Computer simulation of a Kanizsa square percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p095fig03.09 Simulation of a reverse-contrast Kanizsa square percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.10 The visual illusion of eon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.11 Another example of neon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p098fig03.12 Einstein's face: [edges, texture, shading] are overlaid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.13 Ehrenstein percept weakened as lines deviate from perpendicular.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.14 Perpendicular induction at line ends: [locally [,un], globally] preferred.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.15 orientations: [transient before, equilibrium after] choice.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p102fig03.16 Ts and Ls group together based on shared orientations, not identities.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p102fig03.17 Positions of squares give rise to a percept of three regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p103fig03.18 different spatial arrangements of inducers: emergent [horizontal, diagonal] groupings, but inducers have vertical orientations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p103fig03.19 [diagonal, perpendicular, parallel]: thats how multiple orientations can induce boundary completion of an object.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.20 Sean Williams: how boundaries can form.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.23 pointillist painting: Georges Seurat, A Sunday on la Grande Jatte.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p106fig03.24 Do these ideas work on hard problems: Synthetic Aperture Radar [discount illuminant, filling-in, boundaries].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.25 Matisse, The Roofs of Collioure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.26 drawing directly in color leads to colored surface representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.27 Matisse: Open Window, Collioure, [continuously, sparsely] indiced surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p109fig03.29 Chiaroscuro- Rembrandt self-portrait; Trompe l oeil- Graham Rust.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p109fig03.30 Jo Baer triptych: Primary Light Group [red, green, blue].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.31 Henry Hensche painting: The Bather, is suffused with light.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.32 Claude Monet painting: Poppies Near Argenteuil.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p112fig03.33 Boundary web gradient can cause self-luminosity, similar to watercolor illusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p112fig03.34 Examples of Ross Bleckner's self-luminous paintings.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p113fig03.35 Highest Luminance As White (HLAW) rule, Hans Wallach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p113fig03.36 Blurred Highest Luminance As White (BHLAW) rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p114fig03.37 Perceived reflectance vs cross-section of visual field: anchored brightness, self-luminous.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p114fig03.38 Color field painting: Jules Olitski, spray paintings of ambiguous depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p115fig03.39 Gene Davis paintings [full color, monochromatic]: percepts of grouping and relative depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p116fig03.40 Mona Lisa by Leonardo da Vinci: T-junctions and perspective cues give strong percept of depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p117fig03.41 Boundary contours and feature contours- no inhibition, feature signals survive and spread.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p117fig03.42 Two paintings by Frank Stella.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.43 Four paintings by Monet of the Rouen cathedral under different lighting conditions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.44 Rouen Cathedral at sunset (Monet 1892-1894): equiluminant, obscured and less depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.46 Rouen Cathedral full sunlight (Monet 1892-1894): T-junctions greater depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p123fig04.01 Combining stabilized images with filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p124fig04.02 closed boundaries prevent brightness from flowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p126fig04.03 Color constancy: compute ratios, discount the illuminant, compute lightness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p128fig04.04 reflectance changes at contours: fill-in illuminant-discounted colors.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p129fig04.05 reflectance changes at contours: color contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p129fig04.06 reflectance changes at contours: fill-in color; resolve uncertainty.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p130fig04.07 brightness constancy: boundary peaks spatially narrower than feature peaks.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p131fig04.08 brightness constancy: discount illuminant, ratio-sensitive feature contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p131fig04.09 Simulation of brightness contrast.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p132fig04.10 Simulation of brightness assimilation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p132fig04.11 Simulation of double step and COCE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p133fig04.12 Simulation of the 2D COCE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p133fig04.13 Contrast constancy, relative luminances can be reversed, discounting illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p134fig04.14 Experiments on filling-in: in-the-act; simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p138fig04.15 oriented filtering to grouping and boundary completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p139fig04.16 Simplest simple cell model: threshold linear, half-wave rectification.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p140fig04.17 Complex cells: pool like-oriented simple cells of opposite polarity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.18 Binocular Disparity to reconstruct depth from 2D retinal inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.19 Laminar cortical circuit for complex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p142fig04.20 [, reverse-]Glass patterns give rise to different boundary groupings.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p143fig04.21 Hierarchical resolution of uncertainty for a given field size.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p144fig04.22 End Gap and End Cut simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.23 A perceptual disaster in the feature contour system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.24 Hierarchical resolution of uncertainty- End Cuts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p146fig04.25 How are end cuts created: two stages of short-range competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p148fig04.26 End cut during neon color spreading via 2 stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p149fig04.27 Bipole cells boundary completion: long cooperation & short inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p150fig04.28 Bipole property: boundary completion via long-range cooperation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.29 bipole cells in cortical area V2: first neurophysiological evidence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.30 anatomy: horizontal connections in V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p152fig04.31 Bipoles through the ages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p156fig04.33 emergent boundary groupings can segregate textured regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p157fig04.34 texture: Boundary Contour System resolves errors of complex channels model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.35 Spatial impenetrability prevents grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.36 Graffiti art by Banksy: amodal boundary completion; spatial impenetrability.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p161fig04.37 Boundary Contour System model: analog-sensitive boundary completion Kanizsas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p162fig04.38 Cooperation and competition during grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p164fig04.40 Koffka-Benussi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p165fig04.41 Kanizsa-Minguzzi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p166fig04.42 Computer simulation of Kanizsa-Minguzzi ring percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p167fig04.43 T-junction sensitivity: image, Bipole cells, boundary.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.44 main [boundary, surface] formation stages: LGN-> V1-> V2-> V4.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.45 ON and OFF feature contours: filled-in regions when adjacent to boundary.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p170fig04.46 regions can fill-in feature contour inputs when [adjacent to, collinear with] boundary contour inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p170fig04.47 A double-opponent network processes output signals from FIDOs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.48 closed boundaries -> filling-in; open boundaries -> color spread.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.49 DaVinci stereopsis and occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p173fig04.50 closed boundary at prescribed depth: addition of [bi, mon]ocular boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p176fig04.53 OC-OS [within position, across depth]: brighter Kanizsas look closer.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p178fig04.54 figure-ground separation: bipole cooperation and competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p178fig04.55 Amodal completion of boundaries and surfaces in V2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p179fig04.56 Visible surface 3D perception: boundary enrichment, surface filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p182fig04.58 LAMINART explains many percepts of transparency.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.01 Learn many-to-one (compression, naming), one-to-many (expert knowledge) maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.02 Many-to-one map, two stage compression: [visual, auditory] categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.03 Many-to-one map: IF-THEN rules: [symptom, test, treatment]s; length of stay.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p189fig05.04 hippocampus & several brain regions [learn, remember] throughout life.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.05 LGN [ON, OFF] cells respond differently to [side, end]s of lines.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.07 detailed connections between [retinal ganglion cells, LGN, V1].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.08 LGN [activation, inhibition], with[, out] top-down feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p194fig05.09 [feature, boundary] contours from Ehrenstein disk stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p198fig05.10 Competitive learning and Self-Organized Maps (SOMs).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p199fig05.11 Instar learning: bottom-up adaptive filter for feature patterns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.12 Duality of [outstar, instar] networks.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.14 Outstar learning, both [in, de]creases for LTM to learn STM pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p201fig05.15 Spatial learning pattern, outstar learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.16 Geometry of choice and learning, classifying vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.17 Geometry of choice and learning, trains the closest LTM vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p205fig05.18 catastrophic forgetting due to [competition, associative] learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.20 [PN match, N200 mismatch] computationally complementary potentials.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p213fig05.22 If inputs incorrectly activate a category, how to correct the error.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p213fig05.23 A [category, symbol, other] cannot determine whether an error has occurred.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.24 Learning top-down expectations occurs during bottom-up learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.25 Error correction: [learn, compare] TD-BU inputs, Processing Negativity ERP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.26 Mismatch triggers nonspecific arousal, N200 ERP from orienting system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.27 Every event has [specific attentional cue, nonspecific orienting arousal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p220fig05.29 Vigilance [excitation: search better match, inhibition: resonance & learning].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.30 predictive error -> vigilance increase just enough -> minimax learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p224fig05.32 Learning the alphabet with two different levels of vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.33 Some early ARTMAP benchmark studies (no image - link instead).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.34 ARTMAP learned maps of natural terrains better than AI expert systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p233fig05.42 Evidence for the [gamma, beta] prediction in 3 parts of the brain.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p236fig05.43 nucleus basalis of Meynert releases ACh, reduces AHP, increases vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p242fig05.46 Distributed ARTMAP variants learn the 5-4 category structure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p245fig05.47 [long-range excitatory, short-range disynaptic inhibitory] connections realize the bipole grouping law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p252fig06.01 surface-shroud resonance forms as objects bid for spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p254fig06.03 ARTSCAN Search model learns to recognize and name invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p255fig06.04 The ARTSCAN Search for a desired target object in a scene: Wheres Waldo.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p257fig06.05 Spatial attention flows along object boundaries: Macaque V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.06 Neurophysiological data & simulation: attention can flow along a curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.07 Top-down attentional spotlight becomes a shroud.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p259fig06.08 dARTSCN spatial attention hierarchy [Fast Where, Slow What] stream.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p260fig06.09 Crowding: visible objects & confused recognition, increased flanker spacing at higher eccentricity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p260fig06.10 cortical magnification transforms coordinates: artesian (retina) to log polar (V1).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p261fig06.11 Crowding: visible objects and confused recognition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p261fig06.12 A more serial search is needed due to overlapping conjunctions of features.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p265fig06.13 basal ganglia gate perceptual, cognitive, emotional, etc through parallel loops.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p267fig06.14 Perceptual consistency and figure-ground separation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p268fig06.15 saccades within an object: figure-ground outputs control eye movements via V3AA.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p270fig06.16 Predictive remapping of eye movements, from V3A to LIP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p271fig06.17 Persistent activity in IT to [view, position, size]-invariant category learning by positional ARTSCAN.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.18 pARTSCAN: positionally-invariant object learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.19 persistent activity needed to learn positionally-invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p273fig06.20 pARTSCAN simulation of Li & DiCarlo IT cell swapping data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.21 pARTSCAN [position invariance, selectivity] trade-off of Zoccolan etal 2007.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.22 pARTSCAN: IT cortex processes image morphs with high vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.23 IT responses to image morphs, data vs model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.24 Sterogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p276fig06.25 saccades: predictive gain fields [binocular fusion, filling-in of surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p277fig06.26 Predictive remapping maintains binocular boundary fusion as eyes move.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.27 knowing vs seeing resonances: What [knowing, feature-prototype], Where [seeing, surface-shroud].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p283fig07.01 Boundary competition: spatial habituative gates, orientation gated dipole, bipole grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p284fig07.02 Persistence decreases with flash illuminance & duration [data, simulations].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p285fig07.03 Persistence decrease: rebound to input offset inhibits bipole cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.04 Illusory contours persist longer than real contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.05 Illusory contours inhibited by OFF cell rebounds, propagate to center.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.06 Persistence: [less, more] as adaptation orientation [same, orthogonal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.07 Persistence increases with distance, due to weaker spatial competition in hypercomplex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p290fig08.01 Motion pools contrast-sensitive information moving in the same direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p291fig08.02 Complex cells respond to motion: opposite [direction, contrast polarities].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p292fig08.03 Visual aftereffects: [form- MacKay 90 degree, motion- waterfall  180].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p293fig08.04 Local vs overall motion: aperture problem of EVERY neurons receptive field.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p295fig08.05 sparse feature tracking signals [capture ambiguous, determine perceived] motion direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.06 Simplest example of apparent motion: two dots turning on and off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.07 continuous motion illusions: [Beta with, Phi without] percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.08 Delta motion when [luminance, contrast] of flash 2 is larger than flash 1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.09 motion in opposite directions perceived when 2 later flashes on either side of 1st flash.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.10 motion speed-up perceived when flash duration decreases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.11 illusory contours: double illusion in V1-V2, motion V2-MT interaction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.12 Single flash: Gaussian receptive fields, recurrent OC-OS winner-take-all.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.13 Nothing moves: [single flash, exponential decay], Gaussian peak fixed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.14 Visual inertia: flash decay after the flash shuts off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p301fig08.15 two flashes: cell activation by first waning while second one is waxing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p301fig08.16 sum Gaussian flash activity profiles: [waning 1st, waxing 2nd] -> travelling wave.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p302fig08.17 maximum long-rang apparent motion: Gaussian kernel spans successive flashes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p302fig08.18 G-wave theorem 1: wave moves continuously IFF L <= 2*K.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.19 No motion vs motion at multiple scales: flash distance L, Gaussian width K.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.20 G-wave theorem 2: [speed-up, scale] independent of [distance, scale size].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.21 Equal half-time property: multiple scales generate motion percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.22 Korte Laws: ISIs in the hundreds of milliseconds can cause apparent motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p305fig08.23 Ternus motion: ISI [small- stationary, intermediate- element, larger- group].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p305fig08.24 Reverse-contrast Ternus motion: ISI [small- stationarity, intermediate- group (not element!), larger- group] motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p306fig08.25 Motion BCS model [explain, simulate]s long-range motion percepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p306fig08.26 3D FORMOTION model: track objects moving in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p307fig08.27 Ternus motion: [element- weak, group- strong] transients, element [visual persistence, perceived stationarity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p308fig08.28 Ternus group motion: Gaussian filter of 3 flashes forms one global maximum.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p310fig08.29 when individual component motions combine, their perceived direction & speed changes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.30 3D FORMOTION model: feature tracking [get directional, inhibit inconsistent] signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.31 Motion BCS stages: locally ambiguous motion signals -> globally coherent percept, solving the aperture problem.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p312fig08.32 Schematic of motion filtering circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p312fig08.33 Processing motion signals by a population of speed-tuned neurons.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p314fig08.34 VISTARS navigation model: FORMOTION front end for navigational circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p316fig08.36 Motion capture by directional grouping feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p317fig08.37 Motion capture by directional grouping feedback: [short, long]-range filters, transient cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p319fig08.38 Solving the aperture problem takes time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p320fig08.39 Simulation of the barberpole illusion direction field at two times.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p321fig08.40 [, in]visible occluders [do, not] capture boundaries they share with moving edges.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p322fig08.41 motion transparency: asymmetry [near, far], competing opposite directions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p323fig08.42 Chopsticks: motion separation in depth via [, in]visible occluders [display, percept].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p324fig08.43 ambiguous X-junction motion: MT-MST directional grouping bridges the ambiguous position.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p325fig08.44 The role of MT-V1 feedback:  [motion-form feedback, bipole boundary completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p325fig08.45 Closing formotion feedback loop [MT, MST]-to-V1-to-V2-to-[MT, MST].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p326fig08.46 How do we perceive relative motion of object parts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p327fig08.47 Two classical examples of part motion: Symmetrically moving inducers; Duncker wheel.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p328fig08.48 vector decomposition: (retinal - common = part) motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p328fig08.49 What is the mechanism of vector decomposition, prediction: directional peak shift.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p329fig08.50 How is common motion direction computed?  retinal motion->  bipole grouping (form stream)->  V2-MT formotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p329fig08.51 Large and small scale boundaries differentially form illusory contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.52 Correct motion directions after the peak shift top-down expectation acts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.53 Simulation of the various directional signals of the left dot through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.54 Motion directions of a single dot moving slowly along a cycloid curve through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.55 Duncker Wheel, large: stable rightward motion at the center captures motion at the rim.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p332fig08.56 Duncker Wheel, small: wheel motion as seen when directions are collapsed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p332fig08.57 MODE (MOtion DEcision) model: Motion BCS -> saccadic target selection -> basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p333fig08.58 LIP responses during RT task correct trials: coherence and [activation, inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.59 LIP responses for FD task: predictiveness decreases with increasing coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.60 [RT, FD] task behavioral data: more coherence in the motion causes more accurate decisions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.61 RT task behavioural data: reach time (ms) vs % coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.62 LIP encodes not only where, but also when, to move the eyes - No Bayes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p338fig09.01 optic flow through brain regions: moving observer [navigate, track] moving object.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p338fig09.02 Heading (focus of velocity field) from optic flow: humans accurate +- 1 to 2 degrees.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p339fig09.03 Heading with [body move, eye rotate, combined] -> optic flow [expand, translate, rotate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p339fig09.04 How can translation flow (eye rotation) be subtracted from spiral flow to recover the expansion flow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.05 efference copy command: may use outflow movement commands to eye muscles.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.06 Corollary discharges from outflow movement commands that move muscles.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.07 Log polar remapping of optic flow:  [expansion, circular] motion maps to single direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p341fig09.08 optic flows [retina, V1, MT, MSTd, parietal cortex], V1 log polar mapping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p341fig09.09 MSTd cells are sensitive to [spiral, rotation, expansion] motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.10 Retina -> log polar -> MSTd cell, heading eccentricity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.11 importance of efference copy in real movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.12 two retinal views of the Simpsons: [separate, recognize] overlapping figures.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.13 How do our brains figure out which views belong to which pear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p344fig09.14 Heading sensitivity unimpaired: MT tuning width 38°, MSTd spiral tuning 61°.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p345fig09.15 MT double opponent directional fields: relative motions [objects, backgrounds].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p346fig09.16 macrocircuit of 13 brain regions used to move the eyes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p347fig09.17 leftward eye movement model: retina-> MT-> MST[v,d]-> pursuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p347fig09.18 MST[v,d] circuits enable predictive target tracking by the pursuit system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p348fig09.19 MSTv cells: target speed on retina, background speed on retina, pursuit speed command.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.20 Steering from optic flow: goals are attractors, obstacles are repellers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.21 Steering dynamics goal approach: [obstacle, goal, heading] -> steering.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.22 negative Gaussian of an obstacle: avoid obstacle without losing sight of goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.23 Unidirectional transient cells: [lead, trail]ing boundaries, driving video.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.24 Directional transient cells respond most to motion in their preferred directions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.25 M+ computes global motion estimate from noisy local motion estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p352fig09.26 heading direction final stage: beautiful optic flow, accuracy matches humans.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p355fig10.02 seeing vs knowing distinction is difficult because they interact so strongly.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.05 Activation of V1 by direct excitatory signals from LGN to layer 4 of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.06 Why another layer 6-to-4 signal: on-center off-surround.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.09 Grouping starts in layer 2-3: long-range horizontal excitation, short-range inhibition of target pyramidal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p361fig10.10 Bipole property controls perceptual grouping: inputs [excitatory sum, inhibitory normalize].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p362fig10.11 Final grouping: folded feedback, strongest enhanced on-center, weaker suppressed off-surround, interlaminar functional columns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p363fig10.12 V2 repeats V1 circuitry at larger spatial scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.14 Explanation: grouping and attention share the same modulatory decision circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.15 Attention protects target from masking stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.16 Flankers can enhance or suppress targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.17 Attention has greater effect on low contrast targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.18 Texture reduces response to a bar: [iso-orientation, perpendicular] suppression.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p369fig10.19 Unconscious learning of motion direction, without [extra-foveal attention, awareness] of stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p371fig11.01 FACADE theory explains how the 3D boundaries and surfaces are formed to see the world in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p372fig11.02 3D surface filling-in of [lightness, color, depth] by a single process: FACADE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p373fig11.03 Both [contrast-specific binocular fusion, contrast-invariant boundary perception] are needed to see the world in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p374fig11.04 Three processing stages of [monocular simple, complex] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p374fig11.05 Contrast constraint on binocular fusion: only contrasts which are derived from the same objects in space are binoculary matched.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.06 Binocular fusion by obligate cells in V1-3B when =[left,right] contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.08 Correspondance problem: How does the brain inhibit false matches. contrast constraint not enough.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.09 V2 disparity filter solves correspondence problem: false matches suppressed by line-of-sight inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p377fig11.11 DaVinci stereopsis: monocular information and depth percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p378fig11.12 3D LAMINART: V2 monocular+binocular line of sight inputs -> depth perception.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p381fig11.15 DaVinci stereopsis variant of (Gillam, Blackburn, Nakayama 1999): same mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p382fig11.16 DaVinci stereopsis of [3 narrow, one thick] rectangles: same explanation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p383fig11.17 Venetian blind effect: [left, right] eye matching bars.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p384fig11.18 Venetian blind effect: Surface[, -to-boundary] surface contour signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p385fig11.19 Dichoptic masking: [left, right] images have sufficiently different contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p385fig11.20 Dichoptic masking, Panum's limiting case: simplified version of Venetian blind effect.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p386fig11.21 Craik-O'Brien-Cornsweet Effect: 2D surface at a very near depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p387fig11.22 Julesz stereogram: boundaries with[out, ] surface contour feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p388fig11.23 Sparse stereogram, large regions of ambiguous white: correct surface in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p388fig11.24 depth-ambiguous feature contours: boundary groups lift to correct surface in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p389fig11.25 Boundaries: not just edge detectors, or a shaded ellipse would look [flat, uniformly gray].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p390fig11.26 Multiple-scale depth-selective groupings determine perceived depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p391fig11.27 Multiple-scale grouping and size-disparity correlation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p391fig11.28 Ocular dominance columns, LGN mappings into layer 4C of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p392fig11.29 3D vision figure-ground separation: multiple-scale, depth-selective boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p392fig11.30 How multiple scales vote for multiple depths, scale-to-depth and depth-to-scale maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.31 LIGHTSHAFT model: determining depth-from-texture percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.32 Kulikowski stereograms: binocular matching of out-of-phase [Gaussians, rectangles].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p394fig11.33 Kaufman stereogram: simultaneous fusion and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p395fig11.34 3D LAMINART vs 7 other rivalry models: stable vision and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p396fig11.35 Three properties of bipole boundary grouping in V2: boundaries oscillate with rivalry-inducing stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p397fig11.36 temporal dynamics of [rivalrous, coherent] boundary switching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p398fig11.37 Simulation of the no swap baseline condition (Logothetis, Leopold, Sheinberg 1996).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.38 Simulation of the swap condition of (Logothetis, Leopold, Sheinberg 1996).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.39 Simulation of the eye rivalry data of (Lee, Blake 1999).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p400fig11.40 How do ambiguous 2D shapes contextually define a 3D object form.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.41 3D LAMINART: [angle, disparity-gradient] cells learn 3D representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.42 hypothetical cortical hypercolumn: how [angle, disparity-gradient] cells may self-organize during development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.43 A pair of disparate images of a scene from the University of Tsukuba.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.44 3D LAMINART disparities [5, 6, 8, 10, 11, 14]: images of objects in common depth planes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p403fig11.45 SAR processing by multiple scales: reconstruction of a SAR image.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p405fig12.01 [What ventral, Where-How dorsal] cortical streams for [audition, vision].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p406fig12.02 Three S's of movement: Synergy formation, muscle Synchrony, volitional Speed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p407fig12.03 Motor cortical cells: vectors for [direction, length] of commanded movement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p409fig12.04 VITE simulations: difference vector emergent from network interactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.05 VITE: velocity profile invariance [short, long] movements for same GO signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.06 Monkeys transform movement: 2 -> 10 o'clock target, 50 or 100 msec after activation of 2 o'clock target.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p411fig12.07 VITE: higher peak velocity due to target switching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p411fig12.08 GO signals gate agonist-antagonist [difference, present position] vector processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p412fig12.09 Vector Associative Map: difference vector mismatch learning calibrates [target, present] position vectors.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p413fig12.10 VITE: cortical area [4,5] combine [trajectory, inflow] signals from [spinal cord, cerebellum] for [variable loads, obstacles].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p414fig12.11 [data, simulation]s from cortical areas 4 and 5 during a reach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p415fig12.12 [VITE, FLETE, cerebellar, opponent muscle] model for trajectory formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.13 DIRECT model: Endogenous Random Generator learns volitional reaches.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.14 DIRECT reaches [unconstrained, with TOOL, elbow@140°, blind].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p417fig12.15 From Seeing & Reaching (DIRECT) to Hearing & Speaking (DIVA): homologous circular reactions, [tool use, coarticulation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p418fig12.16 Anatomy of DIVA model processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p422fig12.19 ARTSTREAM: derive streams from [pitch, source direction].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.21 Pitch shifts with component shifts, pitch vs lowest harmonic number.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.22 Decomposition of a sound in terms of three of its harmonics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p426fig12.24 Spectrograms of -ba- and -pa- show the transient and sustained parts of their spectrograms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p428fig12.25 ARTSPEECH: auditory-articulatory feedback loop & imitative map, [auditory, motor] dimensionally consistent, motor theory of speech.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p430fig12.26 NormNet: speaker normalization via specializations of mechanisms for auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p431fig12.27 ARTSTREAM & NormNet strip maps: variants of occular dominance columns in visual cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p432fig12.28 SpaN: spatial representations of numerical quantities in the parietal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p433fig12.29 What stream: place-value [number map, language category]s; to Where stream: numerical strip maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.31 Working memory: temporal order STM is often imperfect, then stored in LTM.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.32 Free recall bowed serial position curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.33 Working memory models: item and order, or competitive queuing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p438fig12.34 LTM Invariance Principle: [STM, LTM] new words must not cause catastrophic forgetting of subwords.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p439fig12.35 Normalization Rule: total activity of working memory has upper bound independent of number of items.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p439fig12.36 [Item, Order] working memories: [content-addressable categories, temporal order, [excitatory, inhibitory] recurrence, rehearsal wave.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p440fig12.37 Normalization Rule: primacy bow as more items stored.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p441fig12.38 LTM Invariance Principle: new events do not change the relative activities of past event sequences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p442fig12.39 [LTM invariance, Normalization Rule] Shunt normalization -> STM bow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p442fig12.40 [LTM Invariance, normalization, STM steady attention]: only [primacy, bowed] gradients of activity can be stored.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p443fig12.41 Neurophysiology of sequential copying: [primacy gradient, self-inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p447fig12.45 LIST PARSE: order errors vs serial position with extended pauses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p448fig12.46 Masking Field working memory is a multiple-scale self-similar recurrent shunting on-center off-surround network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p449fig12.47 Masking Field self-similar [recurrent inhibitory, top-down excitatory] signals to the item chunk working memory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p452fig12.48 Perceptual integration of acoustic cues: [silence vs noise] durations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.49 ARTWORD: acoustic cues, phonetic [features, WM], Masking Field unitized lists, gain control.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.50 ARTWORD perception cycle: sequences-> chunks-> compete-> top-down expectations-> item working memory->  develops item-list resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p454fig12.51 Resonant transfer: as silence interval increases, a delayed additional item can facilitate perception of a longer list.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p455fig12.52 cARTWORD dynamics 1-2-3: resonant activity in item and feature layers corresponds to conscious speech percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.53 cARTWORD dynamics 1-silence-3: Gap in resonant activity of 1-silence-3 in [item, feature] layers corresponds to perceived silence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.54 cARTWORD dynamics: 1-noise-3: Resonance of 1-2-3 in [item, feature] layers  restores item 2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p457fig12.55 cARTWORD dynamics 1-noise-5: Figures 12.[54, 55] future context can disambiguate past noisy sequences that are otherwise identical.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p459fig12.56 Rank information on the position of an item in a list using numerical hypercolumns in the prefrontal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p460fig12.57 lisTELOS for saccades: prototype to [store, recall] other [cognitive, spatial, motor] information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p461fig12.58 lisTELOS shows [BG nigro-[thalamic, collicular], FEF, ITa, PFC, PNR-THAL, PPC, SEF, SC, V1, V4-ITp, Visual Cortex input] and [GABA].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p462fig12.59 TELOS: balancing reactive vs. planned movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p463fig12.60 Rank-related activity in PFC and SEF from two different experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p464fig12.61 SEF saccades microstimulating electrode: spatial gradient of habituation alters order, but not which, saccades are performed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p464fig12.62 The most habituated position is foveated last: because stimulation spreads in all directions, saccade trajectories tend to converge.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p465fig12.63 lisTELOS and data: microstimulation biases selection so saccade trajectories converge toward a single location in space.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p467fig12.64 Some of the auditory cortical regions that respond to sustained or transient sounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p468fig12.65 [PHONET, ARTPHONE] linguistic properties: creates rate-invariant representations for variable-rate speech, paradoxical VC-CV category boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.66 PHONET: relative duration of [consonant, vowel] pairs can [preserve, change] a percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.67 PHONET [transient, sustained] cells that respond to certain [consonant transient, sustained vowel] sounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p471fig12.68 Mismatch vs resonant fusion: effect of silence interval length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p473fig12.69 ART Matching Rule properties explain error rate and mean reaction time (RT) data from lexical decision experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p474fig12.70 macrocircuit model to explain lexical decision task data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p476fig12.71 Word frequency data model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p481fig13.01 Cognitive-Emotional-Motor (CogEM): macrocircuit of [function, anatomy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.03 CogEM: supported by anatomical connections [[sensory, orbitofrontal] cortices, amygdala].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.05 Classical conditioning: perhaps simplest kind of associative learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.06 Classical conditioning: inverted-U vs InterStimulus Interval (ISI).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.07 Paradigm of secondary conditioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.08 Blocking paradigm: cues lacking different consequences may fail to be attended.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.09 Equally salient cues can be conditioned in parallel to an emotional consequence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.10 Blocking: both [secondary, attenuation of] conditioning at zero ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p487fig13.11 CogEM : three main properties to explain how attentional blocking occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p488fig13.12 Motivational feedback and blocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p489fig13.13 CogEM and conditioning: positive ISI; inverted-U vs ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.14 Cognitive-Emotional circuit: for proper conditioning, sensory needs >= 2 processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p492fig13.16 Polyvalent CS sampling and US-activated nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p493fig13.17 Learning nonspecific arousal and CR read-out.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.18 Learning to control nonspecific arousal and read-out of the CR: two stages of CS.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.19 CogEM: secondary conditioning of [arousal, response], multiple [drive, input]s, motivational sets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p496fig13.20 A single avalanche sampling cell can learn an arbitrary space-time pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p497fig13.21 nonspecific arousal: primitive crayfish swimmerets, songbird pattern generator avalanche.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p499fig13.23 Self-organizing avalanches [instars filter, serial learning, outstars read-out], Serial list learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p500fig13.24 Primary [excitatory, inhibitory] conditioning using opponent processes and their antagonistic rebounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.25 Unbiased transducer in finite rate physical process: mass action by a chemical transmitter is the result.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.26 Transmitter y [accumulation, release]:  y restored < infinite rate, evolution has exploited this.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p502fig13.27 Transmitter minor mathematical miracle [accumulation, release]: S*y = S*A*B div (A + S) (gate, mass action).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p502fig13.28 Habituative transmitter gate: fast [increment, decrement]s of input lead to [overshoot, habituation, undershoot]s, Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p503fig13.29 ON response to phasic ON input has Weber Law properties due to the habituative transmitter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.30 OFF-rebound transient due to phasic input offset: arousal level sets ratio ON vs OFF rebounds, Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.31 Behavioral contrast rebounds: decrease [food-> negative Frustration, shock-> positive Relief] reinforcers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.32 Behavioral contrast: [response suppression, antagonist rebound] both calibrated by shock levels.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.33 Novelty reset- rebound to arousal onset: equilibrate to [I, J]; keep phasic input J fixed; interpret this equation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p506fig13.34 Novelty reset: rebound to arousal onset, reset of dipole field by unexpected event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p506fig13.35 Shock [cognitive, emotional] effects: [reinforcer, sensory cue, expectancy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p509fig13.36 Life-long learning: selective without [passive forgetting, associative saturation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.37 A disconfirmed expectation inhibits prior incentive, but is insufficient to prevent associative saturation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.38 Dissociation of LTM read-[out, in]: dendritic action potentials as teaching signals, early predictions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.39 Learn net dipole output pattern: [shunting competition, informational noise suppression] in affective gated dipoles, back-propagation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p512fig13.40 Conditioned excitor extinguishes: [learning, forgetting] phases, shock expectation disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.41 Conditioned inhibitor does not extinguish: [learn, forget] phases, same [CS, teacher] can be used.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.42 Conditioned excitor extinguishes when expectation of shock is disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.43 Conditioned excitor extinguishes: expectation that -no shock- follows CS2 is NOT disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p514fig13.44 Analog of the COgEM model maps of [object X, proto-self], assembly of second-order map.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p519fig14.01 Coronal sections of prefrontal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p524fig14.04 Basal ganglia circuit for dopaminergic Now Print signals from the substantia nigra pars compacta in response to unexpected rewards.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p530fig14.05 Visual [pop-out, search]-> reaction time experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.06 ARTSCENE: classification of scenic properties as texture categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.07 ARTSCENE voting achieves even better prediction of scene type.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p533fig14.09 ARTSCENE search [data, simulation]s for 6 pairs of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p540fig15.01 [Delay, trace conditioning] paradigms:  require a CS memory trace over the ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.03 Timed responses from adaptively timed conditioning: Weber laws, inverted U as a function of ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p542fig15.04 blinks of [nictitating membrane, eyelid] are adaptively timed: closure occurs at arrival of the US following the CS, obeys Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.05 Learning with two ISIs: each peak obeys Weber Law, strong evidence for spectral learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.06 Circuit between [dentate granule, CA1 hippocampal pyramid] cells seems to compute spectrally timed responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.07 Spectral timing: STM sensory representation-> Spectral activation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.08 Habituative transmitter gate: spectral activities-> sigmoid signals-> gated by habituative transmitters.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.09 Habituative transmitter gate: increases with accumulation, decreases from gated inactivation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p545fig15.10 A timed spectrum of gated sampling intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p545fig15.11  Associative learning, gated steepest descent learning: output from each population is a doubly gated signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p546fig15.12 Computer simulation of spectral learning: fastest with large sampling signals when the US occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p546fig15.13  Adaptive timing is a population property, random spectrum of rates achieves good collective timing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p547fig15.14 [Un, ]expected non-occurences of goal: a predictive failure leads to: Orienting Reactions, Emotional- Frustration,  Motor- Explorator.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p547fig15.15  Expected non-occurrence of goal: some rewards are reliable but delayed in time, do not lead to orienting reactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.17 The timing paradox: want [accurate timing, to inhibit exploratory behaviour throught ISI].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.18 Weber Law: reconciling accurate and distributed timing, different ISIs-  standard deviation = peak time, Weber law rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.19 Conditioning, Attention, and Timing circuit: Hippocampus spectrum-> Amgdala orienting system-> neocortex motivational attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p550fig15.20 Adaptively timed Long Term Depression between parallel fibres and Purkinje cells-> movement gains within learned time interval.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p551fig15.21 Cerebellum: important cells types and circuitry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p551fig15.22 Responses of a turtle retinal cone to brief flashes of light of increasing intensity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p552fig15.23 Cerebellar biochemistry: mGluR supports adaptively timed conditioning at cerebellar Purkinje cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p556fig15.24 Cerebellar cortex responses: [data, model] short latency responses after lesioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.25 Computer simulations of adaptively timed [LTD at Purkinje cells, activation of cereballar nuclear cells].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.26 Brain [region, process]s that contribute to autistic behavioral symptoms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.27 Spectrally timed SNc learning: brain [region, process]s release of dopaminergic signals, unexpected reinforcing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.28 Neurophysiological data and simulations of SNc responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.29 Excitatory pathways that support activation of the SNc by a US and the conditioning of a CS to the US.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.30 Inhibitory pathway: striosomal cells predict [timing, magnitude] of reward signal to cancel it.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.31 Expectation timing: timing spectrum, striosomal cells delayed transient signals, gate [learning, read-out].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.32 Inhibitory pathway expectation magnitude: is a negative feedback control system for learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.33 MOTIVATOR: thalamocortical loops through basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.34 Distinct basal ganglia zones for each loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p564fig15.35 GO signal to recurrent shunting OC-OS networks: control of the [fore, hind] limbs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p565fig15.36 (a) FOVEATE: control of saccadic eye movements within the peri-pontine reticular formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p566fig15.37 FOVEATE: steps in generation of a saccade.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p567fig15.38 Gated Pacemaker of [diurnal, nocturnal] circadian rythms: whether phasic light turns the pacemaker on or off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p568fig15.39 MOTIVATOR hypothalamic gated dipoles: inputs, [object, value, object-value] categories, reward expectation filter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p569fig15.40 GO and STOP movement signals: control by [direct, indirect] basal ganglia circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p573fig16.01 Hippocampal place cells: discovery from rat [experimental chamber, neurophysiological recordings].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p574fig16.02 Neurophysiological recordings of 18 different place cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p575fig16.03 Rat navigation: firing patterns of [hippocampal place, entrorhinal grid] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p578fig16.04 Cross-sections of the hippocampal regions and the inputs to them.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p580fig16.05 GridPlaceMap hierarchy of SOMs with identical equations: learns 2D [grid, place] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p581fig16.06 Trigonometry of spatial navigation: coactivation of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.07 Stripe cells multiple [orientation, phase, scale]s: directionally-sensitive ring attractors, velocity, distance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.08 Evidence for stripe-like cells: entorhinal cortex data, Band Cells position from grid cell oscillatory interference.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.09 GRIDSmap: stripe cells for rat trajectories, self-organizing map learned hexagonal grid cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.10 GRIDSmap embedded into hierarchy of SOMs: [angular head velocity, linear velocity] signals to place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.11 GRIDSmap learning of hexagonal grid fields, multiple phases per scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.12 Temporal development of grid fields: orientations rotate to align with each other.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.13 Hexagonal grid cell receptive fields: somewhat insensitive to [number, directional selectivities] of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.14 GRIDSmap: Superimposed firing of stripe cells supports learning hexagonal grid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.15 Why is a hexagonal grid favored: stripe cells at intervals of 45 degrees, GRIDSmap does not learn, oscillatory interference does.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.16 Grid-to-place SOM: formation of place cell fields via grid-to-place cell learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p587fig16.17 A refined analysis: SOM amplifies most frequent and energetic coactivations, stripe fields separated by [90°, 60°].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p588fig16.18 GridPlaceMap hierarchy of SOMs: coordinated learning of [grid, place, inomodal] cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p589fig16.19 How does the spatial scale increase along the MEC dorsoventral axis.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p590fig16.20 Dorsoventral gradient in the rate of synaptic integration of MEC layer II stellate cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p590fig16.21 Frequency of membrane potential oscillations in grid cells decreases along the dorsoventral gradient of the MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p591fig16.22 Dorsoventral [time constant, duration] gradients in AHP kinetics of MEC layer II stellate cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p591fig16.23 Spectral spacing model: map cells respond to stripe cell inputs of multiple scales, How do entorhinal cells solve the scale selection problem.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p592fig16.24 Parameter settings in the Spectral Spacing Model that were used in simulations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.25 Spectral Spacing Model equations for [STM, MTM, LTM].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.26 Gradient of grid spacing along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p594fig16.27 Gradient of field width along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p595fig16.28 Peak and mean rates at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.29 Subthreshold membrane mV oscillations: decreasing Hz at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.30 Spatial phases of learned grid and place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.31 Multimodal place cell firing in large spaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.32 Model fits data about grid cell development in juvenile rats:  grid [score increases, spacing flat].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.33 Model fits [place, grid, directional] cell data about grid cell development in juvenile rats:  [spatial information, inter-trial stability] vs postnatal day.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.34 spiking GridPlaceMap: generates theta-modulated place and grid cell firing, unlike the rate-based model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p599fig16.35 anatomically overlapping grid cell modules: effects of [different modules in one animal, DV location, response rate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p602fig16.37 Hippocampal inactivation by muscimol disrupts grid cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p603fig16.38 Role of hippocampal feedback in maintaining grid fields, muscimol inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p607fig16.40 Effects of medial septum (MS) inactivation on grid cells: data, simulations, gridness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p611fig16.41 back-propagating action potentials, recurrent inhibitory interneurons: control learning, regulate rythm- read-out is dissociated from read-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p612fig16.42 Macrocircuit of the main SOVEREIGN subsystems: visual, motor.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.44 SOVEREIGN[target position, difference] vectors, volitional GO computations] to control decision-making and action.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p614fig16.45 [distance, angle] computations learn dimensionally-consistent [visual, motor] information for [decision, action]s.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p615fig16.46 SOVEREIGN uses homologous processing stages to model the [What, Where] cortical streams, motivational mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p615fig16.47 SOVEREIGN: multiple parallel READ circuits, sensory-drive heterarchy amplifies motivationally favored option.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p616fig16.48 SOVEREIGN tests using virtual reality 3D rendering of a cross maze.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p616fig16.49 SOVEREIGN  animat converted inefficient exploration into an efficient direct learned path to the goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p617fig16.50 Spectral Spacing models of [perirhinal what, parahippocampal where] inputs, fused in the hippocampus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p627tbl17.01 Homologs between [reaction-diffusion, recurrent shunting cellular network] models of development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p628fig17.01 A hydra.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p628fig17.02 how different [cut, graft]s of the normal Hydra may [, not] lead to the growth of a new head.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p629fig17.03 How an initial morphogenetic gradient may be contrast enhanced to exceed the threshold for head formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p630fig17.04 Morphogenesis: use cellular models vs [chemical, fluid] reaction-diffusion models.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p631fig17.05 How a blastula develops into a gastrula.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p634fig17.06 How binary cells with a Gaussian distribution of output thresholds generates a sigmoidal population signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p057fig02.03 neuron basic [anatomy, physiology].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.26 drawing directly in color leads to colored surface representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p090fig03.03 Kanizsa square and reverse-contrast Kanizsa square precepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.11 Another example of neon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p124fig04.02 closed boundaries prevent brightness from flowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p156fig04.33 emergent boundary groupings can segregate textured regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p003fig01.03 Amodal completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#Early ARTMAP benchmark studies
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p167fig04.43 T-junction sensitivity: image, Bipole cells, boundary.png
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
#Instructions
#Preface
#Chapter 1 - Overview
#Chapter 2 - How a brain makes a mind
#Chapter 3 - How a brain sees: Constructing reality
#Chapter 4 - How a brain sees: Neural mechanisms
#Chapter 5 - Learning to attend, recognize, and predict the world
#Chapter 6 - Conscious seeing and invariant recognition
#Chapter 7 - How do we see a changing world?
#Chapter 8 - How we see and recognize object motion
#Chapter 9 - Target tracking, navigation, and decision-making
#Chapter 10 - Laminar computing by cerebral cortex
#Chapter 11 - How we see the world in depth
#Chapter 12 - From seeing and reaching to hearing and speaking
#Chapter 13 - From knowing to feeling
#Chapter 14 - How prefrontal cortex works
#Chapter 15 - Adaptively timed learning
#Chapter 16 - Learning maps to navigate space
#Chapter 17 - A universal development code
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p002fig01.01 Seeing an object vs knowing what it is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p002fig01.02 Dalmation in snow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p003fig01.03 Amodal completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p004fig01.04 Kanizsa stratification: transparency images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p008fig01.05 Noise-saturation dilemma: cell activity; current activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p009fig01.06 Primacy gradient of activity in a recurrent shunting OC-OS network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p011fig01.07 signal function determines how initial activity pattern is transformed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p012fig01.08 Sigmoidal signal: a hybrid of [same, slower, faster]-than-linear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p013fig01.09 sigmoid signal: quenching threshold; contrast enhancement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.10 Minimal adaptive prediction: blocking and unblocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.11 BU-TD mismatch -> orienting system -> nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p018fig01.12 Peak shift and behavioural contrast: prefer new experiences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p019fig01.13 Affective circuits are organized into opponent channels.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p023fig01.14 gated dipole opponent process: sustained on-response; transient off-response.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p024fig01.15 READ circuit: REcurrent Associative Dipole.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.16 Cognitive-Emotional-Motor (CogEM) model: sensory cortex, amygdala, PFC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.17 Sensory-drive heterarchy vs drive hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p026fig01.18 Inverted-U behaviour vs arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p027fig01.19 ventral What [percept, class], dorsal Where [spatial represent, action].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p029tbl01.01 complementary streams [visual boundary, what-where, perception & recognition, object tracking, motor target].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030tbl01.02 complementary streams: What- [rapid, stable] learn invariant object categories, Where- [labile spatial, action] actions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030fig01.20 neo-cortex 6 layers: same canonical laminar design cart [vision, speech, cognition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p032fig01.21 [Retina, LGNs, V[1,2,3,4], MT] to What & Where areas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p035fig01.22 Presentation [normal, silence, noise replaced].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p036fig01.23 working memory [longer list, bigger chunk]s.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p037fig01.24 sentence [learn, store, class] via 3 streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p039tbl01.03 [consciousness, movement] links: visual, auditory, emotional.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p042tbl01.04 six main resonances which support different kinds of conscious awareness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p051fig02.01 laterial inhibition: darker appears darker; lighter appears lighter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p052fig02.02 Adaptive Resonance reactivation: features bottom-up; categories top-down.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p057fig02.03 neuron basic [anatomy, physiology].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p058fig02.04 Learning a global arrow in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.05 Effects of intertrial and intratrial intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.06 Bow due to backward effect in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p060fig02.07 Error gradients depend on list position.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p061fig02.08 neural networks can learn forward and backward associations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p063fig02.09 Short Term Memory (STM): Additive Model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.10 STM Shunting Model, mass action in membrane equations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.11 MTM habituative transmitter gate; LTM gated steepest descent learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p065fig02.12 Three sources of neural network research: [binary, linear, nonlinear].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.13 Hartline: lateral inhibition in limulus retina of horseshoe crab.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.15 Noise-Saturation Dilemma: functional unit is a spatial activity pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.16 Noise-Saturation Dilemma:sensitivity to ratios of inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p072fig02.17 Vision: brightness constancy, contrast normalization.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p072fig02.18 Vision: brightness contrast, conserve a total quantity, total activity normalization.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.19 Computing in a bounded activity domain, Gedanken experiment.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.20 Shunting saturation occurs when inputs get larger to non-interacting cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.21 Shunting saturation: how shunting saturation turns on all of a cells excitable sites as input intensity increases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.22 Computing with patterns: how to compute the pattern-sensitive variable.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p074fig02.23 Shunting on-center off-surround network: no saturation! infinite dynamical range, conserve total activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.25 Weber law, adaptation, and shift property, convert to logarithmic coordinates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.26 Mudpuppy retina neurophysiology, adaptation- sensitivity shifts for different backgrounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.27 Mechanism: cooperative-competitive dynamics, subtractive lateral inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.28 Weber Law and adaptation level: hyperpolarization vs silent inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.29 Weber Law and adaptation level: adaptation level theory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.30 Noise suppression: attenuate zero spatial frequency patterns- no information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.31 Noise suppression -> pattern matching: mismatch (out of phase) suppressed, match (in phase) amplifies pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p079fig02.32 Substrate of resonance: match (in phase) of BU and TD input patterns amplifies matched pattern due to automatic gain control by shunting terms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.33 How do noise suppression signals arise: symmetry-breaking during morphogenesis, opposites attract rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.34 Symmetry-breaking: dynamics and anatomy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.35 Ratio contrast detector: reflectance processing, contrast normalization, discount illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.36 [Noise suppression, contour detection]: uniform patterns are suppressed, contrasts are selectively enhanced, contours are detected.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p085fig02.38 Modelling method and cycle, technological applications: at each stage [behavioural data, design principles, neural data, math model and analysis].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p089fig03.02 What do you think lies under the two grey disks (on a checkers board).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p090fig03.03 Kanizsa square and reverse-contrast Kanizsa square precepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p091fig03.04 blind spot and veins can occlude light to the retina.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p093fig03.06 Every line is an illusion!: boundary completion, surface filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p094fig03.07 Complementary properties of boundaries and surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p095fig03.08 Computer simulation of a Kanizsa square percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p095fig03.09 Simulation of a reverse-contrast Kanizsa square percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.10 The visual illusion of eon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.11 Another example of neon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p098fig03.12 Einstein's face: [edges, texture, shading] are overlaid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.13 Ehrenstein percept weakened as lines deviate from perpendicular.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.14 Perpendicular induction at line ends: [locally [,un], globally] preferred.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.15 orientations: [transient before, equilibrium after] choice.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p102fig03.16 Ts and Ls group together based on shared orientations, not identities.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p102fig03.17 Positions of squares give rise to a percept of three regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p103fig03.18 different spatial arrangements of inducers: emergent [horizontal, diagonal] groupings, but inducers have vertical orientations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p103fig03.19 [diagonal, perpendicular, parallel]: thats how multiple orientations can induce boundary completion of an object.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.20 Sean Williams: how boundaries can form.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.23 pointillist painting: Georges Seurat, A Sunday on la Grande Jatte.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p106fig03.24 Do these ideas work on hard problems: Synthetic Aperture Radar [discount illuminant, filling-in, boundaries].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.25 Matisse, The Roofs of Collioure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.26 drawing directly in color leads to colored surface representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.27 Matisse: Open Window, Collioure, [continuously, sparsely] indiced surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p109fig03.29 Chiaroscuro- Rembrandt self-portrait; Trompe l oeil- Graham Rust.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p109fig03.30 Jo Baer triptych: Primary Light Group [red, green, blue].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.31 Henry Hensche painting: The Bather, is suffused with light.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.32 Claude Monet painting: Poppies Near Argenteuil.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p112fig03.33 Boundary web gradient can cause self-luminosity, similar to watercolor illusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p112fig03.34 Examples of Ross Bleckner's self-luminous paintings.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p113fig03.35 Highest Luminance As White (HLAW) rule, Hans Wallach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p113fig03.36 Blurred Highest Luminance As White (BHLAW) rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p114fig03.37 Perceived reflectance vs cross-section of visual field: anchored brightness, self-luminous.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p114fig03.38 Color field painting: Jules Olitski, spray paintings of ambiguous depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p115fig03.39 Gene Davis paintings [full color, monochromatic]: percepts of grouping and relative depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p116fig03.40 Mona Lisa by Leonardo da Vinci: T-junctions and perspective cues give strong percept of depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p117fig03.41 Boundary contours and feature contours- no inhibition, feature signals survive and spread.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p117fig03.42 Two paintings by Frank Stella.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.43 Four paintings by Monet of the Rouen cathedral under different lighting conditions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.44 Rouen Cathedral at sunset (Monet 1892-1894): equiluminant, obscured and less depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.46 Rouen Cathedral full sunlight (Monet 1892-1894): T-junctions greater depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p123fig04.01 Combining stabilized images with filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p124fig04.02 closed boundaries prevent brightness from flowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p126fig04.03 Color constancy: compute ratios, discount the illuminant, compute lightness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p128fig04.04 reflectance changes at contours: fill-in illuminant-discounted colors.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p129fig04.05 reflectance changes at contours: color contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p129fig04.06 reflectance changes at contours: fill-in color; resolve uncertainty.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p130fig04.07 brightness constancy: boundary peaks spatially narrower than feature peaks.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p131fig04.08 brightness constancy: discount illuminant, ratio-sensitive feature contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p131fig04.09 Simulation of brightness contrast.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p132fig04.10 Simulation of brightness assimilation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p132fig04.11 Simulation of double step and COCE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p133fig04.12 Simulation of the 2D COCE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p133fig04.13 Contrast constancy, relative luminances can be reversed, discounting illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p134fig04.14 Experiments on filling-in: in-the-act; simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p138fig04.15 oriented filtering to grouping and boundary completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p139fig04.16 Simplest simple cell model: threshold linear, half-wave rectification.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p140fig04.17 Complex cells: pool like-oriented simple cells of opposite polarity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.18 Binocular Disparity to reconstruct depth from 2D retinal inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.19 Laminar cortical circuit for complex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p142fig04.20 [, reverse-]Glass patterns give rise to different boundary groupings.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p143fig04.21 Hierarchical resolution of uncertainty for a given field size.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p144fig04.22 End Gap and End Cut simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.23 A perceptual disaster in the feature contour system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.24 Hierarchical resolution of uncertainty- End Cuts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p146fig04.25 How are end cuts created: two stages of short-range competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p148fig04.26 End cut during neon color spreading via 2 stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p149fig04.27 Bipole cells boundary completion: long cooperation & short inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p150fig04.28 Bipole property: boundary completion via long-range cooperation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.29 bipole cells in cortical area V2: first neurophysiological evidence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.30 anatomy: horizontal connections in V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p152fig04.31 Bipoles through the ages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p156fig04.33 emergent boundary groupings can segregate textured regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p157fig04.34 texture: Boundary Contour System resolves errors of complex channels model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.35 Spatial impenetrability prevents grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.36 Graffiti art by Banksy: amodal boundary completion; spatial impenetrability.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p161fig04.37 Boundary Contour System model: analog-sensitive boundary completion Kanizsas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p162fig04.38 Cooperation and competition during grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p164fig04.40 Koffka-Benussi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p165fig04.41 Kanizsa-Minguzzi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p166fig04.42 Computer simulation of Kanizsa-Minguzzi ring percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p167fig04.43 T-junction sensitivity: image, Bipole cells, boundary.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.44 main [boundary, surface] formation stages: LGN-> V1-> V2-> V4.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.45 ON and OFF feature contours: filled-in regions when adjacent to boundary.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p170fig04.46 regions can fill-in feature contour inputs when [adjacent to, collinear with] boundary contour inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p170fig04.47 A double-opponent network processes output signals from FIDOs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.48 closed boundaries -> filling-in; open boundaries -> color spread.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.49 DaVinci stereopsis and occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p173fig04.50 closed boundary at prescribed depth: addition of [bi, mon]ocular boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p176fig04.53 OC-OS [within position, across depth]: brighter Kanizsas look closer.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p178fig04.54 figure-ground separation: bipole cooperation and competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p178fig04.55 Amodal completion of boundaries and surfaces in V2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p179fig04.56 Visible surface 3D perception: boundary enrichment, surface filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p182fig04.58 LAMINART explains many percepts of transparency.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.01 Learn many-to-one (compression, naming), one-to-many (expert knowledge) maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.02 Many-to-one map, two stage compression: [visual, auditory] categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.03 Many-to-one map: IF-THEN rules: [symptom, test, treatment]s; length of stay.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p189fig05.04 hippocampus & several brain regions [learn, remember] throughout life.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.05 LGN [ON, OFF] cells respond differently to [side, end]s of lines.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.07 detailed connections between [retinal ganglion cells, LGN, V1].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.08 LGN [activation, inhibition], with[, out] top-down feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p194fig05.09 [feature, boundary] contours from Ehrenstein disk stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p198fig05.10 Competitive learning and Self-Organized Maps (SOMs).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p199fig05.11 Instar learning: bottom-up adaptive filter for feature patterns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.12 Duality of [outstar, instar] networks.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.14 Outstar learning, both [in, de]creases for LTM to learn STM pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p201fig05.15 Spatial learning pattern, outstar learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.16 Geometry of choice and learning, classifying vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.17 Geometry of choice and learning, trains the closest LTM vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p205fig05.18 catastrophic forgetting due to [competition, associative] learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.20 [PN match, N200 mismatch] computationally complementary potentials.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p213fig05.22 If inputs incorrectly activate a category, how to correct the error.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p213fig05.23 A [category, symbol, other] cannot determine whether an error has occurred.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.24 Learning top-down expectations occurs during bottom-up learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.25 Error correction: [learn, compare] TD-BU inputs, Processing Negativity ERP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.26 Mismatch triggers nonspecific arousal, N200 ERP from orienting system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.27 Every event has [specific attentional cue, nonspecific orienting arousal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p220fig05.29 Vigilance [excitation: search better match, inhibition: resonance & learning].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.30 predictive error -> vigilance increase just enough -> minimax learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p224fig05.32 Learning the alphabet with two different levels of vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.33 Some early ARTMAP benchmark studies (no image - link instead).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.34 ARTMAP learned maps of natural terrains better than AI expert systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p233fig05.42 Evidence for the [gamma, beta] prediction in 3 parts of the brain.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p236fig05.43 nucleus basalis of Meynert releases ACh, reduces AHP, increases vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p242fig05.46 Distributed ARTMAP variants learn the 5-4 category structure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p245fig05.47 [long-range excitatory, short-range disynaptic inhibitory] connections realize the bipole grouping law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p252fig06.01 surface-shroud resonance forms as objects bid for spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p254fig06.03 ARTSCAN Search model learns to recognize and name invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p255fig06.04 The ARTSCAN Search for a desired target object in a scene: Wheres Waldo.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p257fig06.05 Spatial attention flows along object boundaries: Macaque V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.06 Neurophysiological data & simulation: attention can flow along a curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.07 Top-down attentional spotlight becomes a shroud.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p259fig06.08 dARTSCN spatial attention hierarchy [Fast Where, Slow What] stream.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p260fig06.09 Crowding: visible objects & confused recognition, increased flanker spacing at higher eccentricity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p260fig06.10 cortical magnification transforms coordinates: artesian (retina) to log polar (V1).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p261fig06.11 Crowding: visible objects and confused recognition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p261fig06.12 A more serial search is needed due to overlapping conjunctions of features.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p265fig06.13 basal ganglia gate perceptual, cognitive, emotional, etc through parallel loops.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p267fig06.14 Perceptual consistency and figure-ground separation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p268fig06.15 saccades within an object: figure-ground outputs control eye movements via V3AA.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p270fig06.16 Predictive remapping of eye movements, from V3A to LIP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p271fig06.17 Persistent activity in IT to [view, position, size]-invariant category learning by positional ARTSCAN.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.18 pARTSCAN: positionally-invariant object learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.19 persistent activity needed to learn positionally-invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p273fig06.20 pARTSCAN simulation of Li & DiCarlo IT cell swapping data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.21 pARTSCAN [position invariance, selectivity] trade-off of Zoccolan etal 2007.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.22 pARTSCAN: IT cortex processes image morphs with high vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.23 IT responses to image morphs, data vs model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.24 Sterogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p276fig06.25 saccades: predictive gain fields [binocular fusion, filling-in of surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p277fig06.26 Predictive remapping maintains binocular boundary fusion as eyes move.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.27 knowing vs seeing resonances: What [knowing, feature-prototype], Where [seeing, surface-shroud].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p283fig07.01 Boundary competition: spatial habituative gates, orientation gated dipole, bipole grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p284fig07.02 Persistence decreases with flash illuminance & duration [data, simulations].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p285fig07.03 Persistence decrease: rebound to input offset inhibits bipole cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.04 Illusory contours persist longer than real contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.05 Illusory contours inhibited by OFF cell rebounds, propagate to center.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.06 Persistence: [less, more] as adaptation orientation [same, orthogonal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.07 Persistence increases with distance, due to weaker spatial competition in hypercomplex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p290fig08.01 Motion pools contrast-sensitive information moving in the same direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p291fig08.02 Complex cells respond to motion: opposite [direction, contrast polarities].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p292fig08.03 Visual aftereffects: [form- MacKay 90 degree, motion- waterfall  180].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p293fig08.04 Local vs overall motion: aperture problem of EVERY neurons receptive field.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p295fig08.05 sparse feature tracking signals [capture ambiguous, determine perceived] motion direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.06 Simplest example of apparent motion: two dots turning on and off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.07 continuous motion illusions: [Beta with, Phi without] percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.08 Delta motion when [luminance, contrast] of flash 2 is larger than flash 1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.09 motion in opposite directions perceived when 2 later flashes on either side of 1st flash.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.10 motion speed-up perceived when flash duration decreases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.11 illusory contours: double illusion in V1-V2, motion V2-MT interaction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.12 Single flash: Gaussian receptive fields, recurrent OC-OS winner-take-all.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.13 Nothing moves: [single flash, exponential decay], Gaussian peak fixed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.14 Visual inertia: flash decay after the flash shuts off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p301fig08.15 two flashes: cell activation by first waning while second one is waxing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p301fig08.16 sum Gaussian flash activity profiles: [waning 1st, waxing 2nd] -> travelling wave.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p302fig08.17 maximum long-rang apparent motion: Gaussian kernel spans successive flashes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p302fig08.18 G-wave theorem 1: wave moves continuously IFF L <= 2*K.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.19 No motion vs motion at multiple scales: flash distance L, Gaussian width K.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.20 G-wave theorem 2: [speed-up, scale] independent of [distance, scale size].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.21 Equal half-time property: multiple scales generate motion percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.22 Korte Laws: ISIs in the hundreds of milliseconds can cause apparent motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p305fig08.23 Ternus motion: ISI [small- stationary, intermediate- element, larger- group].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p305fig08.24 Reverse-contrast Ternus motion: ISI [small- stationarity, intermediate- group (not element!), larger- group] motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p306fig08.25 Motion BCS model [explain, simulate]s long-range motion percepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p306fig08.26 3D FORMOTION model: track objects moving in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p307fig08.27 Ternus motion: [element- weak, group- strong] transients, element [visual persistence, perceived stationarity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p308fig08.28 Ternus group motion: Gaussian filter of 3 flashes forms one global maximum.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p310fig08.29 when individual component motions combine, their perceived direction & speed changes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.30 3D FORMOTION model: feature tracking [get directional, inhibit inconsistent] signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.31 Motion BCS stages: locally ambiguous motion signals -> globally coherent percept, solving the aperture problem.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p312fig08.32 Schematic of motion filtering circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p312fig08.33 Processing motion signals by a population of speed-tuned neurons.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p314fig08.34 VISTARS navigation model: FORMOTION front end for navigational circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p316fig08.36 Motion capture by directional grouping feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p317fig08.37 Motion capture by directional grouping feedback: [short, long]-range filters, transient cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p319fig08.38 Solving the aperture problem takes time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p320fig08.39 Simulation of the barberpole illusion direction field at two times.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p321fig08.40 [, in]visible occluders [do, not] capture boundaries they share with moving edges.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p322fig08.41 motion transparency: asymmetry [near, far], competing opposite directions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p323fig08.42 Chopsticks: motion separation in depth via [, in]visible occluders [display, percept].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p324fig08.43 ambiguous X-junction motion: MT-MST directional grouping bridges the ambiguous position.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p325fig08.44 The role of MT-V1 feedback:  [motion-form feedback, bipole boundary completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p325fig08.45 Closing formotion feedback loop [MT, MST]-to-V1-to-V2-to-[MT, MST].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p326fig08.46 How do we perceive relative motion of object parts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p327fig08.47 Two classical examples of part motion: Symmetrically moving inducers; Duncker wheel.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p328fig08.48 vector decomposition: (retinal - common = part) motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p328fig08.49 What is the mechanism of vector decomposition, prediction: directional peak shift.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p329fig08.50 How is common motion direction computed?  retinal motion->  bipole grouping (form stream)->  V2-MT formotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p329fig08.51 Large and small scale boundaries differentially form illusory contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.52 Correct motion directions after the peak shift top-down expectation acts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.53 Simulation of the various directional signals of the left dot through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.54 Motion directions of a single dot moving slowly along a cycloid curve through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.55 Duncker Wheel, large: stable rightward motion at the center captures motion at the rim.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p332fig08.56 Duncker Wheel, small: wheel motion as seen when directions are collapsed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p332fig08.57 MODE (MOtion DEcision) model: Motion BCS -> saccadic target selection -> basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p333fig08.58 LIP responses during RT task correct trials: coherence and [activation, inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.59 LIP responses for FD task: predictiveness decreases with increasing coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.60 [RT, FD] task behavioral data: more coherence in the motion causes more accurate decisions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.61 RT task behavioural data: reach time (ms) vs % coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.62 LIP encodes not only where, but also when, to move the eyes - No Bayes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p338fig09.01 optic flow through brain regions: moving observer [navigate, track] moving object.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p338fig09.02 Heading (focus of velocity field) from optic flow: humans accurate +- 1 to 2 degrees.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p339fig09.03 Heading with [body move, eye rotate, combined] -> optic flow [expand, translate, rotate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p339fig09.04 How can translation flow (eye rotation) be subtracted from spiral flow to recover the expansion flow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.05 efference copy command: may use outflow movement commands to eye muscles.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.06 Corollary discharges from outflow movement commands that move muscles.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.07 Log polar remapping of optic flow:  [expansion, circular] motion maps to single direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p341fig09.08 optic flows [retina, V1, MT, MSTd, parietal cortex], V1 log polar mapping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p341fig09.09 MSTd cells are sensitive to [spiral, rotation, expansion] motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.10 Retina -> log polar -> MSTd cell, heading eccentricity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.11 importance of efference copy in real movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.12 two retinal views of the Simpsons: [separate, recognize] overlapping figures.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.13 How do our brains figure out which views belong to which pear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p344fig09.14 Heading sensitivity unimpaired: MT tuning width 38°, MSTd spiral tuning 61°.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p345fig09.15 MT double opponent directional fields: relative motions [objects, backgrounds].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p346fig09.16 macrocircuit of 13 brain regions used to move the eyes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p347fig09.17 leftward eye movement model: retina-> MT-> MST[v,d]-> pursuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p347fig09.18 MST[v,d] circuits enable predictive target tracking by the pursuit system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p348fig09.19 MSTv cells: target speed on retina, background speed on retina, pursuit speed command.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.20 Steering from optic flow: goals are attractors, obstacles are repellers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.21 Steering dynamics goal approach: [obstacle, goal, heading] -> steering.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.22 negative Gaussian of an obstacle: avoid obstacle without losing sight of goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.23 Unidirectional transient cells: [lead, trail]ing boundaries, driving video.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.24 Directional transient cells respond most to motion in their preferred directions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.25 M+ computes global motion estimate from noisy local motion estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p352fig09.26 heading direction final stage: beautiful optic flow, accuracy matches humans.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p355fig10.02 seeing vs knowing distinction is difficult because they interact so strongly.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.05 Activation of V1 by direct excitatory signals from LGN to layer 4 of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.06 Why another layer 6-to-4 signal: on-center off-surround.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.09 Grouping starts in layer 2-3: long-range horizontal excitation, short-range inhibition of target pyramidal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p361fig10.10 Bipole property controls perceptual grouping: inputs [excitatory sum, inhibitory normalize].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p362fig10.11 Final grouping: folded feedback, strongest enhanced on-center, weaker suppressed off-surround, interlaminar functional columns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p363fig10.12 V2 repeats V1 circuitry at larger spatial scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.14 Explanation: grouping and attention share the same modulatory decision circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.15 Attention protects target from masking stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.16 Flankers can enhance or suppress targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.17 Attention has greater effect on low contrast targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.18 Texture reduces response to a bar: [iso-orientation, perpendicular] suppression.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p369fig10.19 Unconscious learning of motion direction, without [extra-foveal attention, awareness] of stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p371fig11.01 FACADE theory explains how the 3D boundaries and surfaces are formed to see the world in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p372fig11.02 3D surface filling-in of [lightness, color, depth] by a single process: FACADE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p373fig11.03 Both [contrast-specific binocular fusion, contrast-invariant boundary perception] are needed to see the world in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p374fig11.04 Three processing stages of [monocular simple, complex] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p374fig11.05 Contrast constraint on binocular fusion: only contrasts which are derived from the same objects in space are binoculary matched.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.06 Binocular fusion by obligate cells in V1-3B when =[left,right] contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.08 Correspondance problem: How does the brain inhibit false matches. contrast constraint not enough.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.09 V2 disparity filter solves correspondence problem: false matches suppressed by line-of-sight inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p377fig11.11 DaVinci stereopsis: monocular information and depth percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p378fig11.12 3D LAMINART: V2 monocular+binocular line of sight inputs -> depth perception.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p381fig11.15 DaVinci stereopsis variant of (Gillam, Blackburn, Nakayama 1999): same mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p382fig11.16 DaVinci stereopsis of [3 narrow, one thick] rectangles: same explanation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p383fig11.17 Venetian blind effect: [left, right] eye matching bars.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p384fig11.18 Venetian blind effect: Surface[, -to-boundary] surface contour signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p385fig11.19 Dichoptic masking: [left, right] images have sufficiently different contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p385fig11.20 Dichoptic masking, Panum's limiting case: simplified version of Venetian blind effect.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p386fig11.21 Craik-O'Brien-Cornsweet Effect: 2D surface at a very near depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p387fig11.22 Julesz stereogram: boundaries with[out, ] surface contour feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p388fig11.23 Sparse stereogram, large regions of ambiguous white: correct surface in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p388fig11.24 depth-ambiguous feature contours: boundary groups lift to correct surface in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p389fig11.25 Boundaries: not just edge detectors, or a shaded ellipse would look [flat, uniformly gray].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p390fig11.26 Multiple-scale depth-selective groupings determine perceived depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p391fig11.27 Multiple-scale grouping and size-disparity correlation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p391fig11.28 Ocular dominance columns, LGN mappings into layer 4C of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p392fig11.29 3D vision figure-ground separation: multiple-scale, depth-selective boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p392fig11.30 How multiple scales vote for multiple depths, scale-to-depth and depth-to-scale maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.31 LIGHTSHAFT model: determining depth-from-texture percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.32 Kulikowski stereograms: binocular matching of out-of-phase [Gaussians, rectangles].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p394fig11.33 Kaufman stereogram: simultaneous fusion and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p395fig11.34 3D LAMINART vs 7 other rivalry models: stable vision and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p396fig11.35 Three properties of bipole boundary grouping in V2: boundaries oscillate with rivalry-inducing stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p397fig11.36 temporal dynamics of [rivalrous, coherent] boundary switching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p398fig11.37 Simulation of the no swap baseline condition (Logothetis, Leopold, Sheinberg 1996).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.38 Simulation of the swap condition of (Logothetis, Leopold, Sheinberg 1996).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.39 Simulation of the eye rivalry data of (Lee, Blake 1999).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p400fig11.40 How do ambiguous 2D shapes contextually define a 3D object form.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.41 3D LAMINART: [angle, disparity-gradient] cells learn 3D representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.42 hypothetical cortical hypercolumn: how [angle, disparity-gradient] cells may self-organize during development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.43 A pair of disparate images of a scene from the University of Tsukuba.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.44 3D LAMINART disparities [5, 6, 8, 10, 11, 14]: images of objects in common depth planes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p403fig11.45 SAR processing by multiple scales: reconstruction of a SAR image.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p405fig12.01 [What ventral, Where-How dorsal] cortical streams for [audition, vision].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p406fig12.02 Three S's of movement: Synergy formation, muscle Synchrony, volitional Speed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p407fig12.03 Motor cortical cells: vectors for [direction, length] of commanded movement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p409fig12.04 VITE simulations: difference vector emergent from network interactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.05 VITE: velocity profile invariance [short, long] movements for same GO signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.06 Monkeys transform movement: 2 -> 10 o'clock target, 50 or 100 msec after activation of 2 o'clock target.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p411fig12.07 VITE: higher peak velocity due to target switching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p411fig12.08 GO signals gate agonist-antagonist [difference, present position] vector processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p412fig12.09 Vector Associative Map: difference vector mismatch learning calibrates [target, present] position vectors.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p413fig12.10 VITE: cortical area [4,5] combine [trajectory, inflow] signals from [spinal cord, cerebellum] for [variable loads, obstacles].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p414fig12.11 [data, simulation]s from cortical areas 4 and 5 during a reach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p415fig12.12 [VITE, FLETE, cerebellar, opponent muscle] model for trajectory formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.13 DIRECT model: Endogenous Random Generator learns volitional reaches.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.14 DIRECT reaches [unconstrained, with TOOL, elbow@140°, blind].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p417fig12.15 From Seeing & Reaching (DIRECT) to Hearing & Speaking (DIVA): homologous circular reactions, [tool use, coarticulation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p418fig12.16 Anatomy of DIVA model processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p422fig12.19 ARTSTREAM: derive streams from [pitch, source direction].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.21 Pitch shifts with component shifts, pitch vs lowest harmonic number.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.22 Decomposition of a sound in terms of three of its harmonics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p426fig12.24 Spectrograms of -ba- and -pa- show the transient and sustained parts of their spectrograms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p428fig12.25 ARTSPEECH: auditory-articulatory feedback loop & imitative map, [auditory, motor] dimensionally consistent, motor theory of speech.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p430fig12.26 NormNet: speaker normalization via specializations of mechanisms for auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p431fig12.27 ARTSTREAM & NormNet strip maps: variants of occular dominance columns in visual cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p432fig12.28 SpaN: spatial representations of numerical quantities in the parietal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p433fig12.29 What stream: place-value [number map, language category]s; to Where stream: numerical strip maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.31 Working memory: temporal order STM is often imperfect, then stored in LTM.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.32 Free recall bowed serial position curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.33 Working memory models: item and order, or competitive queuing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p438fig12.34 LTM Invariance Principle: [STM, LTM] new words must not cause catastrophic forgetting of subwords.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p439fig12.35 Normalization Rule: total activity of working memory has upper bound independent of number of items.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p439fig12.36 [Item, Order] working memories: [content-addressable categories, temporal order, [excitatory, inhibitory] recurrence, rehearsal wave.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p440fig12.37 Normalization Rule: primacy bow as more items stored.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p441fig12.38 LTM Invariance Principle: new events do not change the relative activities of past event sequences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p442fig12.39 [LTM invariance, Normalization Rule] Shunt normalization -> STM bow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p442fig12.40 [LTM Invariance, normalization, STM steady attention]: only [primacy, bowed] gradients of activity can be stored.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p443fig12.41 Neurophysiology of sequential copying: [primacy gradient, self-inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p447fig12.45 LIST PARSE: order errors vs serial position with extended pauses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p448fig12.46 Masking Field working memory is a multiple-scale self-similar recurrent shunting on-center off-surround network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p449fig12.47 Masking Field self-similar [recurrent inhibitory, top-down excitatory] signals to the item chunk working memory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p452fig12.48 Perceptual integration of acoustic cues: [silence vs noise] durations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.49 ARTWORD: acoustic cues, phonetic [features, WM], Masking Field unitized lists, gain control.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.50 ARTWORD perception cycle: sequences-> chunks-> compete-> top-down expectations-> item working memory->  develops item-list resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p454fig12.51 Resonant transfer: as silence interval increases, a delayed additional item can facilitate perception of a longer list.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p455fig12.52 cARTWORD dynamics 1-2-3: resonant activity in item and feature layers corresponds to conscious speech percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.53 cARTWORD dynamics 1-silence-3: Gap in resonant activity of 1-silence-3 in [item, feature] layers corresponds to perceived silence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.54 cARTWORD dynamics: 1-noise-3: Resonance of 1-2-3 in [item, feature] layers  restores item 2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p457fig12.55 cARTWORD dynamics 1-noise-5: Figures 12.[54, 55] future context can disambiguate past noisy sequences that are otherwise identical.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p459fig12.56 Rank information on the position of an item in a list using numerical hypercolumns in the prefrontal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p460fig12.57 lisTELOS for saccades: prototype to [store, recall] other [cognitive, spatial, motor] information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p461fig12.58 lisTELOS shows [BG nigro-[thalamic, collicular], FEF, ITa, PFC, PNR-THAL, PPC, SEF, SC, V1, V4-ITp, Visual Cortex input] and [GABA].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p462fig12.59 TELOS: balancing reactive vs. planned movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p463fig12.60 Rank-related activity in PFC and SEF from two different experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p464fig12.61 SEF saccades microstimulating electrode: spatial gradient of habituation alters order, but not which, saccades are performed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p464fig12.62 The most habituated position is foveated last: because stimulation spreads in all directions, saccade trajectories tend to converge.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p465fig12.63 lisTELOS and data: microstimulation biases selection so saccade trajectories converge toward a single location in space.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p467fig12.64 Some of the auditory cortical regions that respond to sustained or transient sounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p468fig12.65 [PHONET, ARTPHONE] linguistic properties: creates rate-invariant representations for variable-rate speech, paradoxical VC-CV category boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.66 PHONET: relative duration of [consonant, vowel] pairs can [preserve, change] a percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.67 PHONET [transient, sustained] cells that respond to certain [consonant transient, sustained vowel] sounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p471fig12.68 Mismatch vs resonant fusion: effect of silence interval length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p473fig12.69 ART Matching Rule properties explain error rate and mean reaction time (RT) data from lexical decision experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p474fig12.70 macrocircuit model to explain lexical decision task data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p476fig12.71 Word frequency data model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p481fig13.01 Cognitive-Emotional-Motor (CogEM): macrocircuit of [function, anatomy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.03 CogEM: supported by anatomical connections [[sensory, orbitofrontal] cortices, amygdala].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.05 Classical conditioning: perhaps simplest kind of associative learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.06 Classical conditioning: inverted-U vs InterStimulus Interval (ISI).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.07 Paradigm of secondary conditioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.08 Blocking paradigm: cues lacking different consequences may fail to be attended.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.09 Equally salient cues can be conditioned in parallel to an emotional consequence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.10 Blocking: both [secondary, attenuation of] conditioning at zero ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p487fig13.11 CogEM : three main properties to explain how attentional blocking occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p488fig13.12 Motivational feedback and blocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p489fig13.13 CogEM and conditioning: positive ISI; inverted-U vs ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.14 Cognitive-Emotional circuit: for proper conditioning, sensory needs >= 2 processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p492fig13.16 Polyvalent CS sampling and US-activated nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p493fig13.17 Learning nonspecific arousal and CR read-out.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.18 Learning to control nonspecific arousal and read-out of the CR: two stages of CS.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.19 CogEM: secondary conditioning of [arousal, response], multiple [drive, input]s, motivational sets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p496fig13.20 A single avalanche sampling cell can learn an arbitrary space-time pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p497fig13.21 nonspecific arousal: primitive crayfish swimmerets, songbird pattern generator avalanche.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p499fig13.23 Self-organizing avalanches [instars filter, serial learning, outstars read-out], Serial list learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p500fig13.24 Primary [excitatory, inhibitory] conditioning using opponent processes and their antagonistic rebounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.25 Unbiased transducer in finite rate physical process: mass action by a chemical transmitter is the result.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.26 Transmitter y [accumulation, release]:  y restored < infinite rate, evolution has exploited this.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p502fig13.27 Transmitter minor mathematical miracle [accumulation, release]: S*y = S*A*B div (A + S) (gate, mass action).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p502fig13.28 Habituative transmitter gate: fast [increment, decrement]s of input lead to [overshoot, habituation, undershoot]s, Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p503fig13.29 ON response to phasic ON input has Weber Law properties due to the habituative transmitter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.30 OFF-rebound transient due to phasic input offset: arousal level sets ratio ON vs OFF rebounds, Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.31 Behavioral contrast rebounds: decrease [food-> negative Frustration, shock-> positive Relief] reinforcers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.32 Behavioral contrast: [response suppression, antagonist rebound] both calibrated by shock levels.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.33 Novelty reset- rebound to arousal onset: equilibrate to [I, J]; keep phasic input J fixed; interpret this equation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p506fig13.34 Novelty reset: rebound to arousal onset, reset of dipole field by unexpected event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p506fig13.35 Shock [cognitive, emotional] effects: [reinforcer, sensory cue, expectancy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p509fig13.36 Life-long learning: selective without [passive forgetting, associative saturation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.37 A disconfirmed expectation inhibits prior incentive, but is insufficient to prevent associative saturation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.38 Dissociation of LTM read-[out, in]: dendritic action potentials as teaching signals, early predictions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.39 Learn net dipole output pattern: [shunting competition, informational noise suppression] in affective gated dipoles, back-propagation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p512fig13.40 Conditioned excitor extinguishes: [learning, forgetting] phases, shock expectation disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.41 Conditioned inhibitor does not extinguish: [learn, forget] phases, same [CS, teacher] can be used.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.42 Conditioned excitor extinguishes when expectation of shock is disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.43 Conditioned excitor extinguishes: expectation that -no shock- follows CS2 is NOT disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p514fig13.44 Analog of the COgEM model maps of [object X, proto-self], assembly of second-order map.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p519fig14.01 Coronal sections of prefrontal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p524fig14.04 Basal ganglia circuit for dopaminergic Now Print signals from the substantia nigra pars compacta in response to unexpected rewards.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p530fig14.05 Visual [pop-out, search]-> reaction time experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.06 ARTSCENE: classification of scenic properties as texture categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.07 ARTSCENE voting achieves even better prediction of scene type.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p533fig14.09 ARTSCENE search [data, simulation]s for 6 pairs of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p540fig15.01 [Delay, trace conditioning] paradigms:  require a CS memory trace over the ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.03 Timed responses from adaptively timed conditioning: Weber laws, inverted U as a function of ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p542fig15.04 blinks of [nictitating membrane, eyelid] are adaptively timed: closure occurs at arrival of the US following the CS, obeys Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.05 Learning with two ISIs: each peak obeys Weber Law, strong evidence for spectral learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.06 Circuit between [dentate granule, CA1 hippocampal pyramid] cells seems to compute spectrally timed responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.07 Spectral timing: STM sensory representation-> Spectral activation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.08 Habituative transmitter gate: spectral activities-> sigmoid signals-> gated by habituative transmitters.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.09 Habituative transmitter gate: increases with accumulation, decreases from gated inactivation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p545fig15.10 A timed spectrum of gated sampling intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p545fig15.11  Associative learning, gated steepest descent learning: output from each population is a doubly gated signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p546fig15.12 Computer simulation of spectral learning: fastest with large sampling signals when the US occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p546fig15.13  Adaptive timing is a population property, random spectrum of rates achieves good collective timing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p547fig15.14 [Un, ]expected non-occurences of goal: a predictive failure leads to: Orienting Reactions, Emotional- Frustration,  Motor- Explorator.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p547fig15.15  Expected non-occurrence of goal: some rewards are reliable but delayed in time, do not lead to orienting reactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.17 The timing paradox: want [accurate timing, to inhibit exploratory behaviour throught ISI].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.18 Weber Law: reconciling accurate and distributed timing, different ISIs-  standard deviation = peak time, Weber law rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.19 Conditioning, Attention, and Timing circuit: Hippocampus spectrum-> Amgdala orienting system-> neocortex motivational attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p550fig15.20 Adaptively timed Long Term Depression between parallel fibres and Purkinje cells-> movement gains within learned time interval.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p551fig15.21 Cerebellum: important cells types and circuitry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p551fig15.22 Responses of a turtle retinal cone to brief flashes of light of increasing intensity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p552fig15.23 Cerebellar biochemistry: mGluR supports adaptively timed conditioning at cerebellar Purkinje cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p556fig15.24 Cerebellar cortex responses: [data, model] short latency responses after lesioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.25 Computer simulations of adaptively timed [LTD at Purkinje cells, activation of cereballar nuclear cells].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.26 Brain [region, process]s that contribute to autistic behavioral symptoms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.27 Spectrally timed SNc learning: brain [region, process]s release of dopaminergic signals, unexpected reinforcing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.28 Neurophysiological data and simulations of SNc responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.29 Excitatory pathways that support activation of the SNc by a US and the conditioning of a CS to the US.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.30 Inhibitory pathway: striosomal cells predict [timing, magnitude] of reward signal to cancel it.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.31 Expectation timing: timing spectrum, striosomal cells delayed transient signals, gate [learning, read-out].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.32 Inhibitory pathway expectation magnitude: is a negative feedback control system for learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.33 MOTIVATOR: thalamocortical loops through basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.34 Distinct basal ganglia zones for each loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p564fig15.35 GO signal to recurrent shunting OC-OS networks: control of the [fore, hind] limbs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p565fig15.36 (a) FOVEATE: control of saccadic eye movements within the peri-pontine reticular formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p566fig15.37 FOVEATE: steps in generation of a saccade.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p567fig15.38 Gated Pacemaker of [diurnal, nocturnal] circadian rythms: whether phasic light turns the pacemaker on or off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p568fig15.39 MOTIVATOR hypothalamic gated dipoles: inputs, [object, value, object-value] categories, reward expectation filter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p569fig15.40 GO and STOP movement signals: control by [direct, indirect] basal ganglia circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p573fig16.01 Hippocampal place cells: discovery from rat [experimental chamber, neurophysiological recordings].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p574fig16.02 Neurophysiological recordings of 18 different place cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p575fig16.03 Rat navigation: firing patterns of [hippocampal place, entrorhinal grid] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p578fig16.04 Cross-sections of the hippocampal regions and the inputs to them.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p580fig16.05 GridPlaceMap hierarchy of SOMs with identical equations: learns 2D [grid, place] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p581fig16.06 Trigonometry of spatial navigation: coactivation of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.07 Stripe cells multiple [orientation, phase, scale]s: directionally-sensitive ring attractors, velocity, distance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.08 Evidence for stripe-like cells: entorhinal cortex data, Band Cells position from grid cell oscillatory interference.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.09 GRIDSmap: stripe cells for rat trajectories, self-organizing map learned hexagonal grid cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.10 GRIDSmap embedded into hierarchy of SOMs: [angular head velocity, linear velocity] signals to place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.11 GRIDSmap learning of hexagonal grid fields, multiple phases per scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.12 Temporal development of grid fields: orientations rotate to align with each other.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.13 Hexagonal grid cell receptive fields: somewhat insensitive to [number, directional selectivities] of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.14 GRIDSmap: Superimposed firing of stripe cells supports learning hexagonal grid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.15 Why is a hexagonal grid favored: stripe cells at intervals of 45 degrees, GRIDSmap does not learn, oscillatory interference does.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.16 Grid-to-place SOM: formation of place cell fields via grid-to-place cell learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p587fig16.17 A refined analysis: SOM amplifies most frequent and energetic coactivations, stripe fields separated by [90°, 60°].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p588fig16.18 GridPlaceMap hierarchy of SOMs: coordinated learning of [grid, place, inomodal] cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p589fig16.19 How does the spatial scale increase along the MEC dorsoventral axis.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p590fig16.20 Dorsoventral gradient in the rate of synaptic integration of MEC layer II stellate cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p590fig16.21 Frequency of membrane potential oscillations in grid cells decreases along the dorsoventral gradient of the MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p591fig16.22 Dorsoventral [time constant, duration] gradients in AHP kinetics of MEC layer II stellate cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p591fig16.23 Spectral spacing model: map cells respond to stripe cell inputs of multiple scales, How do entorhinal cells solve the scale selection problem.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p592fig16.24 Parameter settings in the Spectral Spacing Model that were used in simulations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.25 Spectral Spacing Model equations for [STM, MTM, LTM].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.26 Gradient of grid spacing along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p594fig16.27 Gradient of field width along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p595fig16.28 Peak and mean rates at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.29 Subthreshold membrane mV oscillations: decreasing Hz at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.30 Spatial phases of learned grid and place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.31 Multimodal place cell firing in large spaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.32 Model fits data about grid cell development in juvenile rats:  grid [score increases, spacing flat].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.33 Model fits [place, grid, directional] cell data about grid cell development in juvenile rats:  [spatial information, inter-trial stability] vs postnatal day.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.34 spiking GridPlaceMap: generates theta-modulated place and grid cell firing, unlike the rate-based model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p599fig16.35 anatomically overlapping grid cell modules: effects of [different modules in one animal, DV location, response rate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p602fig16.37 Hippocampal inactivation by muscimol disrupts grid cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p603fig16.38 Role of hippocampal feedback in maintaining grid fields, muscimol inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p607fig16.40 Effects of medial septum (MS) inactivation on grid cells: data, simulations, gridness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p611fig16.41 back-propagating action potentials, recurrent inhibitory interneurons: control learning, regulate rythm- read-out is dissociated from read-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p612fig16.42 Macrocircuit of the main SOVEREIGN subsystems: visual, motor.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.44 SOVEREIGN[target position, difference] vectors, volitional GO computations] to control decision-making and action.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p614fig16.45 [distance, angle] computations learn dimensionally-consistent [visual, motor] information for [decision, action]s.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p615fig16.46 SOVEREIGN uses homologous processing stages to model the [What, Where] cortical streams, motivational mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p615fig16.47 SOVEREIGN: multiple parallel READ circuits, sensory-drive heterarchy amplifies motivationally favored option.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p616fig16.48 SOVEREIGN tests using virtual reality 3D rendering of a cross maze.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p616fig16.49 SOVEREIGN  animat converted inefficient exploration into an efficient direct learned path to the goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p617fig16.50 Spectral Spacing models of [perirhinal what, parahippocampal where] inputs, fused in the hippocampus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p627tbl17.01 Homologs between [reaction-diffusion, recurrent shunting cellular network] models of development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p628fig17.01 A hydra.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p628fig17.02 how different [cut, graft]s of the normal Hydra may [, not] lead to the growth of a new head.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p629fig17.03 How an initial morphogenetic gradient may be contrast enhanced to exceed the threshold for head formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p630fig17.04 Morphogenesis: use cellular models vs [chemical, fluid] reaction-diffusion models.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p631fig17.05 How a blastula develops into a gastrula.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p634fig17.06 How binary cells with a Gaussian distribution of output thresholds generates a sigmoidal population signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p057fig02.03 neuron basic [anatomy, physiology].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.26 drawing directly in color leads to colored surface representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p090fig03.03 Kanizsa square and reverse-contrast Kanizsa square precepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.11 Another example of neon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p124fig04.02 closed boundaries prevent brightness from flowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p156fig04.33 emergent boundary groupings can segregate textured regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p003fig01.03 Amodal completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#Early ARTMAP benchmark studies
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p167fig04.43 T-junction sensitivity: image, Bipole cells, boundary.png
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
#Instructions
#Preface
#Chapter 1 - Overview
#Chapter 2 - How a brain makes a mind
#Chapter 3 - How a brain sees: Constructing reality
#Chapter 4 - How a brain sees: Neural mechanisms
#Chapter 5 - Learning to attend, recognize, and predict the world
#Chapter 6 - Conscious seeing and invariant recognition
#Chapter 7 - How do we see a changing world?
#Chapter 8 - How we see and recognize object motion
#Chapter 9 - Target tracking, navigation, and decision-making
#Chapter 10 - Laminar computing by cerebral cortex
#Chapter 11 - How we see the world in depth
#Chapter 12 - From seeing and reaching to hearing and speaking
#Chapter 13 - From knowing to feeling
#Chapter 14 - How prefrontal cortex works
#Chapter 15 - Adaptively timed learning
#Chapter 16 - Learning maps to navigate space
#Chapter 17 - A universal development code
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p002fig01.01 Seeing an object vs knowing what it is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p002fig01.02 Dalmation in snow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p003fig01.03 Amodal completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p004fig01.04 Kanizsa stratification: transparency images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p008fig01.05 Noise-saturation dilemma: cell activity; current activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p009fig01.06 Primacy gradient of activity in a recurrent shunting OC-OS network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p011fig01.07 signal function determines how initial activity pattern is transformed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p012fig01.08 Sigmoidal signal: a hybrid of [same, slower, faster]-than-linear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p013fig01.09 sigmoid signal: quenching threshold; contrast enhancement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.10 Minimal adaptive prediction: blocking and unblocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.11 BU-TD mismatch -> orienting system -> nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p018fig01.12 Peak shift and behavioural contrast: prefer new experiences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p019fig01.13 Affective circuits are organized into opponent channels.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p023fig01.14 gated dipole opponent process: sustained on-response; transient off-response.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p024fig01.15 READ circuit: REcurrent Associative Dipole.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.16 Cognitive-Emotional-Motor (CogEM) model: sensory cortex, amygdala, PFC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.17 Sensory-drive heterarchy vs drive hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p026fig01.18 Inverted-U behaviour vs arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p027fig01.19 ventral What [percept, class], dorsal Where [spatial represent, action].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p029tbl01.01 complementary streams [visual boundary, what-where, perception & recognition, object tracking, motor target].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030tbl01.02 complementary streams: What- [rapid, stable] learn invariant object categories, Where- [labile spatial, action] actions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030fig01.20 neo-cortex 6 layers: same canonical laminar design cart [vision, speech, cognition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p032fig01.21 [Retina, LGNs, V[1,2,3,4], MT] to What & Where areas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p035fig01.22 Presentation [normal, silence, noise replaced].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p036fig01.23 working memory [longer list, bigger chunk]s.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p037fig01.24 sentence [learn, store, class] via 3 streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p039tbl01.03 [consciousness, movement] links: visual, auditory, emotional.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p042tbl01.04 six main resonances which support different kinds of conscious awareness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p051fig02.01 laterial inhibition: darker appears darker; lighter appears lighter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p052fig02.02 Adaptive Resonance reactivation: features bottom-up; categories top-down.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p057fig02.03 neuron basic [anatomy, physiology].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p058fig02.04 Learning a global arrow in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.05 Effects of intertrial and intratrial intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.06 Bow due to backward effect in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p060fig02.07 Error gradients depend on list position.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p061fig02.08 neural networks can learn forward and backward associations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p063fig02.09 Short Term Memory (STM): Additive Model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.10 STM Shunting Model, mass action in membrane equations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.11 MTM habituative transmitter gate; LTM gated steepest descent learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p065fig02.12 Three sources of neural network research: [binary, linear, nonlinear].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.13 Hartline: lateral inhibition in limulus retina of horseshoe crab.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.15 Noise-Saturation Dilemma: functional unit is a spatial activity pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.16 Noise-Saturation Dilemma:sensitivity to ratios of inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p072fig02.17 Vision: brightness constancy, contrast normalization.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p072fig02.18 Vision: brightness contrast, conserve a total quantity, total activity normalization.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.19 Computing in a bounded activity domain, Gedanken experiment.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.20 Shunting saturation occurs when inputs get larger to non-interacting cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.21 Shunting saturation: how shunting saturation turns on all of a cells excitable sites as input intensity increases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.22 Computing with patterns: how to compute the pattern-sensitive variable.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p074fig02.23 Shunting on-center off-surround network: no saturation! infinite dynamical range, conserve total activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.25 Weber law, adaptation, and shift property, convert to logarithmic coordinates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.26 Mudpuppy retina neurophysiology, adaptation- sensitivity shifts for different backgrounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.27 Mechanism: cooperative-competitive dynamics, subtractive lateral inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.28 Weber Law and adaptation level: hyperpolarization vs silent inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.29 Weber Law and adaptation level: adaptation level theory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.30 Noise suppression: attenuate zero spatial frequency patterns- no information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.31 Noise suppression -> pattern matching: mismatch (out of phase) suppressed, match (in phase) amplifies pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p079fig02.32 Substrate of resonance: match (in phase) of BU and TD input patterns amplifies matched pattern due to automatic gain control by shunting terms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.33 How do noise suppression signals arise: symmetry-breaking during morphogenesis, opposites attract rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.34 Symmetry-breaking: dynamics and anatomy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.35 Ratio contrast detector: reflectance processing, contrast normalization, discount illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.36 [Noise suppression, contour detection]: uniform patterns are suppressed, contrasts are selectively enhanced, contours are detected.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p085fig02.38 Modelling method and cycle, technological applications: at each stage [behavioural data, design principles, neural data, math model and analysis].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p089fig03.02 What do you think lies under the two grey disks (on a checkers board).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p090fig03.03 Kanizsa square and reverse-contrast Kanizsa square precepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p091fig03.04 blind spot and veins can occlude light to the retina.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p093fig03.06 Every line is an illusion!: boundary completion, surface filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p094fig03.07 Complementary properties of boundaries and surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p095fig03.08 Computer simulation of a Kanizsa square percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p095fig03.09 Simulation of a reverse-contrast Kanizsa square percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.10 The visual illusion of eon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.11 Another example of neon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p098fig03.12 Einstein's face: [edges, texture, shading] are overlaid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.13 Ehrenstein percept weakened as lines deviate from perpendicular.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.14 Perpendicular induction at line ends: [locally [,un], globally] preferred.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.15 orientations: [transient before, equilibrium after] choice.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p102fig03.16 Ts and Ls group together based on shared orientations, not identities.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p102fig03.17 Positions of squares give rise to a percept of three regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p103fig03.18 different spatial arrangements of inducers: emergent [horizontal, diagonal] groupings, but inducers have vertical orientations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p103fig03.19 [diagonal, perpendicular, parallel]: thats how multiple orientations can induce boundary completion of an object.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.20 Sean Williams: how boundaries can form.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.23 pointillist painting: Georges Seurat, A Sunday on la Grande Jatte.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p106fig03.24 Do these ideas work on hard problems: Synthetic Aperture Radar [discount illuminant, filling-in, boundaries].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.25 Matisse, The Roofs of Collioure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.26 drawing directly in color leads to colored surface representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.27 Matisse: Open Window, Collioure, [continuously, sparsely] indiced surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p109fig03.29 Chiaroscuro- Rembrandt self-portrait; Trompe l oeil- Graham Rust.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p109fig03.30 Jo Baer triptych: Primary Light Group [red, green, blue].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.31 Henry Hensche painting: The Bather, is suffused with light.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.32 Claude Monet painting: Poppies Near Argenteuil.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p112fig03.33 Boundary web gradient can cause self-luminosity, similar to watercolor illusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p112fig03.34 Examples of Ross Bleckner's self-luminous paintings.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p113fig03.35 Highest Luminance As White (HLAW) rule, Hans Wallach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p113fig03.36 Blurred Highest Luminance As White (BHLAW) rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p114fig03.37 Perceived reflectance vs cross-section of visual field: anchored brightness, self-luminous.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p114fig03.38 Color field painting: Jules Olitski, spray paintings of ambiguous depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p115fig03.39 Gene Davis paintings [full color, monochromatic]: percepts of grouping and relative depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p116fig03.40 Mona Lisa by Leonardo da Vinci: T-junctions and perspective cues give strong percept of depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p117fig03.41 Boundary contours and feature contours- no inhibition, feature signals survive and spread.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p117fig03.42 Two paintings by Frank Stella.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.43 Four paintings by Monet of the Rouen cathedral under different lighting conditions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.44 Rouen Cathedral at sunset (Monet 1892-1894): equiluminant, obscured and less depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.46 Rouen Cathedral full sunlight (Monet 1892-1894): T-junctions greater depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p123fig04.01 Combining stabilized images with filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p124fig04.02 closed boundaries prevent brightness from flowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p126fig04.03 Color constancy: compute ratios, discount the illuminant, compute lightness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p128fig04.04 reflectance changes at contours: fill-in illuminant-discounted colors.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p129fig04.05 reflectance changes at contours: color contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p129fig04.06 reflectance changes at contours: fill-in color; resolve uncertainty.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p130fig04.07 brightness constancy: boundary peaks spatially narrower than feature peaks.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p131fig04.08 brightness constancy: discount illuminant, ratio-sensitive feature contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p131fig04.09 Simulation of brightness contrast.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p132fig04.10 Simulation of brightness assimilation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p132fig04.11 Simulation of double step and COCE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p133fig04.12 Simulation of the 2D COCE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p133fig04.13 Contrast constancy, relative luminances can be reversed, discounting illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p134fig04.14 Experiments on filling-in: in-the-act; simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p138fig04.15 oriented filtering to grouping and boundary completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p139fig04.16 Simplest simple cell model: threshold linear, half-wave rectification.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p140fig04.17 Complex cells: pool like-oriented simple cells of opposite polarity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.18 Binocular Disparity to reconstruct depth from 2D retinal inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.19 Laminar cortical circuit for complex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p142fig04.20 [, reverse-]Glass patterns give rise to different boundary groupings.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p143fig04.21 Hierarchical resolution of uncertainty for a given field size.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p144fig04.22 End Gap and End Cut simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.23 A perceptual disaster in the feature contour system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.24 Hierarchical resolution of uncertainty- End Cuts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p146fig04.25 How are end cuts created: two stages of short-range competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p148fig04.26 End cut during neon color spreading via 2 stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p149fig04.27 Bipole cells boundary completion: long cooperation & short inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p150fig04.28 Bipole property: boundary completion via long-range cooperation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.29 bipole cells in cortical area V2: first neurophysiological evidence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.30 anatomy: horizontal connections in V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p152fig04.31 Bipoles through the ages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p156fig04.33 emergent boundary groupings can segregate textured regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p157fig04.34 texture: Boundary Contour System resolves errors of complex channels model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.35 Spatial impenetrability prevents grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.36 Graffiti art by Banksy: amodal boundary completion; spatial impenetrability.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p161fig04.37 Boundary Contour System model: analog-sensitive boundary completion Kanizsas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p162fig04.38 Cooperation and competition during grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p164fig04.40 Koffka-Benussi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p165fig04.41 Kanizsa-Minguzzi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p166fig04.42 Computer simulation of Kanizsa-Minguzzi ring percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p167fig04.43 T-junction sensitivity: image, Bipole cells, boundary.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.44 main [boundary, surface] formation stages: LGN-> V1-> V2-> V4.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.45 ON and OFF feature contours: filled-in regions when adjacent to boundary.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p170fig04.46 regions can fill-in feature contour inputs when [adjacent to, collinear with] boundary contour inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p170fig04.47 A double-opponent network processes output signals from FIDOs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.48 closed boundaries -> filling-in; open boundaries -> color spread.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.49 DaVinci stereopsis and occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p173fig04.50 closed boundary at prescribed depth: addition of [bi, mon]ocular boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p176fig04.53 OC-OS [within position, across depth]: brighter Kanizsas look closer.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p178fig04.54 figure-ground separation: bipole cooperation and competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p178fig04.55 Amodal completion of boundaries and surfaces in V2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p179fig04.56 Visible surface 3D perception: boundary enrichment, surface filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p182fig04.58 LAMINART explains many percepts of transparency.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.01 Learn many-to-one (compression, naming), one-to-many (expert knowledge) maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.02 Many-to-one map, two stage compression: [visual, auditory] categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.03 Many-to-one map: IF-THEN rules: [symptom, test, treatment]s; length of stay.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p189fig05.04 hippocampus & several brain regions [learn, remember] throughout life.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.05 LGN [ON, OFF] cells respond differently to [side, end]s of lines.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.07 detailed connections between [retinal ganglion cells, LGN, V1].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.08 LGN [activation, inhibition], with[, out] top-down feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p194fig05.09 [feature, boundary] contours from Ehrenstein disk stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p198fig05.10 Competitive learning and Self-Organized Maps (SOMs).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p199fig05.11 Instar learning: bottom-up adaptive filter for feature patterns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.12 Duality of [outstar, instar] networks.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.14 Outstar learning, both [in, de]creases for LTM to learn STM pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p201fig05.15 Spatial learning pattern, outstar learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.16 Geometry of choice and learning, classifying vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.17 Geometry of choice and learning, trains the closest LTM vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p205fig05.18 catastrophic forgetting due to [competition, associative] learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.20 [PN match, N200 mismatch] computationally complementary potentials.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p213fig05.22 If inputs incorrectly activate a category, how to correct the error.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p213fig05.23 A [category, symbol, other] cannot determine whether an error has occurred.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.24 Learning top-down expectations occurs during bottom-up learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.25 Error correction: [learn, compare] TD-BU inputs, Processing Negativity ERP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.26 Mismatch triggers nonspecific arousal, N200 ERP from orienting system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.27 Every event has [specific attentional cue, nonspecific orienting arousal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p220fig05.29 Vigilance [excitation: search better match, inhibition: resonance & learning].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.30 predictive error -> vigilance increase just enough -> minimax learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p224fig05.32 Learning the alphabet with two different levels of vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.33 Some early ARTMAP benchmark studies (no image - link instead).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.34 ARTMAP learned maps of natural terrains better than AI expert systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p233fig05.42 Evidence for the [gamma, beta] prediction in 3 parts of the brain.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p236fig05.43 nucleus basalis of Meynert releases ACh, reduces AHP, increases vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p242fig05.46 Distributed ARTMAP variants learn the 5-4 category structure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p245fig05.47 [long-range excitatory, short-range disynaptic inhibitory] connections realize the bipole grouping law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p252fig06.01 surface-shroud resonance forms as objects bid for spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p254fig06.03 ARTSCAN Search model learns to recognize and name invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p255fig06.04 The ARTSCAN Search for a desired target object in a scene: Wheres Waldo.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p257fig06.05 Spatial attention flows along object boundaries: Macaque V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.06 Neurophysiological data & simulation: attention can flow along a curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.07 Top-down attentional spotlight becomes a shroud.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p259fig06.08 dARTSCN spatial attention hierarchy [Fast Where, Slow What] stream.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p260fig06.09 Crowding: visible objects & confused recognition, increased flanker spacing at higher eccentricity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p260fig06.10 cortical magnification transforms coordinates: artesian (retina) to log polar (V1).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p261fig06.11 Crowding: visible objects and confused recognition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p261fig06.12 A more serial search is needed due to overlapping conjunctions of features.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p265fig06.13 basal ganglia gate perceptual, cognitive, emotional, etc through parallel loops.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p267fig06.14 Perceptual consistency and figure-ground separation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p268fig06.15 saccades within an object: figure-ground outputs control eye movements via V3AA.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p270fig06.16 Predictive remapping of eye movements, from V3A to LIP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p271fig06.17 Persistent activity in IT to [view, position, size]-invariant category learning by positional ARTSCAN.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.18 pARTSCAN: positionally-invariant object learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.19 persistent activity needed to learn positionally-invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p273fig06.20 pARTSCAN simulation of Li & DiCarlo IT cell swapping data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.21 pARTSCAN [position invariance, selectivity] trade-off of Zoccolan etal 2007.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.22 pARTSCAN: IT cortex processes image morphs with high vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.23 IT responses to image morphs, data vs model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.24 Sterogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p276fig06.25 saccades: predictive gain fields [binocular fusion, filling-in of surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p277fig06.26 Predictive remapping maintains binocular boundary fusion as eyes move.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.27 knowing vs seeing resonances: What [knowing, feature-prototype], Where [seeing, surface-shroud].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p283fig07.01 Boundary competition: spatial habituative gates, orientation gated dipole, bipole grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p284fig07.02 Persistence decreases with flash illuminance & duration [data, simulations].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p285fig07.03 Persistence decrease: rebound to input offset inhibits bipole cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.04 Illusory contours persist longer than real contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.05 Illusory contours inhibited by OFF cell rebounds, propagate to center.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.06 Persistence: [less, more] as adaptation orientation [same, orthogonal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.07 Persistence increases with distance, due to weaker spatial competition in hypercomplex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p290fig08.01 Motion pools contrast-sensitive information moving in the same direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p291fig08.02 Complex cells respond to motion: opposite [direction, contrast polarities].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p292fig08.03 Visual aftereffects: [form- MacKay 90 degree, motion- waterfall  180].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p293fig08.04 Local vs overall motion: aperture problem of EVERY neurons receptive field.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p295fig08.05 sparse feature tracking signals [capture ambiguous, determine perceived] motion direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.06 Simplest example of apparent motion: two dots turning on and off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.07 continuous motion illusions: [Beta with, Phi without] percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.08 Delta motion when [luminance, contrast] of flash 2 is larger than flash 1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.09 motion in opposite directions perceived when 2 later flashes on either side of 1st flash.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.10 motion speed-up perceived when flash duration decreases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.11 illusory contours: double illusion in V1-V2, motion V2-MT interaction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.12 Single flash: Gaussian receptive fields, recurrent OC-OS winner-take-all.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.13 Nothing moves: [single flash, exponential decay], Gaussian peak fixed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.14 Visual inertia: flash decay after the flash shuts off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p301fig08.15 two flashes: cell activation by first waning while second one is waxing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p301fig08.16 sum Gaussian flash activity profiles: [waning 1st, waxing 2nd] -> travelling wave.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p302fig08.17 maximum long-rang apparent motion: Gaussian kernel spans successive flashes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p302fig08.18 G-wave theorem 1: wave moves continuously IFF L <= 2*K.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.19 No motion vs motion at multiple scales: flash distance L, Gaussian width K.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.20 G-wave theorem 2: [speed-up, scale] independent of [distance, scale size].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.21 Equal half-time property: multiple scales generate motion percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.22 Korte Laws: ISIs in the hundreds of milliseconds can cause apparent motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p305fig08.23 Ternus motion: ISI [small- stationary, intermediate- element, larger- group].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p305fig08.24 Reverse-contrast Ternus motion: ISI [small- stationarity, intermediate- group (not element!), larger- group] motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p306fig08.25 Motion BCS model [explain, simulate]s long-range motion percepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p306fig08.26 3D FORMOTION model: track objects moving in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p307fig08.27 Ternus motion: [element- weak, group- strong] transients, element [visual persistence, perceived stationarity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p308fig08.28 Ternus group motion: Gaussian filter of 3 flashes forms one global maximum.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p310fig08.29 when individual component motions combine, their perceived direction & speed changes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.30 3D FORMOTION model: feature tracking [get directional, inhibit inconsistent] signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.31 Motion BCS stages: locally ambiguous motion signals -> globally coherent percept, solving the aperture problem.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p312fig08.32 Schematic of motion filtering circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p312fig08.33 Processing motion signals by a population of speed-tuned neurons.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p314fig08.34 VISTARS navigation model: FORMOTION front end for navigational circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p316fig08.36 Motion capture by directional grouping feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p317fig08.37 Motion capture by directional grouping feedback: [short, long]-range filters, transient cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p319fig08.38 Solving the aperture problem takes time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p320fig08.39 Simulation of the barberpole illusion direction field at two times.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p321fig08.40 [, in]visible occluders [do, not] capture boundaries they share with moving edges.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p322fig08.41 motion transparency: asymmetry [near, far], competing opposite directions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p323fig08.42 Chopsticks: motion separation in depth via [, in]visible occluders [display, percept].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p324fig08.43 ambiguous X-junction motion: MT-MST directional grouping bridges the ambiguous position.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p325fig08.44 The role of MT-V1 feedback:  [motion-form feedback, bipole boundary completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p325fig08.45 Closing formotion feedback loop [MT, MST]-to-V1-to-V2-to-[MT, MST].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p326fig08.46 How do we perceive relative motion of object parts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p327fig08.47 Two classical examples of part motion: Symmetrically moving inducers; Duncker wheel.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p328fig08.48 vector decomposition: (retinal - common = part) motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p328fig08.49 What is the mechanism of vector decomposition, prediction: directional peak shift.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p329fig08.50 How is common motion direction computed?  retinal motion->  bipole grouping (form stream)->  V2-MT formotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p329fig08.51 Large and small scale boundaries differentially form illusory contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.52 Correct motion directions after the peak shift top-down expectation acts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.53 Simulation of the various directional signals of the left dot through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.54 Motion directions of a single dot moving slowly along a cycloid curve through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.55 Duncker Wheel, large: stable rightward motion at the center captures motion at the rim.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p332fig08.56 Duncker Wheel, small: wheel motion as seen when directions are collapsed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p332fig08.57 MODE (MOtion DEcision) model: Motion BCS -> saccadic target selection -> basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p333fig08.58 LIP responses during RT task correct trials: coherence and [activation, inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.59 LIP responses for FD task: predictiveness decreases with increasing coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.60 [RT, FD] task behavioral data: more coherence in the motion causes more accurate decisions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.61 RT task behavioural data: reach time (ms) vs % coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.62 LIP encodes not only where, but also when, to move the eyes - No Bayes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p338fig09.01 optic flow through brain regions: moving observer [navigate, track] moving object.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p338fig09.02 Heading (focus of velocity field) from optic flow: humans accurate +- 1 to 2 degrees.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p339fig09.03 Heading with [body move, eye rotate, combined] -> optic flow [expand, translate, rotate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p339fig09.04 How can translation flow (eye rotation) be subtracted from spiral flow to recover the expansion flow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.05 efference copy command: may use outflow movement commands to eye muscles.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.06 Corollary discharges from outflow movement commands that move muscles.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.07 Log polar remapping of optic flow:  [expansion, circular] motion maps to single direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p341fig09.08 optic flows [retina, V1, MT, MSTd, parietal cortex], V1 log polar mapping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p341fig09.09 MSTd cells are sensitive to [spiral, rotation, expansion] motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.10 Retina -> log polar -> MSTd cell, heading eccentricity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.11 importance of efference copy in real movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.12 two retinal views of the Simpsons: [separate, recognize] overlapping figures.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.13 How do our brains figure out which views belong to which pear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p344fig09.14 Heading sensitivity unimpaired: MT tuning width 38°, MSTd spiral tuning 61°.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p345fig09.15 MT double opponent directional fields: relative motions [objects, backgrounds].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p346fig09.16 macrocircuit of 13 brain regions used to move the eyes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p347fig09.17 leftward eye movement model: retina-> MT-> MST[v,d]-> pursuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p347fig09.18 MST[v,d] circuits enable predictive target tracking by the pursuit system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p348fig09.19 MSTv cells: target speed on retina, background speed on retina, pursuit speed command.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.20 Steering from optic flow: goals are attractors, obstacles are repellers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.21 Steering dynamics goal approach: [obstacle, goal, heading] -> steering.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.22 negative Gaussian of an obstacle: avoid obstacle without losing sight of goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.23 Unidirectional transient cells: [lead, trail]ing boundaries, driving video.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.24 Directional transient cells respond most to motion in their preferred directions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.25 M+ computes global motion estimate from noisy local motion estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p352fig09.26 heading direction final stage: beautiful optic flow, accuracy matches humans.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p355fig10.02 seeing vs knowing distinction is difficult because they interact so strongly.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.05 Activation of V1 by direct excitatory signals from LGN to layer 4 of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.06 Why another layer 6-to-4 signal: on-center off-surround.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.09 Grouping starts in layer 2-3: long-range horizontal excitation, short-range inhibition of target pyramidal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p361fig10.10 Bipole property controls perceptual grouping: inputs [excitatory sum, inhibitory normalize].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p362fig10.11 Final grouping: folded feedback, strongest enhanced on-center, weaker suppressed off-surround, interlaminar functional columns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p363fig10.12 V2 repeats V1 circuitry at larger spatial scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.14 Explanation: grouping and attention share the same modulatory decision circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.15 Attention protects target from masking stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.16 Flankers can enhance or suppress targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.17 Attention has greater effect on low contrast targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.18 Texture reduces response to a bar: [iso-orientation, perpendicular] suppression.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p369fig10.19 Unconscious learning of motion direction, without [extra-foveal attention, awareness] of stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p371fig11.01 FACADE theory explains how the 3D boundaries and surfaces are formed to see the world in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p372fig11.02 3D surface filling-in of [lightness, color, depth] by a single process: FACADE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p373fig11.03 Both [contrast-specific binocular fusion, contrast-invariant boundary perception] are needed to see the world in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p374fig11.04 Three processing stages of [monocular simple, complex] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p374fig11.05 Contrast constraint on binocular fusion: only contrasts which are derived from the same objects in space are binoculary matched.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.06 Binocular fusion by obligate cells in V1-3B when =[left,right] contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.08 Correspondance problem: How does the brain inhibit false matches. contrast constraint not enough.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.09 V2 disparity filter solves correspondence problem: false matches suppressed by line-of-sight inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p377fig11.11 DaVinci stereopsis: monocular information and depth percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p378fig11.12 3D LAMINART: V2 monocular+binocular line of sight inputs -> depth perception.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p381fig11.15 DaVinci stereopsis variant of (Gillam, Blackburn, Nakayama 1999): same mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p382fig11.16 DaVinci stereopsis of [3 narrow, one thick] rectangles: same explanation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p383fig11.17 Venetian blind effect: [left, right] eye matching bars.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p384fig11.18 Venetian blind effect: Surface[, -to-boundary] surface contour signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p385fig11.19 Dichoptic masking: [left, right] images have sufficiently different contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p385fig11.20 Dichoptic masking, Panum's limiting case: simplified version of Venetian blind effect.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p386fig11.21 Craik-O'Brien-Cornsweet Effect: 2D surface at a very near depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p387fig11.22 Julesz stereogram: boundaries with[out, ] surface contour feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p388fig11.23 Sparse stereogram, large regions of ambiguous white: correct surface in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p388fig11.24 depth-ambiguous feature contours: boundary groups lift to correct surface in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p389fig11.25 Boundaries: not just edge detectors, or a shaded ellipse would look [flat, uniformly gray].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p390fig11.26 Multiple-scale depth-selective groupings determine perceived depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p391fig11.27 Multiple-scale grouping and size-disparity correlation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p391fig11.28 Ocular dominance columns, LGN mappings into layer 4C of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p392fig11.29 3D vision figure-ground separation: multiple-scale, depth-selective boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p392fig11.30 How multiple scales vote for multiple depths, scale-to-depth and depth-to-scale maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.31 LIGHTSHAFT model: determining depth-from-texture percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.32 Kulikowski stereograms: binocular matching of out-of-phase [Gaussians, rectangles].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p394fig11.33 Kaufman stereogram: simultaneous fusion and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p395fig11.34 3D LAMINART vs 7 other rivalry models: stable vision and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p396fig11.35 Three properties of bipole boundary grouping in V2: boundaries oscillate with rivalry-inducing stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p397fig11.36 temporal dynamics of [rivalrous, coherent] boundary switching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p398fig11.37 Simulation of the no swap baseline condition (Logothetis, Leopold, Sheinberg 1996).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.38 Simulation of the swap condition of (Logothetis, Leopold, Sheinberg 1996).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.39 Simulation of the eye rivalry data of (Lee, Blake 1999).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p400fig11.40 How do ambiguous 2D shapes contextually define a 3D object form.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.41 3D LAMINART: [angle, disparity-gradient] cells learn 3D representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.42 hypothetical cortical hypercolumn: how [angle, disparity-gradient] cells may self-organize during development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.43 A pair of disparate images of a scene from the University of Tsukuba.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.44 3D LAMINART disparities [5, 6, 8, 10, 11, 14]: images of objects in common depth planes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p403fig11.45 SAR processing by multiple scales: reconstruction of a SAR image.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p405fig12.01 [What ventral, Where-How dorsal] cortical streams for [audition, vision].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p406fig12.02 Three S's of movement: Synergy formation, muscle Synchrony, volitional Speed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p407fig12.03 Motor cortical cells: vectors for [direction, length] of commanded movement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p409fig12.04 VITE simulations: difference vector emergent from network interactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.05 VITE: velocity profile invariance [short, long] movements for same GO signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.06 Monkeys transform movement: 2 -> 10 o'clock target, 50 or 100 msec after activation of 2 o'clock target.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p411fig12.07 VITE: higher peak velocity due to target switching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p411fig12.08 GO signals gate agonist-antagonist [difference, present position] vector processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p412fig12.09 Vector Associative Map: difference vector mismatch learning calibrates [target, present] position vectors.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p413fig12.10 VITE: cortical area [4,5] combine [trajectory, inflow] signals from [spinal cord, cerebellum] for [variable loads, obstacles].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p414fig12.11 [data, simulation]s from cortical areas 4 and 5 during a reach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p415fig12.12 [VITE, FLETE, cerebellar, opponent muscle] model for trajectory formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.13 DIRECT model: Endogenous Random Generator learns volitional reaches.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.14 DIRECT reaches [unconstrained, with TOOL, elbow@140°, blind].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p417fig12.15 From Seeing & Reaching (DIRECT) to Hearing & Speaking (DIVA): homologous circular reactions, [tool use, coarticulation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p418fig12.16 Anatomy of DIVA model processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p422fig12.19 ARTSTREAM: derive streams from [pitch, source direction].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.21 Pitch shifts with component shifts, pitch vs lowest harmonic number.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.22 Decomposition of a sound in terms of three of its harmonics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p426fig12.24 Spectrograms of -ba- and -pa- show the transient and sustained parts of their spectrograms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p428fig12.25 ARTSPEECH: auditory-articulatory feedback loop & imitative map, [auditory, motor] dimensionally consistent, motor theory of speech.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p430fig12.26 NormNet: speaker normalization via specializations of mechanisms for auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p431fig12.27 ARTSTREAM & NormNet strip maps: variants of occular dominance columns in visual cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p432fig12.28 SpaN: spatial representations of numerical quantities in the parietal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p433fig12.29 What stream: place-value [number map, language category]s; to Where stream: numerical strip maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.31 Working memory: temporal order STM is often imperfect, then stored in LTM.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.32 Free recall bowed serial position curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.33 Working memory models: item and order, or competitive queuing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p438fig12.34 LTM Invariance Principle: [STM, LTM] new words must not cause catastrophic forgetting of subwords.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p439fig12.35 Normalization Rule: total activity of working memory has upper bound independent of number of items.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p439fig12.36 [Item, Order] working memories: [content-addressable categories, temporal order, [excitatory, inhibitory] recurrence, rehearsal wave.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p440fig12.37 Normalization Rule: primacy bow as more items stored.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p441fig12.38 LTM Invariance Principle: new events do not change the relative activities of past event sequences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p442fig12.39 [LTM invariance, Normalization Rule] Shunt normalization -> STM bow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p442fig12.40 [LTM Invariance, normalization, STM steady attention]: only [primacy, bowed] gradients of activity can be stored.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p443fig12.41 Neurophysiology of sequential copying: [primacy gradient, self-inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p447fig12.45 LIST PARSE: order errors vs serial position with extended pauses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p448fig12.46 Masking Field working memory is a multiple-scale self-similar recurrent shunting on-center off-surround network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p449fig12.47 Masking Field self-similar [recurrent inhibitory, top-down excitatory] signals to the item chunk working memory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p452fig12.48 Perceptual integration of acoustic cues: [silence vs noise] durations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.49 ARTWORD: acoustic cues, phonetic [features, WM], Masking Field unitized lists, gain control.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.50 ARTWORD perception cycle: sequences-> chunks-> compete-> top-down expectations-> item working memory->  develops item-list resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p454fig12.51 Resonant transfer: as silence interval increases, a delayed additional item can facilitate perception of a longer list.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p455fig12.52 cARTWORD dynamics 1-2-3: resonant activity in item and feature layers corresponds to conscious speech percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.53 cARTWORD dynamics 1-silence-3: Gap in resonant activity of 1-silence-3 in [item, feature] layers corresponds to perceived silence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.54 cARTWORD dynamics: 1-noise-3: Resonance of 1-2-3 in [item, feature] layers  restores item 2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p457fig12.55 cARTWORD dynamics 1-noise-5: Figures 12.[54, 55] future context can disambiguate past noisy sequences that are otherwise identical.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p459fig12.56 Rank information on the position of an item in a list using numerical hypercolumns in the prefrontal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p460fig12.57 lisTELOS for saccades: prototype to [store, recall] other [cognitive, spatial, motor] information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p461fig12.58 lisTELOS shows [BG nigro-[thalamic, collicular], FEF, ITa, PFC, PNR-THAL, PPC, SEF, SC, V1, V4-ITp, Visual Cortex input] and [GABA].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p462fig12.59 TELOS: balancing reactive vs. planned movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p463fig12.60 Rank-related activity in PFC and SEF from two different experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p464fig12.61 SEF saccades microstimulating electrode: spatial gradient of habituation alters order, but not which, saccades are performed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p464fig12.62 The most habituated position is foveated last: because stimulation spreads in all directions, saccade trajectories tend to converge.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p465fig12.63 lisTELOS and data: microstimulation biases selection so saccade trajectories converge toward a single location in space.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p467fig12.64 Some of the auditory cortical regions that respond to sustained or transient sounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p468fig12.65 [PHONET, ARTPHONE] linguistic properties: creates rate-invariant representations for variable-rate speech, paradoxical VC-CV category boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.66 PHONET: relative duration of [consonant, vowel] pairs can [preserve, change] a percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.67 PHONET [transient, sustained] cells that respond to certain [consonant transient, sustained vowel] sounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p471fig12.68 Mismatch vs resonant fusion: effect of silence interval length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p473fig12.69 ART Matching Rule properties explain error rate and mean reaction time (RT) data from lexical decision experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p474fig12.70 macrocircuit model to explain lexical decision task data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p476fig12.71 Word frequency data model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p481fig13.01 Cognitive-Emotional-Motor (CogEM): macrocircuit of [function, anatomy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.03 CogEM: supported by anatomical connections [[sensory, orbitofrontal] cortices, amygdala].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.05 Classical conditioning: perhaps simplest kind of associative learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.06 Classical conditioning: inverted-U vs InterStimulus Interval (ISI).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.07 Paradigm of secondary conditioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.08 Blocking paradigm: cues lacking different consequences may fail to be attended.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.09 Equally salient cues can be conditioned in parallel to an emotional consequence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.10 Blocking: both [secondary, attenuation of] conditioning at zero ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p487fig13.11 CogEM : three main properties to explain how attentional blocking occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p488fig13.12 Motivational feedback and blocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p489fig13.13 CogEM and conditioning: positive ISI; inverted-U vs ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.14 Cognitive-Emotional circuit: for proper conditioning, sensory needs >= 2 processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p492fig13.16 Polyvalent CS sampling and US-activated nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p493fig13.17 Learning nonspecific arousal and CR read-out.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.18 Learning to control nonspecific arousal and read-out of the CR: two stages of CS.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.19 CogEM: secondary conditioning of [arousal, response], multiple [drive, input]s, motivational sets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p496fig13.20 A single avalanche sampling cell can learn an arbitrary space-time pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p497fig13.21 nonspecific arousal: primitive crayfish swimmerets, songbird pattern generator avalanche.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p499fig13.23 Self-organizing avalanches [instars filter, serial learning, outstars read-out], Serial list learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p500fig13.24 Primary [excitatory, inhibitory] conditioning using opponent processes and their antagonistic rebounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.25 Unbiased transducer in finite rate physical process: mass action by a chemical transmitter is the result.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.26 Transmitter y [accumulation, release]:  y restored < infinite rate, evolution has exploited this.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p502fig13.27 Transmitter minor mathematical miracle [accumulation, release]: S*y = S*A*B div (A + S) (gate, mass action).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p502fig13.28 Habituative transmitter gate: fast [increment, decrement]s of input lead to [overshoot, habituation, undershoot]s, Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p503fig13.29 ON response to phasic ON input has Weber Law properties due to the habituative transmitter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.30 OFF-rebound transient due to phasic input offset: arousal level sets ratio ON vs OFF rebounds, Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.31 Behavioral contrast rebounds: decrease [food-> negative Frustration, shock-> positive Relief] reinforcers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.32 Behavioral contrast: [response suppression, antagonist rebound] both calibrated by shock levels.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.33 Novelty reset- rebound to arousal onset: equilibrate to [I, J]; keep phasic input J fixed; interpret this equation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p506fig13.34 Novelty reset: rebound to arousal onset, reset of dipole field by unexpected event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p506fig13.35 Shock [cognitive, emotional] effects: [reinforcer, sensory cue, expectancy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p509fig13.36 Life-long learning: selective without [passive forgetting, associative saturation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.37 A disconfirmed expectation inhibits prior incentive, but is insufficient to prevent associative saturation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.38 Dissociation of LTM read-[out, in]: dendritic action potentials as teaching signals, early predictions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.39 Learn net dipole output pattern: [shunting competition, informational noise suppression] in affective gated dipoles, back-propagation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p512fig13.40 Conditioned excitor extinguishes: [learning, forgetting] phases, shock expectation disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.41 Conditioned inhibitor does not extinguish: [learn, forget] phases, same [CS, teacher] can be used.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.42 Conditioned excitor extinguishes when expectation of shock is disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.43 Conditioned excitor extinguishes: expectation that -no shock- follows CS2 is NOT disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p514fig13.44 Analog of the COgEM model maps of [object X, proto-self], assembly of second-order map.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p519fig14.01 Coronal sections of prefrontal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p524fig14.04 Basal ganglia circuit for dopaminergic Now Print signals from the substantia nigra pars compacta in response to unexpected rewards.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p530fig14.05 Visual [pop-out, search]-> reaction time experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.06 ARTSCENE: classification of scenic properties as texture categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.07 ARTSCENE voting achieves even better prediction of scene type.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p533fig14.09 ARTSCENE search [data, simulation]s for 6 pairs of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p540fig15.01 [Delay, trace conditioning] paradigms:  require a CS memory trace over the ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.03 Timed responses from adaptively timed conditioning: Weber laws, inverted U as a function of ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p542fig15.04 blinks of [nictitating membrane, eyelid] are adaptively timed: closure occurs at arrival of the US following the CS, obeys Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.05 Learning with two ISIs: each peak obeys Weber Law, strong evidence for spectral learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.06 Circuit between [dentate granule, CA1 hippocampal pyramid] cells seems to compute spectrally timed responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.07 Spectral timing: STM sensory representation-> Spectral activation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.08 Habituative transmitter gate: spectral activities-> sigmoid signals-> gated by habituative transmitters.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.09 Habituative transmitter gate: increases with accumulation, decreases from gated inactivation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p545fig15.10 A timed spectrum of gated sampling intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p545fig15.11  Associative learning, gated steepest descent learning: output from each population is a doubly gated signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p546fig15.12 Computer simulation of spectral learning: fastest with large sampling signals when the US occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p546fig15.13  Adaptive timing is a population property, random spectrum of rates achieves good collective timing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p547fig15.14 [Un, ]expected non-occurences of goal: a predictive failure leads to: Orienting Reactions, Emotional- Frustration,  Motor- Explorator.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p547fig15.15  Expected non-occurrence of goal: some rewards are reliable but delayed in time, do not lead to orienting reactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.17 The timing paradox: want [accurate timing, to inhibit exploratory behaviour throught ISI].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.18 Weber Law: reconciling accurate and distributed timing, different ISIs-  standard deviation = peak time, Weber law rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.19 Conditioning, Attention, and Timing circuit: Hippocampus spectrum-> Amgdala orienting system-> neocortex motivational attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p550fig15.20 Adaptively timed Long Term Depression between parallel fibres and Purkinje cells-> movement gains within learned time interval.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p551fig15.21 Cerebellum: important cells types and circuitry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p551fig15.22 Responses of a turtle retinal cone to brief flashes of light of increasing intensity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p552fig15.23 Cerebellar biochemistry: mGluR supports adaptively timed conditioning at cerebellar Purkinje cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p556fig15.24 Cerebellar cortex responses: [data, model] short latency responses after lesioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.25 Computer simulations of adaptively timed [LTD at Purkinje cells, activation of cereballar nuclear cells].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.26 Brain [region, process]s that contribute to autistic behavioral symptoms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.27 Spectrally timed SNc learning: brain [region, process]s release of dopaminergic signals, unexpected reinforcing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.28 Neurophysiological data and simulations of SNc responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.29 Excitatory pathways that support activation of the SNc by a US and the conditioning of a CS to the US.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.30 Inhibitory pathway: striosomal cells predict [timing, magnitude] of reward signal to cancel it.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.31 Expectation timing: timing spectrum, striosomal cells delayed transient signals, gate [learning, read-out].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.32 Inhibitory pathway expectation magnitude: is a negative feedback control system for learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.33 MOTIVATOR: thalamocortical loops through basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.34 Distinct basal ganglia zones for each loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p564fig15.35 GO signal to recurrent shunting OC-OS networks: control of the [fore, hind] limbs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p565fig15.36 (a) FOVEATE: control of saccadic eye movements within the peri-pontine reticular formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p566fig15.37 FOVEATE: steps in generation of a saccade.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p567fig15.38 Gated Pacemaker of [diurnal, nocturnal] circadian rythms: whether phasic light turns the pacemaker on or off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p568fig15.39 MOTIVATOR hypothalamic gated dipoles: inputs, [object, value, object-value] categories, reward expectation filter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p569fig15.40 GO and STOP movement signals: control by [direct, indirect] basal ganglia circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p573fig16.01 Hippocampal place cells: discovery from rat [experimental chamber, neurophysiological recordings].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p574fig16.02 Neurophysiological recordings of 18 different place cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p575fig16.03 Rat navigation: firing patterns of [hippocampal place, entrorhinal grid] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p578fig16.04 Cross-sections of the hippocampal regions and the inputs to them.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p580fig16.05 GridPlaceMap hierarchy of SOMs with identical equations: learns 2D [grid, place] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p581fig16.06 Trigonometry of spatial navigation: coactivation of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.07 Stripe cells multiple [orientation, phase, scale]s: directionally-sensitive ring attractors, velocity, distance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.08 Evidence for stripe-like cells: entorhinal cortex data, Band Cells position from grid cell oscillatory interference.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.09 GRIDSmap: stripe cells for rat trajectories, self-organizing map learned hexagonal grid cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.10 GRIDSmap embedded into hierarchy of SOMs: [angular head velocity, linear velocity] signals to place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.11 GRIDSmap learning of hexagonal grid fields, multiple phases per scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.12 Temporal development of grid fields: orientations rotate to align with each other.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.13 Hexagonal grid cell receptive fields: somewhat insensitive to [number, directional selectivities] of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.14 GRIDSmap: Superimposed firing of stripe cells supports learning hexagonal grid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.15 Why is a hexagonal grid favored: stripe cells at intervals of 45 degrees, GRIDSmap does not learn, oscillatory interference does.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.16 Grid-to-place SOM: formation of place cell fields via grid-to-place cell learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p587fig16.17 A refined analysis: SOM amplifies most frequent and energetic coactivations, stripe fields separated by [90°, 60°].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p588fig16.18 GridPlaceMap hierarchy of SOMs: coordinated learning of [grid, place, inomodal] cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p589fig16.19 How does the spatial scale increase along the MEC dorsoventral axis.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p590fig16.20 Dorsoventral gradient in the rate of synaptic integration of MEC layer II stellate cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p590fig16.21 Frequency of membrane potential oscillations in grid cells decreases along the dorsoventral gradient of the MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p591fig16.22 Dorsoventral [time constant, duration] gradients in AHP kinetics of MEC layer II stellate cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p591fig16.23 Spectral spacing model: map cells respond to stripe cell inputs of multiple scales, How do entorhinal cells solve the scale selection problem.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p592fig16.24 Parameter settings in the Spectral Spacing Model that were used in simulations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.25 Spectral Spacing Model equations for [STM, MTM, LTM].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.26 Gradient of grid spacing along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p594fig16.27 Gradient of field width along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p595fig16.28 Peak and mean rates at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.29 Subthreshold membrane mV oscillations: decreasing Hz at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.30 Spatial phases of learned grid and place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.31 Multimodal place cell firing in large spaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.32 Model fits data about grid cell development in juvenile rats:  grid [score increases, spacing flat].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.33 Model fits [place, grid, directional] cell data about grid cell development in juvenile rats:  [spatial information, inter-trial stability] vs postnatal day.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.34 spiking GridPlaceMap: generates theta-modulated place and grid cell firing, unlike the rate-based model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p599fig16.35 anatomically overlapping grid cell modules: effects of [different modules in one animal, DV location, response rate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p602fig16.37 Hippocampal inactivation by muscimol disrupts grid cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p603fig16.38 Role of hippocampal feedback in maintaining grid fields, muscimol inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p607fig16.40 Effects of medial septum (MS) inactivation on grid cells: data, simulations, gridness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p611fig16.41 back-propagating action potentials, recurrent inhibitory interneurons: control learning, regulate rythm- read-out is dissociated from read-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p612fig16.42 Macrocircuit of the main SOVEREIGN subsystems: visual, motor.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.44 SOVEREIGN[target position, difference] vectors, volitional GO computations] to control decision-making and action.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p614fig16.45 [distance, angle] computations learn dimensionally-consistent [visual, motor] information for [decision, action]s.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p615fig16.46 SOVEREIGN uses homologous processing stages to model the [What, Where] cortical streams, motivational mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p615fig16.47 SOVEREIGN: multiple parallel READ circuits, sensory-drive heterarchy amplifies motivationally favored option.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p616fig16.48 SOVEREIGN tests using virtual reality 3D rendering of a cross maze.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p616fig16.49 SOVEREIGN  animat converted inefficient exploration into an efficient direct learned path to the goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p617fig16.50 Spectral Spacing models of [perirhinal what, parahippocampal where] inputs, fused in the hippocampus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p627tbl17.01 Homologs between [reaction-diffusion, recurrent shunting cellular network] models of development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p628fig17.01 A hydra.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p628fig17.02 how different [cut, graft]s of the normal Hydra may [, not] lead to the growth of a new head.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p629fig17.03 How an initial morphogenetic gradient may be contrast enhanced to exceed the threshold for head formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p630fig17.04 Morphogenesis: use cellular models vs [chemical, fluid] reaction-diffusion models.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p631fig17.05 How a blastula develops into a gastrula.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p634fig17.06 How binary cells with a Gaussian distribution of output thresholds generates a sigmoidal population signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p057fig02.03 neuron basic [anatomy, physiology].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.26 drawing directly in color leads to colored surface representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p090fig03.03 Kanizsa square and reverse-contrast Kanizsa square precepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.11 Another example of neon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p124fig04.02 closed boundaries prevent brightness from flowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p156fig04.33 emergent boundary groupings can segregate textured regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p003fig01.03 Amodal completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#Early ARTMAP benchmark studies
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p167fig04.43 T-junction sensitivity: image, Bipole cells, boundary.png
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopCopyright.html#Grossberg
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/home/bill/web/Neural nets/Grossberg/reader Howell notes.HtmWeb.html
#Instructions
#Preface
#Chapter 1 - Overview
#Chapter 2 - How a brain makes a mind
#Chapter 3 - How a brain sees: Constructing reality
#Chapter 4 - How a brain sees: Neural mechanisms
#Chapter 5 - Learning to attend, recognize, and predict the world
#Chapter 6 - Conscious seeing and invariant recognition
#Chapter 7 - How do we see a changing world?
#Chapter 8 - How we see and recognize object motion
#Chapter 9 - Target tracking, navigation, and decision-making
#Chapter 10 - Laminar computing by cerebral cortex
#Chapter 11 - How we see the world in depth
#Chapter 12 - From seeing and reaching to hearing and speaking
#Chapter 13 - From knowing to feeling
#Chapter 14 - How prefrontal cortex works
#Chapter 15 - Adaptively timed learning
#Chapter 16 - Learning maps to navigate space
#Chapter 17 - A universal development code
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p002fig01.01 Seeing an object vs knowing what it is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p002fig01.02 Dalmation in snow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p003fig01.03 Amodal completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p004fig01.04 Kanizsa stratification: transparency images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p008fig01.05 Noise-saturation dilemma: cell activity; current activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p009fig01.06 Primacy gradient of activity in a recurrent shunting OC-OS network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p011fig01.07 signal function determines how initial activity pattern is transformed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p012fig01.08 Sigmoidal signal: a hybrid of [same, slower, faster]-than-linear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p013fig01.09 sigmoid signal: quenching threshold; contrast enhancement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.10 Minimal adaptive prediction: blocking and unblocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p016fig01.11 BU-TD mismatch -> orienting system -> nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p018fig01.12 Peak shift and behavioural contrast: prefer new experiences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p019fig01.13 Affective circuits are organized into opponent channels.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p023fig01.14 gated dipole opponent process: sustained on-response; transient off-response.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p024fig01.15 READ circuit: REcurrent Associative Dipole.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.16 Cognitive-Emotional-Motor (CogEM) model: sensory cortex, amygdala, PFC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p025fig01.17 Sensory-drive heterarchy vs drive hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p026fig01.18 Inverted-U behaviour vs arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p027fig01.19 ventral What [percept, class], dorsal Where [spatial represent, action].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p029tbl01.01 complementary streams [visual boundary, what-where, perception & recognition, object tracking, motor target].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030tbl01.02 complementary streams: What- [rapid, stable] learn invariant object categories, Where- [labile spatial, action] actions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p030fig01.20 neo-cortex 6 layers: same canonical laminar design cart [vision, speech, cognition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p032fig01.21 [Retina, LGNs, V[1,2,3,4], MT] to What & Where areas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p035fig01.22 Presentation [normal, silence, noise replaced].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p036fig01.23 working memory [longer list, bigger chunk]s.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p037fig01.24 sentence [learn, store, class] via 3 streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p039tbl01.03 [consciousness, movement] links: visual, auditory, emotional.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p042tbl01.04 six main resonances which support different kinds of conscious awareness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p051fig02.01 laterial inhibition: darker appears darker; lighter appears lighter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p052fig02.02 Adaptive Resonance reactivation: features bottom-up; categories top-down.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p057fig02.03 neuron basic [anatomy, physiology].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p058fig02.04 Learning a global arrow in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.05 Effects of intertrial and intratrial intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p059fig02.06 Bow due to backward effect in time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p060fig02.07 Error gradients depend on list position.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p061fig02.08 neural networks can learn forward and backward associations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p063fig02.09 Short Term Memory (STM): Additive Model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.10 STM Shunting Model, mass action in membrane equations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p064fig02.11 MTM habituative transmitter gate; LTM gated steepest descent learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p065fig02.12 Three sources of neural network research: [binary, linear, nonlinear].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.13 Hartline: lateral inhibition in limulus retina of horseshoe crab.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.15 Noise-Saturation Dilemma: functional unit is a spatial activity pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p071fig02.16 Noise-Saturation Dilemma:sensitivity to ratios of inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p072fig02.17 Vision: brightness constancy, contrast normalization.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p072fig02.18 Vision: brightness contrast, conserve a total quantity, total activity normalization.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.19 Computing in a bounded activity domain, Gedanken experiment.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.20 Shunting saturation occurs when inputs get larger to non-interacting cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.21 Shunting saturation: how shunting saturation turns on all of a cells excitable sites as input intensity increases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p073fig02.22 Computing with patterns: how to compute the pattern-sensitive variable.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p074fig02.23 Shunting on-center off-surround network: no saturation! infinite dynamical range, conserve total activity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.25 Weber law, adaptation, and shift property, convert to logarithmic coordinates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p076fig02.26 Mudpuppy retina neurophysiology, adaptation- sensitivity shifts for different backgrounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.27 Mechanism: cooperative-competitive dynamics, subtractive lateral inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p077fig02.28 Weber Law and adaptation level: hyperpolarization vs silent inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.29 Weber Law and adaptation level: adaptation level theory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.30 Noise suppression: attenuate zero spatial frequency patterns- no information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p078fig02.31 Noise suppression -> pattern matching: mismatch (out of phase) suppressed, match (in phase) amplifies pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p079fig02.32 Substrate of resonance: match (in phase) of BU and TD input patterns amplifies matched pattern due to automatic gain control by shunting terms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.33 How do noise suppression signals arise: symmetry-breaking during morphogenesis, opposites attract rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p080fig02.34 Symmetry-breaking: dynamics and anatomy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.35 Ratio contrast detector: reflectance processing, contrast normalization, discount illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p081fig02.36 [Noise suppression, contour detection]: uniform patterns are suppressed, contrasts are selectively enhanced, contours are detected.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p085fig02.38 Modelling method and cycle, technological applications: at each stage [behavioural data, design principles, neural data, math model and analysis].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p089fig03.02 What do you think lies under the two grey disks (on a checkers board).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p090fig03.03 Kanizsa square and reverse-contrast Kanizsa square precepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p091fig03.04 blind spot and veins can occlude light to the retina.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p093fig03.06 Every line is an illusion!: boundary completion, surface filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p094fig03.07 Complementary properties of boundaries and surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p095fig03.08 Computer simulation of a Kanizsa square percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p095fig03.09 Simulation of a reverse-contrast Kanizsa square percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.10 The visual illusion of eon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.11 Another example of neon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p098fig03.12 Einstein's face: [edges, texture, shading] are overlaid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.13 Ehrenstein percept weakened as lines deviate from perpendicular.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.14 Perpendicular induction at line ends: [locally [,un], globally] preferred.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p100fig03.15 orientations: [transient before, equilibrium after] choice.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p102fig03.16 Ts and Ls group together based on shared orientations, not identities.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p102fig03.17 Positions of squares give rise to a percept of three regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p103fig03.18 different spatial arrangements of inducers: emergent [horizontal, diagonal] groupings, but inducers have vertical orientations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p103fig03.19 [diagonal, perpendicular, parallel]: thats how multiple orientations can induce boundary completion of an object.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.20 Sean Williams: how boundaries can form.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.23 pointillist painting: Georges Seurat, A Sunday on la Grande Jatte.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p106fig03.24 Do these ideas work on hard problems: Synthetic Aperture Radar [discount illuminant, filling-in, boundaries].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.25 Matisse, The Roofs of Collioure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.26 drawing directly in color leads to colored surface representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.27 Matisse: Open Window, Collioure, [continuously, sparsely] indiced surfaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p109fig03.29 Chiaroscuro- Rembrandt self-portrait; Trompe l oeil- Graham Rust.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p109fig03.30 Jo Baer triptych: Primary Light Group [red, green, blue].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.31 Henry Hensche painting: The Bather, is suffused with light.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p110fig03.32 Claude Monet painting: Poppies Near Argenteuil.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p112fig03.33 Boundary web gradient can cause self-luminosity, similar to watercolor illusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p112fig03.34 Examples of Ross Bleckner's self-luminous paintings.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p113fig03.35 Highest Luminance As White (HLAW) rule, Hans Wallach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p113fig03.36 Blurred Highest Luminance As White (BHLAW) rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p114fig03.37 Perceived reflectance vs cross-section of visual field: anchored brightness, self-luminous.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p114fig03.38 Color field painting: Jules Olitski, spray paintings of ambiguous depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p115fig03.39 Gene Davis paintings [full color, monochromatic]: percepts of grouping and relative depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p116fig03.40 Mona Lisa by Leonardo da Vinci: T-junctions and perspective cues give strong percept of depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p117fig03.41 Boundary contours and feature contours- no inhibition, feature signals survive and spread.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p117fig03.42 Two paintings by Frank Stella.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.43 Four paintings by Monet of the Rouen cathedral under different lighting conditions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p120fig03.44 Rouen Cathedral at sunset (Monet 1892-1894): equiluminant, obscured and less depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.46 Rouen Cathedral full sunlight (Monet 1892-1894): T-junctions greater depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p123fig04.01 Combining stabilized images with filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p124fig04.02 closed boundaries prevent brightness from flowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p126fig04.03 Color constancy: compute ratios, discount the illuminant, compute lightness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p128fig04.04 reflectance changes at contours: fill-in illuminant-discounted colors.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p129fig04.05 reflectance changes at contours: color contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p129fig04.06 reflectance changes at contours: fill-in color; resolve uncertainty.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p130fig04.07 brightness constancy: boundary peaks spatially narrower than feature peaks.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p131fig04.08 brightness constancy: discount illuminant, ratio-sensitive feature contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p131fig04.09 Simulation of brightness contrast.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p132fig04.10 Simulation of brightness assimilation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p132fig04.11 Simulation of double step and COCE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p133fig04.12 Simulation of the 2D COCE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p133fig04.13 Contrast constancy, relative luminances can be reversed, discounting illuminant.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p134fig04.14 Experiments on filling-in: in-the-act; simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p138fig04.15 oriented filtering to grouping and boundary completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p139fig04.16 Simplest simple cell model: threshold linear, half-wave rectification.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p140fig04.17 Complex cells: pool like-oriented simple cells of opposite polarity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.18 Binocular Disparity to reconstruct depth from 2D retinal inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p141fig04.19 Laminar cortical circuit for complex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p142fig04.20 [, reverse-]Glass patterns give rise to different boundary groupings.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p143fig04.21 Hierarchical resolution of uncertainty for a given field size.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p144fig04.22 End Gap and End Cut simulation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.23 A perceptual disaster in the feature contour system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p145fig04.24 Hierarchical resolution of uncertainty- End Cuts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p146fig04.25 How are end cuts created: two stages of short-range competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p148fig04.26 End cut during neon color spreading via 2 stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p149fig04.27 Bipole cells boundary completion: long cooperation & short inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p150fig04.28 Bipole property: boundary completion via long-range cooperation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.29 bipole cells in cortical area V2: first neurophysiological evidence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p151fig04.30 anatomy: horizontal connections in V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p152fig04.31 Bipoles through the ages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p156fig04.33 emergent boundary groupings can segregate textured regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p157fig04.34 texture: Boundary Contour System resolves errors of complex channels model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.35 Spatial impenetrability prevents grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p159fig04.36 Graffiti art by Banksy: amodal boundary completion; spatial impenetrability.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p161fig04.37 Boundary Contour System model: analog-sensitive boundary completion Kanizsas.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p162fig04.38 Cooperation and competition during grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p164fig04.40 Koffka-Benussi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p165fig04.41 Kanizsa-Minguzzi ring.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p166fig04.42 Computer simulation of Kanizsa-Minguzzi ring percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p167fig04.43 T-junction sensitivity: image, Bipole cells, boundary.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.44 main [boundary, surface] formation stages: LGN-> V1-> V2-> V4.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p168fig04.45 ON and OFF feature contours: filled-in regions when adjacent to boundary.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p170fig04.46 regions can fill-in feature contour inputs when [adjacent to, collinear with] boundary contour inputs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p170fig04.47 A double-opponent network processes output signals from FIDOs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.48 closed boundaries -> filling-in; open boundaries -> color spread.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p171fig04.49 DaVinci stereopsis and occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p173fig04.50 closed boundary at prescribed depth: addition of [bi, mon]ocular boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p176fig04.53 OC-OS [within position, across depth]: brighter Kanizsas look closer.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p178fig04.54 figure-ground separation: bipole cooperation and competition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p178fig04.55 Amodal completion of boundaries and surfaces in V2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p179fig04.56 Visible surface 3D perception: boundary enrichment, surface filling-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p182fig04.58 LAMINART explains many percepts of transparency.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.01 Learn many-to-one (compression, naming), one-to-many (expert knowledge) maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.02 Many-to-one map, two stage compression: [visual, auditory] categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p186fig05.03 Many-to-one map: IF-THEN rules: [symptom, test, treatment]s; length of stay.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p189fig05.04 hippocampus & several brain regions [learn, remember] throughout life.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.05 LGN [ON, OFF] cells respond differently to [side, end]s of lines.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.07 detailed connections between [retinal ganglion cells, LGN, V1].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p193fig05.08 LGN [activation, inhibition], with[, out] top-down feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p194fig05.09 [feature, boundary] contours from Ehrenstein disk stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p198fig05.10 Competitive learning and Self-Organized Maps (SOMs).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p199fig05.11 Instar learning: bottom-up adaptive filter for feature patterns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.12 Duality of [outstar, instar] networks.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.14 Outstar learning, both [in, de]creases for LTM to learn STM pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p201fig05.15 Spatial learning pattern, outstar learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.16 Geometry of choice and learning, classifying vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p202fig05.17 Geometry of choice and learning, trains the closest LTM vector.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p205fig05.18 catastrophic forgetting due to [competition, associative] learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.20 [PN match, N200 mismatch] computationally complementary potentials.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p213fig05.22 If inputs incorrectly activate a category, how to correct the error.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p213fig05.23 A [category, symbol, other] cannot determine whether an error has occurred.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.24 Learning top-down expectations occurs during bottom-up learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.25 Error correction: [learn, compare] TD-BU inputs, Processing Negativity ERP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p214fig05.26 Mismatch triggers nonspecific arousal, N200 ERP from orienting system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.27 Every event has [specific attentional cue, nonspecific orienting arousal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p220fig05.29 Vigilance [excitation: search better match, inhibition: resonance & learning].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.30 predictive error -> vigilance increase just enough -> minimax learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p224fig05.32 Learning the alphabet with two different levels of vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.33 Some early ARTMAP benchmark studies (no image - link instead).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p225fig05.34 ARTMAP learned maps of natural terrains better than AI expert systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p233fig05.42 Evidence for the [gamma, beta] prediction in 3 parts of the brain.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p236fig05.43 nucleus basalis of Meynert releases ACh, reduces AHP, increases vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p242fig05.46 Distributed ARTMAP variants learn the 5-4 category structure.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p245fig05.47 [long-range excitatory, short-range disynaptic inhibitory] connections realize the bipole grouping law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p252fig06.01 surface-shroud resonance forms as objects bid for spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p254fig06.03 ARTSCAN Search model learns to recognize and name invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p255fig06.04 The ARTSCAN Search for a desired target object in a scene: Wheres Waldo.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p257fig06.05 Spatial attention flows along object boundaries: Macaque V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.06 Neurophysiological data & simulation: attention can flow along a curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p258fig06.07 Top-down attentional spotlight becomes a shroud.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p259fig06.08 dARTSCN spatial attention hierarchy [Fast Where, Slow What] stream.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p260fig06.09 Crowding: visible objects & confused recognition, increased flanker spacing at higher eccentricity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p260fig06.10 cortical magnification transforms coordinates: artesian (retina) to log polar (V1).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p261fig06.11 Crowding: visible objects and confused recognition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p261fig06.12 A more serial search is needed due to overlapping conjunctions of features.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p265fig06.13 basal ganglia gate perceptual, cognitive, emotional, etc through parallel loops.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p267fig06.14 Perceptual consistency and figure-ground separation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p268fig06.15 saccades within an object: figure-ground outputs control eye movements via V3AA.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p270fig06.16 Predictive remapping of eye movements, from V3A to LIP.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p271fig06.17 Persistent activity in IT to [view, position, size]-invariant category learning by positional ARTSCAN.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.18 pARTSCAN: positionally-invariant object learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p272fig06.19 persistent activity needed to learn positionally-invariant object categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p273fig06.20 pARTSCAN simulation of Li & DiCarlo IT cell swapping data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.21 pARTSCAN [position invariance, selectivity] trade-off of Zoccolan etal 2007.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p274fig06.22 pARTSCAN: IT cortex processes image morphs with high vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.23 IT responses to image morphs, data vs model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p275fig06.24 Sterogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p276fig06.25 saccades: predictive gain fields [binocular fusion, filling-in of surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p277fig06.26 Predictive remapping maintains binocular boundary fusion as eyes move.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.27 knowing vs seeing resonances: What [knowing, feature-prototype], Where [seeing, surface-shroud].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p283fig07.01 Boundary competition: spatial habituative gates, orientation gated dipole, bipole grouping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p284fig07.02 Persistence decreases with flash illuminance & duration [data, simulations].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p285fig07.03 Persistence decrease: rebound to input offset inhibits bipole cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.04 Illusory contours persist longer than real contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p286fig07.05 Illusory contours inhibited by OFF cell rebounds, propagate to center.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.06 Persistence: [less, more] as adaptation orientation [same, orthogonal].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p287fig07.07 Persistence increases with distance, due to weaker spatial competition in hypercomplex cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p290fig08.01 Motion pools contrast-sensitive information moving in the same direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p291fig08.02 Complex cells respond to motion: opposite [direction, contrast polarities].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p292fig08.03 Visual aftereffects: [form- MacKay 90 degree, motion- waterfall  180].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p293fig08.04 Local vs overall motion: aperture problem of EVERY neurons receptive field.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p295fig08.05 sparse feature tracking signals [capture ambiguous, determine perceived] motion direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.06 Simplest example of apparent motion: two dots turning on and off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p296fig08.07 continuous motion illusions: [Beta with, Phi without] percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.08 Delta motion when [luminance, contrast] of flash 2 is larger than flash 1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p297fig08.09 motion in opposite directions perceived when 2 later flashes on either side of 1st flash.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.10 motion speed-up perceived when flash duration decreases.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p298fig08.11 illusory contours: double illusion in V1-V2, motion V2-MT interaction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.12 Single flash: Gaussian receptive fields, recurrent OC-OS winner-take-all.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.13 Nothing moves: [single flash, exponential decay], Gaussian peak fixed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p300fig08.14 Visual inertia: flash decay after the flash shuts off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p301fig08.15 two flashes: cell activation by first waning while second one is waxing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p301fig08.16 sum Gaussian flash activity profiles: [waning 1st, waxing 2nd] -> travelling wave.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p302fig08.17 maximum long-rang apparent motion: Gaussian kernel spans successive flashes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p302fig08.18 G-wave theorem 1: wave moves continuously IFF L <= 2*K.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.19 No motion vs motion at multiple scales: flash distance L, Gaussian width K.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p303fig08.20 G-wave theorem 2: [speed-up, scale] independent of [distance, scale size].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.21 Equal half-time property: multiple scales generate motion percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p304fig08.22 Korte Laws: ISIs in the hundreds of milliseconds can cause apparent motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p305fig08.23 Ternus motion: ISI [small- stationary, intermediate- element, larger- group].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p305fig08.24 Reverse-contrast Ternus motion: ISI [small- stationarity, intermediate- group (not element!), larger- group] motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p306fig08.25 Motion BCS model [explain, simulate]s long-range motion percepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p306fig08.26 3D FORMOTION model: track objects moving in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p307fig08.27 Ternus motion: [element- weak, group- strong] transients, element [visual persistence, perceived stationarity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p308fig08.28 Ternus group motion: Gaussian filter of 3 flashes forms one global maximum.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p310fig08.29 when individual component motions combine, their perceived direction & speed changes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.30 3D FORMOTION model: feature tracking [get directional, inhibit inconsistent] signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p311fig08.31 Motion BCS stages: locally ambiguous motion signals -> globally coherent percept, solving the aperture problem.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p312fig08.32 Schematic of motion filtering circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p312fig08.33 Processing motion signals by a population of speed-tuned neurons.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p314fig08.34 VISTARS navigation model: FORMOTION front end for navigational circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p315fig08.35 How to select correct direction and preserve speed estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p316fig08.36 Motion capture by directional grouping feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p317fig08.37 Motion capture by directional grouping feedback: [short, long]-range filters, transient cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p319fig08.38 Solving the aperture problem takes time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p320fig08.39 Simulation of the barberpole illusion direction field at two times.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p321fig08.40 [, in]visible occluders [do, not] capture boundaries they share with moving edges.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p322fig08.41 motion transparency: asymmetry [near, far], competing opposite directions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p323fig08.42 Chopsticks: motion separation in depth via [, in]visible occluders [display, percept].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p324fig08.43 ambiguous X-junction motion: MT-MST directional grouping bridges the ambiguous position.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p325fig08.44 The role of MT-V1 feedback:  [motion-form feedback, bipole boundary completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p325fig08.45 Closing formotion feedback loop [MT, MST]-to-V1-to-V2-to-[MT, MST].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p326fig08.46 How do we perceive relative motion of object parts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p327fig08.47 Two classical examples of part motion: Symmetrically moving inducers; Duncker wheel.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p328fig08.48 vector decomposition: (retinal - common = part) motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p328fig08.49 What is the mechanism of vector decomposition, prediction: directional peak shift.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p329fig08.50 How is common motion direction computed?  retinal motion->  bipole grouping (form stream)->  V2-MT formotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p329fig08.51 Large and small scale boundaries differentially form illusory contours.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.52 Correct motion directions after the peak shift top-down expectation acts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p330fig08.53 Simulation of the various directional signals of the left dot through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.54 Motion directions of a single dot moving slowly along a cycloid curve through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p331fig08.55 Duncker Wheel, large: stable rightward motion at the center captures motion at the rim.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p332fig08.56 Duncker Wheel, small: wheel motion as seen when directions are collapsed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p332fig08.57 MODE (MOtion DEcision) model: Motion BCS -> saccadic target selection -> basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p333fig08.58 LIP responses during RT task correct trials: coherence and [activation, inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.59 LIP responses for FD task: predictiveness decreases with increasing coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p334fig08.60 [RT, FD] task behavioral data: more coherence in the motion causes more accurate decisions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.61 RT task behavioural data: reach time (ms) vs % coherence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p335fig08.62 LIP encodes not only where, but also when, to move the eyes - No Bayes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p338fig09.01 optic flow through brain regions: moving observer [navigate, track] moving object.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p338fig09.02 Heading (focus of velocity field) from optic flow: humans accurate +- 1 to 2 degrees.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p339fig09.03 Heading with [body move, eye rotate, combined] -> optic flow [expand, translate, rotate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p339fig09.04 How can translation flow (eye rotation) be subtracted from spiral flow to recover the expansion flow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.05 efference copy command: may use outflow movement commands to eye muscles.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.06 Corollary discharges from outflow movement commands that move muscles.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p340fig09.07 Log polar remapping of optic flow:  [expansion, circular] motion maps to single direction.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p341fig09.08 optic flows [retina, V1, MT, MSTd, parietal cortex], V1 log polar mapping.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p341fig09.09 MSTd cells are sensitive to [spiral, rotation, expansion] motion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.10 Retina -> log polar -> MSTd cell, heading eccentricity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p342fig09.11 importance of efference copy in real movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.12 two retinal views of the Simpsons: [separate, recognize] overlapping figures.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p343fig09.13 How do our brains figure out which views belong to which pear.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p344fig09.14 Heading sensitivity unimpaired: MT tuning width 38°, MSTd spiral tuning 61°.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p345fig09.15 MT double opponent directional fields: relative motions [objects, backgrounds].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p346fig09.16 macrocircuit of 13 brain regions used to move the eyes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p347fig09.17 leftward eye movement model: retina-> MT-> MST[v,d]-> pursuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p347fig09.18 MST[v,d] circuits enable predictive target tracking by the pursuit system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p348fig09.19 MSTv cells: target speed on retina, background speed on retina, pursuit speed command.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.20 Steering from optic flow: goals are attractors, obstacles are repellers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p349fig09.21 Steering dynamics goal approach: [obstacle, goal, heading] -> steering.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.22 negative Gaussian of an obstacle: avoid obstacle without losing sight of goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p350fig09.23 Unidirectional transient cells: [lead, trail]ing boundaries, driving video.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.24 Directional transient cells respond most to motion in their preferred directions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p351fig09.25 M+ computes global motion estimate from noisy local motion estimates.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p352fig09.26 heading direction final stage: beautiful optic flow, accuracy matches humans.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p355fig10.02 seeing vs knowing distinction is difficult because they interact so strongly.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.05 Activation of V1 by direct excitatory signals from LGN to layer 4 of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.06 Why another layer 6-to-4 signal: on-center off-surround.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p360fig10.09 Grouping starts in layer 2-3: long-range horizontal excitation, short-range inhibition of target pyramidal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p361fig10.10 Bipole property controls perceptual grouping: inputs [excitatory sum, inhibitory normalize].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p362fig10.11 Final grouping: folded feedback, strongest enhanced on-center, weaker suppressed off-surround, interlaminar functional columns.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p363fig10.12 V2 repeats V1 circuitry at larger spatial scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p364fig10.14 Explanation: grouping and attention share the same modulatory decision circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.15 Attention protects target from masking stimulus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p367fig10.16 Flankers can enhance or suppress targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.17 Attention has greater effect on low contrast targets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p368fig10.18 Texture reduces response to a bar: [iso-orientation, perpendicular] suppression.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p369fig10.19 Unconscious learning of motion direction, without [extra-foveal attention, awareness] of stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p371fig11.01 FACADE theory explains how the 3D boundaries and surfaces are formed to see the world in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p372fig11.02 3D surface filling-in of [lightness, color, depth] by a single process: FACADE.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p373fig11.03 Both [contrast-specific binocular fusion, contrast-invariant boundary perception] are needed to see the world in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p374fig11.04 Three processing stages of [monocular simple, complex] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p374fig11.05 Contrast constraint on binocular fusion: only contrasts which are derived from the same objects in space are binoculary matched.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.06 Binocular fusion by obligate cells in V1-3B when =[left,right] contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.08 Correspondance problem: How does the brain inhibit false matches. contrast constraint not enough.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.09 V2 disparity filter solves correspondence problem: false matches suppressed by line-of-sight inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p377fig11.11 DaVinci stereopsis: monocular information and depth percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p378fig11.12 3D LAMINART: V2 monocular+binocular line of sight inputs -> depth perception.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p381fig11.15 DaVinci stereopsis variant of (Gillam, Blackburn, Nakayama 1999): same mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p382fig11.16 DaVinci stereopsis of [3 narrow, one thick] rectangles: same explanation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p383fig11.17 Venetian blind effect: [left, right] eye matching bars.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p384fig11.18 Venetian blind effect: Surface[, -to-boundary] surface contour signals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p385fig11.19 Dichoptic masking: [left, right] images have sufficiently different contrasts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p385fig11.20 Dichoptic masking, Panum's limiting case: simplified version of Venetian blind effect.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p386fig11.21 Craik-O'Brien-Cornsweet Effect: 2D surface at a very near depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p387fig11.22 Julesz stereogram: boundaries with[out, ] surface contour feedback.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p388fig11.23 Sparse stereogram, large regions of ambiguous white: correct surface in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p388fig11.24 depth-ambiguous feature contours: boundary groups lift to correct surface in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p389fig11.25 Boundaries: not just edge detectors, or a shaded ellipse would look [flat, uniformly gray].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p390fig11.26 Multiple-scale depth-selective groupings determine perceived depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p391fig11.27 Multiple-scale grouping and size-disparity correlation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p391fig11.28 Ocular dominance columns, LGN mappings into layer 4C of V1.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p392fig11.29 3D vision figure-ground separation: multiple-scale, depth-selective boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p392fig11.30 How multiple scales vote for multiple depths, scale-to-depth and depth-to-scale maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.31 LIGHTSHAFT model: determining depth-from-texture percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p393fig11.32 Kulikowski stereograms: binocular matching of out-of-phase [Gaussians, rectangles].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p394fig11.33 Kaufman stereogram: simultaneous fusion and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p395fig11.34 3D LAMINART vs 7 other rivalry models: stable vision and rivalry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p396fig11.35 Three properties of bipole boundary grouping in V2: boundaries oscillate with rivalry-inducing stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p397fig11.36 temporal dynamics of [rivalrous, coherent] boundary switching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p398fig11.37 Simulation of the no swap baseline condition (Logothetis, Leopold, Sheinberg 1996).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.38 Simulation of the swap condition of (Logothetis, Leopold, Sheinberg 1996).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p399fig11.39 Simulation of the eye rivalry data of (Lee, Blake 1999).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p400fig11.40 How do ambiguous 2D shapes contextually define a 3D object form.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.41 3D LAMINART: [angle, disparity-gradient] cells learn 3D representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p401fig11.42 hypothetical cortical hypercolumn: how [angle, disparity-gradient] cells may self-organize during development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.43 A pair of disparate images of a scene from the University of Tsukuba.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p402fig11.44 3D LAMINART disparities [5, 6, 8, 10, 11, 14]: images of objects in common depth planes.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p403fig11.45 SAR processing by multiple scales: reconstruction of a SAR image.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p405fig12.01 [What ventral, Where-How dorsal] cortical streams for [audition, vision].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p406fig12.02 Three S's of movement: Synergy formation, muscle Synchrony, volitional Speed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p407fig12.03 Motor cortical cells: vectors for [direction, length] of commanded movement.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p409fig12.04 VITE simulations: difference vector emergent from network interactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.05 VITE: velocity profile invariance [short, long] movements for same GO signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p410fig12.06 Monkeys transform movement: 2 -> 10 o'clock target, 50 or 100 msec after activation of 2 o'clock target.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p411fig12.07 VITE: higher peak velocity due to target switching.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p411fig12.08 GO signals gate agonist-antagonist [difference, present position] vector processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p412fig12.09 Vector Associative Map: difference vector mismatch learning calibrates [target, present] position vectors.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p413fig12.10 VITE: cortical area [4,5] combine [trajectory, inflow] signals from [spinal cord, cerebellum] for [variable loads, obstacles].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p414fig12.11 [data, simulation]s from cortical areas 4 and 5 during a reach.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p415fig12.12 [VITE, FLETE, cerebellar, opponent muscle] model for trajectory formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.13 DIRECT model: Endogenous Random Generator learns volitional reaches.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p416fig12.14 DIRECT reaches [unconstrained, with TOOL, elbow@140°, blind].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p417fig12.15 From Seeing & Reaching (DIRECT) to Hearing & Speaking (DIVA): homologous circular reactions, [tool use, coarticulation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p418fig12.16 Anatomy of DIVA model processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p422fig12.19 ARTSTREAM: derive streams from [pitch, source direction].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.21 Pitch shifts with component shifts, pitch vs lowest harmonic number.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p424fig12.22 Decomposition of a sound in terms of three of its harmonics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p426fig12.24 Spectrograms of -ba- and -pa- show the transient and sustained parts of their spectrograms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p428fig12.25 ARTSPEECH: auditory-articulatory feedback loop & imitative map, [auditory, motor] dimensionally consistent, motor theory of speech.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p430fig12.26 NormNet: speaker normalization via specializations of mechanisms for auditory streams.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p431fig12.27 ARTSTREAM & NormNet strip maps: variants of occular dominance columns in visual cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p432fig12.28 SpaN: spatial representations of numerical quantities in the parietal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p433fig12.29 What stream: place-value [number map, language category]s; to Where stream: numerical strip maps.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p436fig12.31 Working memory: temporal order STM is often imperfect, then stored in LTM.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.32 Free recall bowed serial position curve.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p437fig12.33 Working memory models: item and order, or competitive queuing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p438fig12.34 LTM Invariance Principle: [STM, LTM] new words must not cause catastrophic forgetting of subwords.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p439fig12.35 Normalization Rule: total activity of working memory has upper bound independent of number of items.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p439fig12.36 [Item, Order] working memories: [content-addressable categories, temporal order, [excitatory, inhibitory] recurrence, rehearsal wave.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p440fig12.37 Normalization Rule: primacy bow as more items stored.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p441fig12.38 LTM Invariance Principle: new events do not change the relative activities of past event sequences.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p442fig12.39 [LTM invariance, Normalization Rule] Shunt normalization -> STM bow.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p442fig12.40 [LTM Invariance, normalization, STM steady attention]: only [primacy, bowed] gradients of activity can be stored.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p443fig12.41 Neurophysiology of sequential copying: [primacy gradient, self-inhibition].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p447fig12.45 LIST PARSE: order errors vs serial position with extended pauses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p448fig12.46 Masking Field working memory is a multiple-scale self-similar recurrent shunting on-center off-surround network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p449fig12.47 Masking Field self-similar [recurrent inhibitory, top-down excitatory] signals to the item chunk working memory.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p452fig12.48 Perceptual integration of acoustic cues: [silence vs noise] durations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.49 ARTWORD: acoustic cues, phonetic [features, WM], Masking Field unitized lists, gain control.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p453fig12.50 ARTWORD perception cycle: sequences-> chunks-> compete-> top-down expectations-> item working memory->  develops item-list resonance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p454fig12.51 Resonant transfer: as silence interval increases, a delayed additional item can facilitate perception of a longer list.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p455fig12.52 cARTWORD dynamics 1-2-3: resonant activity in item and feature layers corresponds to conscious speech percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.53 cARTWORD dynamics 1-silence-3: Gap in resonant activity of 1-silence-3 in [item, feature] layers corresponds to perceived silence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p456fig12.54 cARTWORD dynamics: 1-noise-3: Resonance of 1-2-3 in [item, feature] layers  restores item 2.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p457fig12.55 cARTWORD dynamics 1-noise-5: Figures 12.[54, 55] future context can disambiguate past noisy sequences that are otherwise identical.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p459fig12.56 Rank information on the position of an item in a list using numerical hypercolumns in the prefrontal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p460fig12.57 lisTELOS for saccades: prototype to [store, recall] other [cognitive, spatial, motor] information.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p461fig12.58 lisTELOS shows [BG nigro-[thalamic, collicular], FEF, ITa, PFC, PNR-THAL, PPC, SEF, SC, V1, V4-ITp, Visual Cortex input] and [GABA].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p462fig12.59 TELOS: balancing reactive vs. planned movements.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p463fig12.60 Rank-related activity in PFC and SEF from two different experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p464fig12.61 SEF saccades microstimulating electrode: spatial gradient of habituation alters order, but not which, saccades are performed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p464fig12.62 The most habituated position is foveated last: because stimulation spreads in all directions, saccade trajectories tend to converge.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p465fig12.63 lisTELOS and data: microstimulation biases selection so saccade trajectories converge toward a single location in space.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p467fig12.64 Some of the auditory cortical regions that respond to sustained or transient sounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p468fig12.65 [PHONET, ARTPHONE] linguistic properties: creates rate-invariant representations for variable-rate speech, paradoxical VC-CV category boundaries.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.66 PHONET: relative duration of [consonant, vowel] pairs can [preserve, change] a percept.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p469fig12.67 PHONET [transient, sustained] cells that respond to certain [consonant transient, sustained vowel] sounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p471fig12.68 Mismatch vs resonant fusion: effect of silence interval length.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p473fig12.69 ART Matching Rule properties explain error rate and mean reaction time (RT) data from lexical decision experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p474fig12.70 macrocircuit model to explain lexical decision task data.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p476fig12.71 Word frequency data model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p481fig13.01 Cognitive-Emotional-Motor (CogEM): macrocircuit of [function, anatomy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.03 CogEM: supported by anatomical connections [[sensory, orbitofrontal] cortices, amygdala].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.05 Classical conditioning: perhaps simplest kind of associative learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.06 Classical conditioning: inverted-U vs InterStimulus Interval (ISI).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p485fig13.07 Paradigm of secondary conditioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.08 Blocking paradigm: cues lacking different consequences may fail to be attended.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.09 Equally salient cues can be conditioned in parallel to an emotional consequence.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p486fig13.10 Blocking: both [secondary, attenuation of] conditioning at zero ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p487fig13.11 CogEM : three main properties to explain how attentional blocking occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p488fig13.12 Motivational feedback and blocking.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p489fig13.13 CogEM and conditioning: positive ISI; inverted-U vs ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.14 Cognitive-Emotional circuit: for proper conditioning, sensory needs >= 2 processing stages.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p492fig13.16 Polyvalent CS sampling and US-activated nonspecific arousal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p493fig13.17 Learning nonspecific arousal and CR read-out.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.18 Learning to control nonspecific arousal and read-out of the CR: two stages of CS.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p494fig13.19 CogEM: secondary conditioning of [arousal, response], multiple [drive, input]s, motivational sets.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p496fig13.20 A single avalanche sampling cell can learn an arbitrary space-time pattern.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p497fig13.21 nonspecific arousal: primitive crayfish swimmerets, songbird pattern generator avalanche.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p499fig13.23 Self-organizing avalanches [instars filter, serial learning, outstars read-out], Serial list learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p500fig13.24 Primary [excitatory, inhibitory] conditioning using opponent processes and their antagonistic rebounds.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.25 Unbiased transducer in finite rate physical process: mass action by a chemical transmitter is the result.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p501fig13.26 Transmitter y [accumulation, release]:  y restored < infinite rate, evolution has exploited this.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p502fig13.27 Transmitter minor mathematical miracle [accumulation, release]: S*y = S*A*B div (A + S) (gate, mass action).png
/home/bill/web/Neural nets/Grossberg/images- captioned/p502fig13.28 Habituative transmitter gate: fast [increment, decrement]s of input lead to [overshoot, habituation, undershoot]s, Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p503fig13.29 ON response to phasic ON input has Weber Law properties due to the habituative transmitter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.30 OFF-rebound transient due to phasic input offset: arousal level sets ratio ON vs OFF rebounds, Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p504fig13.31 Behavioral contrast rebounds: decrease [food-> negative Frustration, shock-> positive Relief] reinforcers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.32 Behavioral contrast: [response suppression, antagonist rebound] both calibrated by shock levels.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p505fig13.33 Novelty reset- rebound to arousal onset: equilibrate to [I, J]; keep phasic input J fixed; interpret this equation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p506fig13.34 Novelty reset: rebound to arousal onset, reset of dipole field by unexpected event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p506fig13.35 Shock [cognitive, emotional] effects: [reinforcer, sensory cue, expectancy].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p509fig13.36 Life-long learning: selective without [passive forgetting, associative saturation].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.37 A disconfirmed expectation inhibits prior incentive, but is insufficient to prevent associative saturation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.38 Dissociation of LTM read-[out, in]: dendritic action potentials as teaching signals, early predictions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p510fig13.39 Learn net dipole output pattern: [shunting competition, informational noise suppression] in affective gated dipoles, back-propagation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p512fig13.40 Conditioned excitor extinguishes: [learning, forgetting] phases, shock expectation disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.41 Conditioned inhibitor does not extinguish: [learn, forget] phases, same [CS, teacher] can be used.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.42 Conditioned excitor extinguishes when expectation of shock is disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p513fig13.43 Conditioned excitor extinguishes: expectation that -no shock- follows CS2 is NOT disconfirmed.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p514fig13.44 Analog of the COgEM model maps of [object X, proto-self], assembly of second-order map.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p519fig14.01 Coronal sections of prefrontal cortex.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p524fig14.04 Basal ganglia circuit for dopaminergic Now Print signals from the substantia nigra pars compacta in response to unexpected rewards.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p530fig14.05 Visual [pop-out, search]-> reaction time experiments.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.06 ARTSCENE: classification of scenic properties as texture categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p531fig14.07 ARTSCENE voting achieves even better prediction of scene type.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p533fig14.09 ARTSCENE search [data, simulation]s for 6 pairs of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p540fig15.01 [Delay, trace conditioning] paradigms:  require a CS memory trace over the ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.03 Timed responses from adaptively timed conditioning: Weber laws, inverted U as a function of ISI.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p542fig15.04 blinks of [nictitating membrane, eyelid] are adaptively timed: closure occurs at arrival of the US following the CS, obeys Weber Law.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.05 Learning with two ISIs: each peak obeys Weber Law, strong evidence for spectral learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p543fig15.06 Circuit between [dentate granule, CA1 hippocampal pyramid] cells seems to compute spectrally timed responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.07 Spectral timing: STM sensory representation-> Spectral activation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.08 Habituative transmitter gate: spectral activities-> sigmoid signals-> gated by habituative transmitters.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p544fig15.09 Habituative transmitter gate: increases with accumulation, decreases from gated inactivation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p545fig15.10 A timed spectrum of gated sampling intervals.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p545fig15.11  Associative learning, gated steepest descent learning: output from each population is a doubly gated signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p546fig15.12 Computer simulation of spectral learning: fastest with large sampling signals when the US occurs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p546fig15.13  Adaptive timing is a population property, random spectrum of rates achieves good collective timing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p547fig15.14 [Un, ]expected non-occurences of goal: a predictive failure leads to: Orienting Reactions, Emotional- Frustration,  Motor- Explorator.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p547fig15.15  Expected non-occurrence of goal: some rewards are reliable but delayed in time, do not lead to orienting reactions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.16 Homolog between ART and CogEM model: complementary systems.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p548fig15.17 The timing paradox: want [accurate timing, to inhibit exploratory behaviour throught ISI].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.18 Weber Law: reconciling accurate and distributed timing, different ISIs-  standard deviation = peak time, Weber law rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p549fig15.19 Conditioning, Attention, and Timing circuit: Hippocampus spectrum-> Amgdala orienting system-> neocortex motivational attention.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p550fig15.20 Adaptively timed Long Term Depression between parallel fibres and Purkinje cells-> movement gains within learned time interval.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p551fig15.21 Cerebellum: important cells types and circuitry.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p551fig15.22 Responses of a turtle retinal cone to brief flashes of light of increasing intensity.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p552fig15.23 Cerebellar biochemistry: mGluR supports adaptively timed conditioning at cerebellar Purkinje cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p556fig15.24 Cerebellar cortex responses: [data, model] short latency responses after lesioning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.25 Computer simulations of adaptively timed [LTD at Purkinje cells, activation of cereballar nuclear cells].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p557fig15.26 Brain [region, process]s that contribute to autistic behavioral symptoms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.27 Spectrally timed SNc learning: brain [region, process]s release of dopaminergic signals, unexpected reinforcing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p559fig15.28 Neurophysiological data and simulations of SNc responses.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.29 Excitatory pathways that support activation of the SNc by a US and the conditioning of a CS to the US.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p560fig15.30 Inhibitory pathway: striosomal cells predict [timing, magnitude] of reward signal to cancel it.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.31 Expectation timing: timing spectrum, striosomal cells delayed transient signals, gate [learning, read-out].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p561fig15.32 Inhibitory pathway expectation magnitude: is a negative feedback control system for learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.33 MOTIVATOR: thalamocortical loops through basal ganglia.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p563fig15.34 Distinct basal ganglia zones for each loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p564fig15.35 GO signal to recurrent shunting OC-OS networks: control of the [fore, hind] limbs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p565fig15.36 (a) FOVEATE: control of saccadic eye movements within the peri-pontine reticular formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p566fig15.37 FOVEATE: steps in generation of a saccade.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p567fig15.38 Gated Pacemaker of [diurnal, nocturnal] circadian rythms: whether phasic light turns the pacemaker on or off.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p568fig15.39 MOTIVATOR hypothalamic gated dipoles: inputs, [object, value, object-value] categories, reward expectation filter.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p569fig15.40 GO and STOP movement signals: control by [direct, indirect] basal ganglia circuits.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p573fig16.01 Hippocampal place cells: discovery from rat [experimental chamber, neurophysiological recordings].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p574fig16.02 Neurophysiological recordings of 18 different place cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p575fig16.03 Rat navigation: firing patterns of [hippocampal place, entrorhinal grid] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p578fig16.04 Cross-sections of the hippocampal regions and the inputs to them.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p580fig16.05 GridPlaceMap hierarchy of SOMs with identical equations: learns 2D [grid, place] cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p581fig16.06 Trigonometry of spatial navigation: coactivation of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.07 Stripe cells multiple [orientation, phase, scale]s: directionally-sensitive ring attractors, velocity, distance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p582fig16.08 Evidence for stripe-like cells: entorhinal cortex data, Band Cells position from grid cell oscillatory interference.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.09 GRIDSmap: stripe cells for rat trajectories, self-organizing map learned hexagonal grid cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p583fig16.10 GRIDSmap embedded into hierarchy of SOMs: [angular head velocity, linear velocity] signals to place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.11 GRIDSmap learning of hexagonal grid fields, multiple phases per scale.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p584fig16.12 Temporal development of grid fields: orientations rotate to align with each other.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.13 Hexagonal grid cell receptive fields: somewhat insensitive to [number, directional selectivities] of stripe cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p585fig16.14 GRIDSmap: Superimposed firing of stripe cells supports learning hexagonal grid.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.15 Why is a hexagonal grid favored: stripe cells at intervals of 45 degrees, GRIDSmap does not learn, oscillatory interference does.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p586fig16.16 Grid-to-place SOM: formation of place cell fields via grid-to-place cell learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p587fig16.17 A refined analysis: SOM amplifies most frequent and energetic coactivations, stripe fields separated by [90°, 60°].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p588fig16.18 GridPlaceMap hierarchy of SOMs: coordinated learning of [grid, place, inomodal] cell receptive fields.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p589fig16.19 How does the spatial scale increase along the MEC dorsoventral axis.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p590fig16.20 Dorsoventral gradient in the rate of synaptic integration of MEC layer II stellate cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p590fig16.21 Frequency of membrane potential oscillations in grid cells decreases along the dorsoventral gradient of the MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p591fig16.22 Dorsoventral [time constant, duration] gradients in AHP kinetics of MEC layer II stellate cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p591fig16.23 Spectral spacing model: map cells respond to stripe cell inputs of multiple scales, How do entorhinal cells solve the scale selection problem.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p592fig16.24 Parameter settings in the Spectral Spacing Model that were used in simulations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.25 Spectral Spacing Model equations for [STM, MTM, LTM].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p593fig16.26 Gradient of grid spacing along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p594fig16.27 Gradient of field width along dorsoventral axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p595fig16.28 Peak and mean rates at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.29 Subthreshold membrane mV oscillations: decreasing Hz at different locations along DV axis of MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p596fig16.30 Spatial phases of learned grid and place cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.31 Multimodal place cell firing in large spaces.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p597fig16.32 Model fits data about grid cell development in juvenile rats:  grid [score increases, spacing flat].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.33 Model fits [place, grid, directional] cell data about grid cell development in juvenile rats:  [spatial information, inter-trial stability] vs postnatal day.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p598fig16.34 spiking GridPlaceMap: generates theta-modulated place and grid cell firing, unlike the rate-based model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p599fig16.35 anatomically overlapping grid cell modules: effects of [different modules in one animal, DV location, response rate].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p602fig16.37 Hippocampal inactivation by muscimol disrupts grid cells.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p603fig16.38 Role of hippocampal feedback in maintaining grid fields, muscimol inhibition.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p605fig16.39 Disruptive effects of MS inactivation in MEC.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p607fig16.40 Effects of medial septum (MS) inactivation on grid cells: data, simulations, gridness.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p611fig16.41 back-propagating action potentials, recurrent inhibitory interneurons: control learning, regulate rythm- read-out is dissociated from read-in.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p612fig16.42 Macrocircuit of the main SOVEREIGN subsystems: visual, motor.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.44 SOVEREIGN[target position, difference] vectors, volitional GO computations] to control decision-making and action.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p614fig16.45 [distance, angle] computations learn dimensionally-consistent [visual, motor] information for [decision, action]s.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p615fig16.46 SOVEREIGN uses homologous processing stages to model the [What, Where] cortical streams, motivational mechanisms.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p615fig16.47 SOVEREIGN: multiple parallel READ circuits, sensory-drive heterarchy amplifies motivationally favored option.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p616fig16.48 SOVEREIGN tests using virtual reality 3D rendering of a cross maze.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p616fig16.49 SOVEREIGN  animat converted inefficient exploration into an efficient direct learned path to the goal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p617fig16.50 Spectral Spacing models of [perirhinal what, parahippocampal where] inputs, fused in the hippocampus.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p627tbl17.01 Homologs between [reaction-diffusion, recurrent shunting cellular network] models of development.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p628fig17.01 A hydra.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p628fig17.02 how different [cut, graft]s of the normal Hydra may [, not] lead to the growth of a new head.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p629fig17.03 How an initial morphogenetic gradient may be contrast enhanced to exceed the threshold for head formation.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p630fig17.04 Morphogenesis: use cellular models vs [chemical, fluid] reaction-diffusion models.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p631fig17.05 How a blastula develops into a gastrula.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p634fig17.06 How binary cells with a Gaussian distribution of output thresholds generates a sigmoidal population signal.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p057fig02.03 neuron basic [anatomy, physiology].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p075fig02.24 Membrane equations of physiology: shunting equation, not additive.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p107fig03.26 drawing directly in color leads to colored surface representations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p090fig03.03 Kanizsa square and reverse-contrast Kanizsa square precepts.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p096fig03.11 Another example of neon color spreading.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p124fig04.02 closed boundaries prevent brightness from flowing.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p156fig04.33 emergent boundary groupings can segregate textured regions.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p153fig04.32 Double filter and grouping network.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p003fig01.03 Amodal completion.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.png
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#Early ARTMAP benchmark studies
/home/bill/web/Neural nets/Grossberg/images- captioned/p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p167fig04.43 T-junction sensitivity: image, Bipole cells, boundary.png
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#Introduction
#Grossberg: why ART is relevant to consciousness in Transformer NNs
#Grossberg's [non-linear DEs, CogEm, CLEARS, ART, LAMINART, cART] models
#1958-59 ?among the world's first? systems of non-linear differential equations for NNs
#?date? CogEm Cognitive-Emotional model
#?date? CLEARS [Cognition, Learning, Expectation, Attention, Resonance, Synchrony]
#1976 ART Adaptive Resonance Theory
#?date? LAMINART Laminar computing ART
#?date? cART conciousness ART
#The underlying basis in [bio, psycho]logical data
#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#The underlying basis in [bio, psycho]logical data
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
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/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
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#Introduction
#Grossberg: why ART is relevant to consciousness in Transformer NNs
#Grossberg's [non-linear DEs, CogEm, CLEARS, ART, LAMINART, cART] models
#1958-59 ?among the world's first? systems of non-linear differential equations for NNs
#?date? CogEm Cognitive-Emotional model
#?date? CLEARS [Cognition, Learning, Expectation, Attention, Resonance, Synchrony]
#1976 ART Adaptive Resonance Theory
#?date? LAMINART Laminar computing ART
#?date? cART conciousness ART
#The underlying basis in [bio, psycho]logical data
#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#The underlying basis in [bio, psycho]logical data
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
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/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Introduction
#Grossberg: why ART is relevant to consciousness in Transformer NNs
#Grossberg's [non-linear DEs, CogEm, CLEARS, ART, LAMINART, cART] models
#1958-59 ?among the world's first? systems of non-linear differential equations for NNs
#?date? CogEm Cognitive-Emotional model
#?date? CLEARS [Cognition, Learning, Expectation, Attention, Resonance, Synchrony]
#1976 ART Adaptive Resonance Theory
#?date? LAMINART Laminar computing ART
#?date? cART conciousness ART
#The underlying basis in [bio, psycho]logical data
#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#The underlying basis in [bio, psycho]logical data
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
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/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Introduction
#Grossberg: why ART is relevant to consciousness in Transformer NNs
#Grossberg's [non-linear DEs, CogEm, CLEARS, ART, LAMINART, cART] models
#1958-59 ?among the world's first? systems of non-linear differential equations for NNs
#?date? CogEm Cognitive-Emotional model
#?date? CLEARS [Cognition, Learning, Expectation, Attention, Resonance, Synchrony]
#1976 ART Adaptive Resonance Theory
#?date? LAMINART Laminar computing ART
#?date? cART conciousness ART
#The underlying basis in [bio, psycho]logical data
#Credibility from non-[bio, psycho]logical applications of Grossberg's ART
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html#The underlying basis in [bio, psycho]logical data
/home/bill/web/Neural nets/consciousness/Introduction.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
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#Introduction
#Co-evolution of membrane equations and on-center-off-surround anatomies?
#Cambrian Period explosion of [complex, diverse] multicellular life
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#Introduction
#Co-evolution of membrane equations and on-center-off-surround anatomies?
#Cambrian Period explosion of [complex, diverse] multicellular life
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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#Introduction
#Co-evolution of membrane equations and on-center-off-surround anatomies?
#Cambrian Period explosion of [complex, diverse] multicellular life
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopStatus.html#Grossberg
/home/bill/web/webWork/pMenuTopCopyright.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Introduction
#Co-evolution of membrane equations and on-center-off-surround anatomies?
#Cambrian Period explosion of [complex, diverse] multicellular life
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopStatus.html#Grossberg
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#Grossberg OR[anticipated, predicted, unified] the [experimental result, model]s
#What is consciousness?
#How does one evolve a computational brain?
#Grossberg's comments for some well-known consciousness theories
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
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/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Grossberg OR[anticipated, predicted, unified] the [experimental result, model]s
#What is consciousness?
#How does one evolve a computational brain?
#Grossberg's comments for some well-known consciousness theories
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopStatus.html#Grossberg
/home/bill/web/webWork/pMenuTopCopyright.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Grossberg OR[anticipated, predicted, unified] the [experimental result, model]s
#What is consciousness?
#How does one evolve a computational brain?
#Grossberg's comments for some well-known consciousness theories
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopStatus.html#Grossberg
/home/bill/web/webWork/pMenuTopCopyright.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Grossberg OR[anticipated, predicted, unified] the [experimental result, model]s
#What is consciousness?
#How does one evolve a computational brain?
#Grossberg's comments for some well-known consciousness theories
/home/bill/web/Neural nets/Grossberg/images- captioned/p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.png
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopCopyright.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Introduction
#Conventional theory - components of consciousness
#Grossberg & colleagues - 
#Which [module, modal architecture, process]s [are, are not] conscious?
#dorsal concious, ventral sub-concious
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
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/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Introduction
#Conventional theory - components of consciousness
#Grossberg & colleagues - 
#Which [module, modal architecture, process]s [are, are not] conscious?
#dorsal concious, ventral sub-concious
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopStatus.html#Grossberg
/home/bill/web/webWork/pMenuTopCopyright.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Introduction
#Conventional theory - components of consciousness
#Grossberg & colleagues - 
#Which [module, modal architecture, process]s [are, are not] conscious?
#dorsal concious, ventral sub-concious
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopStatus.html#Grossberg
/home/bill/web/webWork/pMenuTopCopyright.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#Introduction
#Conventional theory - components of consciousness
#Grossberg & colleagues - 
#Which [module, modal architecture, process]s [are, are not] conscious?
#dorsal concious, ventral sub-concious
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
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/home/bill/web/Neural nets/Grossberg/Howell notes: Grossberg 2025.HtmWeb.html#Table of Contents
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/home/bill/web/Neural nets/Grossberg/Howell notes: Grossberg 2025.HtmWeb.html#Table of Contents
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#???
#???
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 1st prize, Matt Pritchard- The Phantom Queen.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 2nd prize, Michael Cohen- The Changing Room Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 3rd prize, Dawei Bai & Brent Strickland - The Double Ring Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Pascal Wallisch & Michael Karlovich - Crocs & Socks.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Christopher Tyler and David Phillips - Emergent City Flyby with Colossus.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Olivier and Chloe Redon - Oh La La Box.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Yutaro Sato, Kento Imai and Kenri Kodaka - Slime Hand.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Kokichi Sugihara - Rising Object Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Michael Cheshire - Cubic Confusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Peter Veto - The Phantom Wiggle.ogv
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#???
#???
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 1st prize, Matt Pritchard- The Phantom Queen.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 2nd prize, Michael Cohen- The Changing Room Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 3rd prize, Dawei Bai & Brent Strickland - The Double Ring Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Pascal Wallisch & Michael Karlovich - Crocs & Socks.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Christopher Tyler and David Phillips - Emergent City Flyby with Colossus.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Olivier and Chloe Redon - Oh La La Box.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Yutaro Sato, Kento Imai and Kenri Kodaka - Slime Hand.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Kokichi Sugihara - Rising Object Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Michael Cheshire - Cubic Confusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Peter Veto - The Phantom Wiggle.ogv
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopStatus.html#Grossberg
/home/bill/web/webWork/pMenuTopCopyright.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#???
#???
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 1st prize, Matt Pritchard- The Phantom Queen.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 2nd prize, Michael Cohen- The Changing Room Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 3rd prize, Dawei Bai & Brent Strickland - The Double Ring Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Pascal Wallisch & Michael Karlovich - Crocs & Socks.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Christopher Tyler and David Phillips - Emergent City Flyby with Colossus.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Olivier and Chloe Redon - Oh La La Box.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Yutaro Sato, Kento Imai and Kenri Kodaka - Slime Hand.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Kokichi Sugihara - Rising Object Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Michael Cheshire - Cubic Confusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Peter Veto - The Phantom Wiggle.ogv
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopStatus.html#Grossberg
/home/bill/web/webWork/pMenuTopCopyright.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
#???
#???
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 1st prize, Matt Pritchard- The Phantom Queen.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 2nd prize, Michael Cohen- The Changing Room Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 3rd prize, Dawei Bai & Brent Strickland - The Double Ring Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Pascal Wallisch & Michael Karlovich - Crocs & Socks.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Christopher Tyler and David Phillips - Emergent City Flyby with Colossus.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Olivier and Chloe Redon - Oh La La Box.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Yutaro Sato, Kento Imai and Kenri Kodaka - Slime Hand.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Kokichi Sugihara - Rising Object Illusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Michael Cheshire - Cubic Confusion.ogv
/home/bill/web/Neural nets/Grossberg/IllusionOfTheYear/2021 Peter Veto - The Phantom Wiggle.ogv
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
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/home/bill/web/webWork/pMenuTopStatus.html#Grossberg
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/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#incorporate reader questions into theme webPage
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Navigation: [menu, link, directory]s
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/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
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/home/bill/web/webWork/pMenuTopHelp.html#Grossberg
/home/bill/web/webWork/pMenuTopHelp.html#incorporate reader questions into theme webPage
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html#Navigation: [menu, link, directory]s
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/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
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/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
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/home/bill/web/Neural nets/Grossberg/References/Sejnowski 21Aug2022 Large Language Models and the Reverse Turing Test.pdf
https://arxiv.org/abs/1706.03762
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https://rationalwiki.org/wiki/Talk:Quantum_consciousness
/home/bill/web/Neural nets/Grossberg/References/Sejnowski 21Aug2022 Large Language Models and the Reverse Turing Test.pdf
https://arxiv.org/abs/1706.03762
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https://rationalwiki.org/wiki/Quantum_consciousness
https://rationalwiki.org/wiki/Talk:Quantum_consciousness
/home/bill/web/Neural nets/Grossberg/References/Sejnowski 21Aug2022 Large Language Models and the Reverse Turing Test.pdf
https://arxiv.org/abs/1706.03762
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/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
https://en.wikipedia.org/wiki/Hard_problem_of_consciousness

/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
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/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
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/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
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/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
https://en.wikipedia.org/wiki/Hard_problem_of_consciousness

/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
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/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
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/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
https://en.wikipedia.org/wiki/Hard_problem_of_consciousness

/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
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#???
#???
???
/home/bill/web/webWork/bash script: thematic, list of txt files to search.txt
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html
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/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p484fig13.04 Cognitive-Emotional resonance:  top-down feedback from the orbitofrontal cortex closes a feedback loop.png
/home/bill/web/Neural nets/Grossberg/images- captioned/pxvifig00.01 Macrocircuit of the visual system.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.png
https://en.wikipedia.org/wiki/Hard_problem_of_consciousness

/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p038fig01.25 ART Matching Rule stabilizes learning: [real time learn, object attention].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 models using only local computations look like an ART prototype model.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].png
/home/bill/web/Neural nets/Grossberg/images- captioned/p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.png
/home/bill/web/Neural nets/Grossberg/images- captioned/p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.png
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#Introduction
#principles, architecture, function, process
#Principles, Principia
#equations of the [brain, mind]
#modules & modal architectures ([micro, macro]-circuits)
#CogEM Cognitive-Emotional-Motor model
#[intra, inter]-cellular process
#informational noise suppression
#[stable, robust, adaptive] learning
#on-center off-surround
#top-down bottom-up
#cooperative-competitive
#complementary computing
#laminar computing
#computing with cellular patterns
#family of ART.....base, linguistic
#ART - Adaptive Resonance Theory
#LAMINART vison, speech, cognition
#[, n]START learning & memory consolidation
#ARTMAP associate learned categories across ART networks
#[, c]ARTWORD word perception cycle
#LIST PARSE [linguistic, spatial, motor] working memory
#family of ART ....visual, auditory
#[, d, p]ARTSCAN attentional shroud, binocular rivalry
#ARTSCENE classification of scenic properties
#SMART synchronous matching ART, mismatch triggering
#ARTPHONE [gain control, working] memory
#ARTSTREAM auditory streaming, SPINET sound spectra
#consciousness
#What is consciousness?
#conscious vs non-conscious
#Grossberg: other consciousness theories
#random fun themes
#art (painting etc)
#biology, evolution, paleontology
#brain disorders and disease
#hippocampus IS a cognitive map!
#auditory continuity illusion
#see-reach to hear-speak
#neurotransmitter
#learning and development
#Why are there hexagonal grid cell receptive fields?
#Strange themes
#AI, machine intelligence, etc
#Brain is NOT Bayesian?
#brain rythms & Schuman resonances
#Explainable AI
#logic vs connectionist
#Successful [datamodelling, applications] showing effectiveness of cART etc
#[bio, neuro, psycho]logy data
#[software, engineering, other] applications
/home/bill/web/webWork/pMenuTopHelp.html#Navigation: [menu, link, directory]s
/home/bill/web/webWork/pMenuTopHelp.html#Theme webPage generation by bash script
/home/bill/web/webWork/pMenuTopHelp.html#Notation for [chapter, section, figure, table, index, note]s
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#Introduction
#principles, architecture, function, process
#Principles, Principia
#equations of the [brain, mind]
#modules & modal architectures ([micro, macro]-circuits)
#CogEM Cognitive-Emotional-Motor model
#[intra, inter]-cellular process
#informational noise suppression
#[stable, robust, adaptive] learning
#on-center off-surround
#top-down bottom-up
#cooperative-competitive
#complementary computing
#laminar computing
#computing with cellular patterns
#family of ART.....base, linguistic
#ART - Adaptive Resonance Theory
#LAMINART vison, speech, cognition
#[, n]START learning & memory consolidation
#ARTMAP associate learned categories across ART networks
#[, c]ARTWORD word perception cycle
#LIST PARSE [linguistic, spatial, motor] working memory
#family of ART ....visual, auditory
#[, d, p]ARTSCAN attentional shroud, binocular rivalry
#ARTSCENE classification of scenic properties
#SMART synchronous matching ART, mismatch triggering
#ARTPHONE [gain control, working] memory
#ARTSTREAM auditory streaming, SPINET sound spectra
#consciousness
#What is consciousness?
#conscious vs non-conscious
#Grossberg: other consciousness theories
#random fun themes
#art (painting etc)
#biology, evolution, paleontology
#brain disorders and disease
#hippocampus IS a cognitive map!
#auditory continuity illusion
#see-reach to hear-speak
#neurotransmitter
#learning and development
#Why are there hexagonal grid cell receptive fields?
#Strange themes
#AI, machine intelligence, etc
#Brain is NOT Bayesian?
#brain rythms & Schuman resonances
#Explainable AI
#logic vs connectionist
#Successful [datamodelling, applications] showing effectiveness of cART etc
#[bio, neuro, psycho]logy data
#[software, engineering, other] applications
/home/bill/web/webWork/pMenuTopHelp.html#Navigation: [menu, link, directory]s
/home/bill/web/webWork/pMenuTopHelp.html#Theme webPage generation by bash script
/home/bill/web/webWork/pMenuTopHelp.html#Notation for [chapter, section, figure, table, index, note]s
/home/bill/web/webWork/pMenuTopHelp.html#incorporate reader questions into theme webPages
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http://www.ijcnn2007.org
2007 IJCNN Orlando website/committee.htm
2006 WCCI website - official/2006 WCCI06 Operating Committee.htm
http://faculty.uwb.edu/ijcnn05/index.html
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http://www.ijcnn2007.org
2007 IJCNN Orlando website/committee.htm
2006 WCCI website - official/2006 WCCI06 Operating Committee.htm
http://faculty.uwb.edu/ijcnn05/index.html
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http://www.ijcnn2007.org
2007 IJCNN Orlando website/committee.htm
2006 WCCI website - official/2006 WCCI06 Operating Committee.htm
http://faculty.uwb.edu/ijcnn05/index.html
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http://www.ijcnn2007.org
2007 IJCNN Orlando website/committee.htm
2006 WCCI website - official/2006 WCCI06 Operating Committee.htm
http://faculty.uwb.edu/ijcnn05/index.html
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#Introduction
#callerID-SNNs
#[why, how] does a neuron spike? (or not) : 
#Advantages, characteristics : 
#Disadvantages : 
#Mechanisms for processing [DNA, RNA] 'program code'
#MindCode: Genetics and NNs
#Stephen Grossberg 2021 'Conscious Mind, Resonant Brain'
/home/bill/web/Neural nets/callerID-SNNs/callerID-SNNs.HtmWeb.html
/home/bill/web/References/Mathematics/images/Izhikevich Nov2003 Known types of neurons.png
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/home/bill/web/Neural nets/MindCode/MindCode/MindCode.HtmWeb.html
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/home/bill/web/Neural nets/Grossberg/Grossbergs list of [figure, table]s.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html
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#Introduction
#callerID-SNNs
#[why, how] does a neuron spike? (or not) : 
#Advantages, characteristics : 
#Disadvantages : 
#Mechanisms for processing [DNA, RNA] 'program code'
#MindCode: Genetics and NNs
#Stephen Grossberg 2021 'Conscious Mind, Resonant Brain'
/home/bill/web/Neural nets/callerID-SNNs/callerID-SNNs.HtmWeb.html
/home/bill/web/References/Mathematics/images/Izhikevich Nov2003 Known types of neurons.png
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/home/bill/web/Neural nets/MindCode/MindCode/MindCode.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/webWork/pMenuTopMenu Grossberg.html
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [figure, table]s.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
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#Introduction
#callerID-SNNs
#[why, how] does a neuron spike? (or not) : 
#Advantages, characteristics : 
#Disadvantages : 
#Mechanisms for processing [DNA, RNA] 'program code'
#MindCode: Genetics and NNs
#Stephen Grossberg 2021 'Conscious Mind, Resonant Brain'
/home/bill/web/Neural nets/callerID-SNNs/callerID-SNNs.HtmWeb.html
/home/bill/web/References/Mathematics/images/Izhikevich Nov2003 Known types of neurons.png
/home/bill/web/Neural nets/genetic mechanisms/genetic mechanisms for [transport, code].html
/home/bill/web/Neural nets/MindCode/MindCode/MindCode.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/webWork/pMenuTopMenu Grossberg.html
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [figure, table]s.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html
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#Introduction
#callerID-SNNs
#[why, how] does a neuron spike? (or not) : 
#Advantages, characteristics : 
#Disadvantages : 
#Mechanisms for processing [DNA, RNA] 'program code'
#MindCode: Genetics and NNs
#Stephen Grossberg 2021 'Conscious Mind, Resonant Brain'
/home/bill/web/Neural nets/callerID-SNNs/callerID-SNNs.HtmWeb.html
/home/bill/web/References/Mathematics/images/Izhikevich Nov2003 Known types of neurons.png
/home/bill/web/Neural nets/genetic mechanisms/genetic mechanisms for [transport, code].html
/home/bill/web/Neural nets/MindCode/MindCode/MindCode.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/webWork/pMenuTopMenu Grossberg.html
/home/bill/web/Neural nets/Grossberg/Grossbergs list of [figure, table]s.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html
http://www.billhowell.ca/Neural nets/Mind2023/Mind2023 webPage.html
http://www.billhowell.ca/Neural nets/Mind2023/Mind2023 webPage.html
http://www.billhowell.ca/Neural nets/Mind2023/Mind2023 webPage.html
http://www.billhowell.ca/Neural nets/Mind2023/Mind2023 webPage.html
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#Preface
#[Turing, von Neuman] machines of gene mechanisms?
#[intra, extra]-cellular processes, [neuron, astrocyte]s
#Cellular mechanisms for [protein, information]
#[DNA, rhibosome, etc] addresses
#[2, 4]-value logic for [protein, information]
#ribosomes
#microtubules: platforms for [transport, info processing]?
#same mRNA code, different mechanism: ergo diff [program, protein]?
#missing sub-sections for genetic machinery
#DNA transcription to mRNA
#Computations with multiple RNA strands
#NOT: simple function of a single RNA input strand, one output RNA
#AND: simple function of two input RNA strands, one output RNA
#GoTo
#IF: control branching to an [operation, address]
#???
#MindCode [learn, evolve]: Grossberg's 'Conscious Mind, Resonant Brain'
#Consciousness: Grossberg's tie-in of [, non] conscious processes
#Biological context
#[pro, eu]karyotes 
#Lamarckian versus Mendellian heredity, spiking MindCode as special case
#Astrocyctes
#Ontogeny: the growth of bioFirmWare
#Biological similies
#Endocrine system
#bone [shell, fibre]s
#Building MindCode from [bio, psycho]logical data
#[proteomic, neuroinfo, MindCode]*[protein, program]*bioFirmWare
#MindCode [identify, library, model, predict, change, heal]
#MindCode applications to keep in mind
#Autonomous systems, networks, robots
#Missing concepts - among hundreds
#Shapiro &amp Benenson: example stream from early MindCode 2005 on...
#Links to my related work
#[lists, outline, concept]s to keep in mind from 2020
#Computations with multiple RNA strands
/home/bill/web/Neural nets/callerID-SNNs/callerID-SNNs.HtmWeb.html
/home/bill/web/ProjMini/Colin James - KanbanNN, 4VL logic/four-valued logic analysis/
/home/bill/web/Neural nets/callerID-SNNs/
https://en.wikipedia.org/wiki/Transfer_RNA
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/home/bill/web/Neural nets/MindCode/Howell 050824 Junk DNA & NeuralNetworks, conjecture on directions and implications, IJCNN05 workshop panel presentation.ppt

/home/bill/web/Neural nets/MindCode/Howell 060215 Genetic specification of neural networks, draft concepts and implications.pdf
/home/bill/web/Neural nets/MindCode/Howell 060716 Genetic specification of recurrent neural networks, Initial thoughts, WCCI 2006 paper 1341.pdf
/home/bill/web/Neural nets/MindCode/Howell 060721 Genetic Specification of Recurrent Neural Networks Initial Thoughts, WCCI 2006 presentation.ppt
/home/bill/web/Neural nets/MindCode/
/home/bill/web/Neural nets/MindCode/MindCode.HtmWeb.html
/home/bill/web/Neural nets/MindCode/16 Introduction - Conceptual pseudo-basis for MindCode.odt
/home/bill/web/Neural nets/MindCode/
/home/bill/web/Neural nets/MindCode/voice musings/
#[DNA, rhibosome, etc] addresses
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Puetz 2022 - Infinitely fractal universe.pdf
/home/bill/web/Neural nets/MindCode/voice musings/231106_2212 Glenn Borchardts concept of infinity, avoid myopic thinking.mp3
/home/bill/web/ProjMini/PuetzUWS/Puetz, Borchardt 150925 Quasi-periodic fractal patterns in geomagnetic reversals, geological activity, and astronomical events.pdf
/home/bill/web/Neural nets/MindCode/Howell 060716 Genetic specification of recurrent neural networks, Initial thoughts, WCCI 2006 paper 1341.pdf
/home/bill/web/Neural nets/MindCode/MindCode.HtmWeb.html
/home/bill/web/Qnial/MY_NDFS/MindCode/12_MindCode [data, optr]s.txt
/home/bill/web/Neural nets/callerID-SNNs/callerID-SNNs.HtmWeb.html
/home/bill/web/Qnial/MY_NDFS/callerID-SNNs/callerID-SNNs.ndf
/home/bill/web/bin/fileops.sh
/home/bill/web/Neural nets/z_history/Mind2020/
/home/bill/web/Neural nets/MindCode/16 Introduction - Conceptual pseudo-basis for MindCode.odt
/home/bill/web/Neural nets/MindCode/12 MindCode components.odt
/home/bill/web/Neural nets/MindCode/13 Historical [DNA, Protein, Evolutionary Computing, ANN] hybrid basis for epiDNA-NNs.odt
/home/bill/web/Neural nets/MindCode/14 MindCode - arbitrary selections from Multiple Conflicting Hypothesis.odt
/home/bill/web/Neural nets/MindCode/15 Assumed rules of the game.odt
/home/bill/web/Neural nets/MindCode/17 Questions, not answers.odt
/home/bill/web/Neural nets/MindCode/20 Static epiDNA-NN.odt
/home/bill/web/Neural nets/MindCode/22 Dynamic epiDNA-NN coding.odt
/home/bill/web/Neural nets/MindCode/50 [Neurological, biological] basis for epiDNA coding.odt
/home/bill/web/Neural nets/MindCode/80 Ontogeny.odt
/home/bill/web/Neural nets/MindCode/80 Specialized epiDNA-NNs for MindCode.odt
/home/bill/web/Neural nets/MindCode/90 Hybrids of [algorithms, conventional computing, ANNs, MindCode].odt
/home/bill/web/Neural nets/consciousness/Taylors consciousness.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
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#Preface
#[Turing, von Neuman] machines of gene mechanisms?
#[intra, extra]-cellular processes, [neuron, astrocyte]s
#Cellular mechanisms for [protein, information]
#[DNA, rhibosome, etc] addresses
#[2, 4]-value logic for [protein, information]
#ribosomes
#microtubules: platforms for [transport, info processing]?
#same mRNA code, different mechanism: ergo diff [program, protein]?
#missing sub-sections for genetic machinery
#DNA transcription to mRNA
#Computations with multiple RNA strands
#NOT: simple function of a single RNA input strand, one output RNA
#AND: simple function of two input RNA strands, one output RNA
#GoTo
#IF: control branching to an [operation, address]
#???
#MindCode [learn, evolve]: Grossberg's 'Conscious Mind, Resonant Brain'
#Consciousness: Grossberg's tie-in of [, non] conscious processes
#Biological context
#[pro, eu]karyotes 
#Lamarckian versus Mendellian heredity, spiking MindCode as special case
#Astrocyctes
#Ontogeny: the growth of bioFirmWare
#Biological similies
#Endocrine system
#bone [shell, fibre]s
#Building MindCode from [bio, psycho]logical data
#[proteomic, neuroinfo, MindCode]*[protein, program]*bioFirmWare
#MindCode [identify, library, model, predict, change, heal]
#MindCode applications to keep in mind
#Autonomous systems, networks, robots
#Missing concepts - among hundreds
#Shapiro &amp Benenson: example stream from early MindCode 2005 on...
#Links to my related work
#[lists, outline, concept]s to keep in mind from 2020
#Computations with multiple RNA strands
/home/bill/web/Neural nets/callerID-SNNs/callerID-SNNs.HtmWeb.html
/home/bill/web/ProjMini/Colin James - KanbanNN, 4VL logic/four-valued logic analysis/
/home/bill/web/Neural nets/callerID-SNNs/
https://en.wikipedia.org/wiki/Transfer_RNA
/home/bill/web/webWork/link lost.HtmWeb.html
/home/bill/web/webWork/link lost.HtmWeb.html
/home/bill/web/Neural nets/MindCode/Howell 050824 Junk DNA & NeuralNetworks, conjecture on directions and implications, IJCNN05 workshop panel presentation.ppt

/home/bill/web/Neural nets/MindCode/Howell 060215 Genetic specification of neural networks, draft concepts and implications.pdf
/home/bill/web/Neural nets/MindCode/Howell 060716 Genetic specification of recurrent neural networks, Initial thoughts, WCCI 2006 paper 1341.pdf
/home/bill/web/Neural nets/MindCode/Howell 060721 Genetic Specification of Recurrent Neural Networks Initial Thoughts, WCCI 2006 presentation.ppt
/home/bill/web/Neural nets/MindCode/
/home/bill/web/Neural nets/MindCode/MindCode.HtmWeb.html
/home/bill/web/Neural nets/MindCode/16 Introduction - Conceptual pseudo-basis for MindCode.odt
/home/bill/web/Neural nets/MindCode/
/home/bill/web/Neural nets/MindCode/voice musings/
#[DNA, rhibosome, etc] addresses
/home/bill/web/webWork/pMenuTopMenu.html#Grossberg
/home/bill/web/Neural nets/Grossberg/Grossbergs [core, fun, strange] concepts.html
/home/bill/web/Neural nets/Grossberg/Grossbergs overview.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Puetz 2022 - Infinitely fractal universe.pdf
/home/bill/web/Neural nets/MindCode/voice musings/231106_2212 Glenn Borchardts concept of infinity, avoid myopic thinking.mp3
/home/bill/web/ProjMini/PuetzUWS/Puetz, Borchardt 150925 Quasi-periodic fractal patterns in geomagnetic reversals, geological activity, and astronomical events.pdf
/home/bill/web/Neural nets/MindCode/Howell 060716 Genetic specification of recurrent neural networks, Initial thoughts, WCCI 2006 paper 1341.pdf
/home/bill/web/Neural nets/MindCode/MindCode.HtmWeb.html
/home/bill/web/Qnial/MY_NDFS/MindCode/12_MindCode [data, optr]s.txt
/home/bill/web/Neural nets/callerID-SNNs/callerID-SNNs.HtmWeb.html
/home/bill/web/Qnial/MY_NDFS/callerID-SNNs/callerID-SNNs.ndf
/home/bill/web/bin/fileops.sh
/home/bill/web/Neural nets/z_history/Mind2020/
/home/bill/web/Neural nets/MindCode/16 Introduction - Conceptual pseudo-basis for MindCode.odt
/home/bill/web/Neural nets/MindCode/12 MindCode components.odt
/home/bill/web/Neural nets/MindCode/13 Historical [DNA, Protein, Evolutionary Computing, ANN] hybrid basis for epiDNA-NNs.odt
/home/bill/web/Neural nets/MindCode/14 MindCode - arbitrary selections from Multiple Conflicting Hypothesis.odt
/home/bill/web/Neural nets/MindCode/15 Assumed rules of the game.odt
/home/bill/web/Neural nets/MindCode/17 Questions, not answers.odt
/home/bill/web/Neural nets/MindCode/20 Static epiDNA-NN.odt
/home/bill/web/Neural nets/MindCode/22 Dynamic epiDNA-NN coding.odt
/home/bill/web/Neural nets/MindCode/50 [Neurological, biological] basis for epiDNA coding.odt
/home/bill/web/Neural nets/MindCode/80 Ontogeny.odt
/home/bill/web/Neural nets/MindCode/80 Specialized epiDNA-NNs for MindCode.odt
/home/bill/web/Neural nets/MindCode/90 Hybrids of [algorithms, conventional computing, ANNs, MindCode].odt
/home/bill/web/Neural nets/consciousness/Taylors consciousness.HtmWeb.html
/home/bill/web/Neural nets/consciousness/What is consciousness: from historical to Grossberg.HtmWeb.html
/home/bill/web/Neural nets/consciousness/[definitions, models] of consciousness.html
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#Introduction
#Query 1: gravity is an Electro-Magnetic (EM) effect
#Query 2: historical concepts of gravity as electro-magnetic
#Conclusion: failure, mis-leading
https://en.wikipedia.org/wiki/History_of_gravitational_theory
https://physics.stackexchange.com/questions/120163/is-gravity-just-electromagnetic-attraction
https://physics.stackexchange.com/questions/144541/gravity-and-magnetism-one-and-the-same?noredirect=1&lq=1
https://physics.stackexchange.com/questions/192494/is-gravity-weak-negative-electric-charge?noredirect=1&lq=1
https://www.youtube.com/watch?v=8yNze99C0Ys

https://link.springer.com/chapter/10.1007/0-8176-4454-7_2
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#Introduction
#Query 1: gravity is an Electro-Magnetic (EM) effect
#Query 2: historical concepts of gravity as electro-magnetic
#Conclusion: failure, mis-leading
https://en.wikipedia.org/wiki/History_of_gravitational_theory
https://physics.stackexchange.com/questions/120163/is-gravity-just-electromagnetic-attraction
https://physics.stackexchange.com/questions/144541/gravity-and-magnetism-one-and-the-same?noredirect=1&lq=1
https://physics.stackexchange.com/questions/192494/is-gravity-weak-negative-electric-charge?noredirect=1&lq=1
https://www.youtube.com/watch?v=8yNze99C0Ys

https://link.springer.com/chapter/10.1007/0-8176-4454-7_2
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#Introduction
#Query 1: gravity is an Electro-Magnetic (EM) effect
#Query 2: historical concepts of gravity as electro-magnetic
#Conclusion: failure, mis-leading
https://en.wikipedia.org/wiki/History_of_gravitational_theory
https://physics.stackexchange.com/questions/120163/is-gravity-just-electromagnetic-attraction
https://physics.stackexchange.com/questions/144541/gravity-and-magnetism-one-and-the-same?noredirect=1&lq=1
https://physics.stackexchange.com/questions/192494/is-gravity-weak-negative-electric-charge?noredirect=1&lq=1
https://www.youtube.com/watch?v=8yNze99C0Ys

https://link.springer.com/chapter/10.1007/0-8176-4454-7_2
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#Introduction
#Query 1: gravity is an Electro-Magnetic (EM) effect
#Query 2: historical concepts of gravity as electro-magnetic
#Conclusion: failure, mis-leading
https://en.wikipedia.org/wiki/History_of_gravitational_theory
https://physics.stackexchange.com/questions/120163/is-gravity-just-electromagnetic-attraction
https://physics.stackexchange.com/questions/144541/gravity-and-magnetism-one-and-the-same?noredirect=1&lq=1
https://physics.stackexchange.com/questions/192494/is-gravity-weak-negative-electric-charge?noredirect=1&lq=1
https://www.youtube.com/watch?v=8yNze99C0Ys

https://link.springer.com/chapter/10.1007/0-8176-4454-7_2
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#Google Gemini:
#Three simple example themes, counter to science religions
#Conclusions compiled from my initial learning examples
#PRIORITY: genetic mechanisms for multi-strand tRNA co-processing
#meta-level themes
#fractal themes for multi-fractal analysis
#Emergent Gemini potential, as per initial learning examples
#Needles in the haystack, diversity: Multiple Conflicting Hypothesis (MCH)
#my 'Dark Optimism' theme : How do people actually [think, believe, behave]?
#Great truths, or not?
#[dichotomies, beliefs]: don't drop the dark side
#Forgotten tools?
#Over-extended tools?
#fraudulent intellectualism?
#Context
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/Solar micronova and catastrophism.HtmWeb.html
https://www.youtube.com/watch?v=-sGPCMIQZLw&t=0s
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/gravity is an Electro-Magnetic (EM) effect.HtmWeb.html
/home/bill/web/ProjMajor/Lies, Damned Lies, and Scientists/7_references: [GR, QM] alternatives.html
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/Expanding Earth hypothesis versus plate tectonics.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Howell - PuetzUWS [time, price] multifractal of detrended SPX 1871-2020.html
/home/bill/web/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length, theme] multifractals.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU biology.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU geology.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU human.HtmWeb.html
/home/bill/web//home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU [physics, astronomy].HtmWeb.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/EU [weather, climate].html
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#Google Gemini:
#Three simple example themes, counter to science religions
#Conclusions compiled from my initial learning examples
#PRIORITY: genetic mechanisms for multi-strand tRNA co-processing
#meta-level themes
#fractal themes for multi-fractal analysis
#Emergent Gemini potential, as per initial learning examples
#Needles in the haystack, diversity: Multiple Conflicting Hypothesis (MCH)
#my 'Dark Optimism' theme : How do people actually [think, believe, behave]?
#Great truths, or not?
#[dichotomies, beliefs]: don't drop the dark side
#Forgotten tools?
#Over-extended tools?
#fraudulent intellectualism?
#Context
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/Solar micronova and catastrophism.HtmWeb.html
https://www.youtube.com/watch?v=-sGPCMIQZLw&t=0s
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/gravity is an Electro-Magnetic (EM) effect.HtmWeb.html
/home/bill/web/ProjMajor/Lies, Damned Lies, and Scientists/7_references: [GR, QM] alternatives.html
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/Expanding Earth hypothesis versus plate tectonics.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Howell - PuetzUWS [time, price] multifractal of detrended SPX 1871-2020.html
/home/bill/web/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length, theme] multifractals.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU biology.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU geology.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU human.HtmWeb.html
/home/bill/web//home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU [physics, astronomy].HtmWeb.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/EU [weather, climate].html
/home/bill/web/webWork/pMenuTopMenu.html#neural
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/home/bill/web/webWork/pMenuTopStatus.html#neural
/home/bill/web/webWork/pMenuTopCopyright.html#neural
/home/bill/web/webWork/pMenuTopHelp.html#neural
#Google Gemini:
#Three simple example themes, counter to science religions
#Conclusions compiled from my initial learning examples
#PRIORITY: genetic mechanisms for multi-strand tRNA co-processing
#meta-level themes
#fractal themes for multi-fractal analysis
#Emergent Gemini potential, as per initial learning examples
#Needles in the haystack, diversity: Multiple Conflicting Hypothesis (MCH)
#my 'Dark Optimism' theme : How do people actually [think, believe, behave]?
#Great truths, or not?
#[dichotomies, beliefs]: don't drop the dark side
#Forgotten tools?
#Over-extended tools?
#fraudulent intellectualism?
#Context
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/Solar micronova and catastrophism.HtmWeb.html
https://www.youtube.com/watch?v=-sGPCMIQZLw&t=0s
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/gravity is an Electro-Magnetic (EM) effect.HtmWeb.html
/home/bill/web/ProjMajor/Lies, Damned Lies, and Scientists/7_references: [GR, QM] alternatives.html
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/Expanding Earth hypothesis versus plate tectonics.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Howell - PuetzUWS [time, price] multifractal of detrended SPX 1871-2020.html
/home/bill/web/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length, theme] multifractals.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU biology.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU geology.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU human.HtmWeb.html
/home/bill/web//home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU [physics, astronomy].HtmWeb.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/EU [weather, climate].html
/home/bill/web/webWork/pMenuTopMenu.html#neural
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/home/bill/web/webWork/pMenuTopStatus.html#neural
/home/bill/web/webWork/pMenuTopCopyright.html#neural
/home/bill/web/webWork/pMenuTopHelp.html#neural
#Google Gemini:
#Three simple example themes, counter to science religions
#Conclusions compiled from my initial learning examples
#PRIORITY: genetic mechanisms for multi-strand tRNA co-processing
#meta-level themes
#fractal themes for multi-fractal analysis
#Emergent Gemini potential, as per initial learning examples
#Needles in the haystack, diversity: Multiple Conflicting Hypothesis (MCH)
#my 'Dark Optimism' theme : How do people actually [think, believe, behave]?
#Great truths, or not?
#[dichotomies, beliefs]: don't drop the dark side
#Forgotten tools?
#Over-extended tools?
#fraudulent intellectualism?
#Context
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/Solar micronova and catastrophism.HtmWeb.html
https://www.youtube.com/watch?v=-sGPCMIQZLw&t=0s
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/gravity is an Electro-Magnetic (EM) effect.HtmWeb.html
/home/bill/web/ProjMajor/Lies, Damned Lies, and Scientists/7_references: [GR, QM] alternatives.html
/home/bill/web/Neural nets/Transformer NNs/LLMs for Conceptual Predators/Expanding Earth hypothesis versus plate tectonics.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
/home/bill/web/ProjMini/PuetzUWS/Howell - PuetzUWS [time, price] multifractal of detrended SPX 1871-2020.html
/home/bill/web/economics, markets/market data/detrend/220802/detrend StockMkt Indices 1871-2022 PuetzUWS2011.png
/home/bill/web/My sports & clubs/natural- Thunderbolts/Electric Universe [time, length, theme] multifractals.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU biology.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU geology.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU human.HtmWeb.html
/home/bill/web//home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/EU [physics, astronomy].HtmWeb.html
/home/bill/web/My sports & clubs/natural- Thunderbolts/EU [weather, climate].html
/home/bill/web/webWork/pMenuTopMenu.html#neural
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#Introduction
#Query 1: Solar micronova and catastrophism
#Query 2: Solar micro-nova and historical catastrophes
#Conclusion: not useful
/home/bill/web/webWork/pMenuTopMenu.html#neural
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#Introduction
#Query 1: Solar micronova and catastrophism
#Query 2: Solar micro-nova and historical catastrophes
#Conclusion: not useful
/home/bill/web/webWork/pMenuTopMenu.html#neural
./
/home/bill/web/webWork/pMenuTopStatus.html#neural
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#Introduction
#Query 1: Solar micronova and catastrophism
#Query 2: Solar micro-nova and historical catastrophes
#Conclusion: not useful
/home/bill/web/webWork/pMenuTopMenu.html#neural
./
/home/bill/web/webWork/pMenuTopStatus.html#neural
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#Introduction
#Query 1: Solar micronova and catastrophism
#Query 2: Solar micro-nova and historical catastrophes
#Conclusion: not useful
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/home/bill/web/page Howell - blog.HtmWeb.html
/home/bill/web/Cool emails/
/home/bill/web/Cool stuff/
/home/bill/web/page Howell - comments about Suspicious Observers videos, references.txt
/home/bill/web/ProjMini/Howells projects.ods
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/home/bill/web/page Howell - blog.HtmWeb.html
/home/bill/web/Cool emails/
/home/bill/web/Cool stuff/
/home/bill/web/page Howell - comments about Suspicious Observers videos, references.txt
/home/bill/web/ProjMini/Howells projects.ods
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/home/bill/web/page Howell - blog.HtmWeb.html
/home/bill/web/Cool emails/
/home/bill/web/Cool stuff/
/home/bill/web/page Howell - comments about Suspicious Observers videos, references.txt
/home/bill/web/ProjMini/Howells projects.ods
/home/bill/web/webWork/pMenuTopMenu.html#personal
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/home/bill/web/page Howell - blog.HtmWeb.html
/home/bill/web/Cool emails/
/home/bill/web/Cool stuff/
/home/bill/web/page Howell - comments about Suspicious Observers videos, references.txt
/home/bill/web/ProjMini/Howells projects.ods
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/home/bill/web/Personal/Personal/Almost true tall tales.HtmWeb.html
/home/bill/web/Personal/Challenging the dogmas.HtmWeb.html
/home/bill/web/Personal/Crazy ideas/Crazy ideas.HtmWeb.html
/home/bill/web/Personal/Crystal ball.HtmWeb.html
/home/bill/web/Personal/Howell - favourite sayings.HtmWeb.html
/home/bill/web/Personal/z_history/130726 Deer collison/Car collision with a deer.HtmWeb.html
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/home/bill/web/Personal/Crazy ideas/Crazy ideas.HtmWeb.html
/home/bill/web/Personal/Crystal ball.HtmWeb.html
/home/bill/web/Personal/Howell - favourite sayings.HtmWeb.html
/home/bill/web/Personal/z_history/130726 Deer collison/Car collision with a deer.HtmWeb.html
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/home/bill/web/Personal/Crazy ideas/Crazy ideas.HtmWeb.html
/home/bill/web/Personal/Crystal ball.HtmWeb.html
/home/bill/web/Personal/Howell - favourite sayings.HtmWeb.html
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/home/bill/web/Personal/Crazy ideas/Crazy ideas.HtmWeb.html
/home/bill/web/Personal/Crystal ball.HtmWeb.html
/home/bill/web/Personal/Howell - favourite sayings.HtmWeb.html
/home/bill/web/Personal/z_history/130726 Deer collison/Car collision with a deer.HtmWeb.html
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https://www.mackinac.org/SP1998-01
https://www.marketwatch.com/story/investors-should-be-wary-of-warren-buffetts-crash-warning-2020-08-26?mod=newsviewer_click
https://www.tradingview.com/chart/SPX500/4mxAC2o8-Stock-prices-have-reached-a-permanently-high-plateau/
/home/bill/web/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Birkeland rotation in galaxy - not dark matter.HtmWeb.html
https://suspicious0bservers.org/
/home/bill/web/Bill Howells videos/Birkeland rotation in galaxy - not dark matter/Dark matter video 1 - initial, simple.mpeg
/home/bill/web/Personal/z_history/181211 Van versus Semi collision.HtmWeb.html
/home/bill/web/CompLangs/LibreOffice/LibreCalc bank account macro system.txt
/home/bill/web/CompLangs/LibreOffice/Howell_codification_macLib/
/home/bill/web/ProjMajor/Lies, Damned Lies, and Scientists/Howell - paradigm shift examples.odt
/home/bill/web/ProjMajor/Sun climate/180624 Howell - de Jager, Nieuwenhuijzen, Nieuwenhuijzen, Duhau, northern hemisphere ground temperature and solar [polar,equatorial] magnetic activity.txt
https://www.youtube.com/watch?v=u2wYS300pf0
http://safireproject.com/
https://www.youtube.com/user/Suspicious0bservers/videos
https://www.youtube.com/watch?v=u2wYS300pf0
https://www.youtube.com/watch?v=RAV-3PhGU7k
https://www.nature.com/articles/d41586-018-05143-8
/home/bill/web/ProjMajor/Electric Universe/Ginenthal 96mmdd Description of Great fire of Chicago.pdf
http://www.doiserbia.nb.rs/img/doi/0354-9836/2014 OnLine-First/0354-98361400036R.pdf
https://www.youtube.com/watch?v=4emMZmbkYlA&feature=youtu.be
/home/bill/web/Cool stuff/Hoyte & Schatten year - solar influence on climate & natural systems, graphs.pdf
https://en.wikipedia.org/wiki/Alexey_Grigorevich_Ivakhnenko
http://www.idsia.ch/~juergen/DeepLearning2July2014.pdf
http://people.idsia.ch/~juergen/deep-learning-overview.html
https://plus.google.com/100849856540000067209
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Past & future worlds.html
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/
/home/bill/web/Bill Howells videos/160901 Big Data, Deep Learning, and Safety/Howell 161220 Big Data, Deep Learning, Safety.ogv
/home/bill/web/Bill Howells videos/160901 Big Data, Deep Learning, and Safety/Howell 161220 Big Data, Deep Learning, Safety.ogv
/home/bill/web/webOther/Vaughan, Paul/Vaughan 160527 Ring_of_Fire_Volcano_Explosivity_El_Nino_La_Nina.png
http://www.suspicious0bservers.org
/home/bill/web/ProjMajor/Sun pandemics, health/Howell 151208 The Greatest of cycles - Human implications.docx
http://dx.doi.org/10.1016/j.chaos.2015.09.029
http://dx.doi.org/10.1016/j.chaos.2014.04.001
http://www.sciencedirect.com/science/article/pii/S0378375815001901
http://dx.doi.org/10.1016/j.chaos.2015.09.029
/home/bill/web/ProjMajor/Electric Universe/Howell 150627 Electric Universe - Climate & Svensmark Milankovic, Dark matter - Veizer-Shaviv.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/Script for Icebreaker Unchained.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/
/home/bill/web/Personal/Crazy ideas/A Riddle by, for, of, on, and with the Mind.pdf
/home/bill/web/ProjMajor/Sun pandemics, health/Howell 151208 The Greatest of cycles - Human implications.docx
http://dx.doi.org/10.1016/j.chaos.2015.09.029
http://dx.doi.org/10.1016/j.chaos.2014.04.001
http://www.sciencedirect.com/science/article/pii/S0378375815001901
http://dx.doi.org/10.1016/j.chaos.2015.09.029
/home/bill/web/ProjMajor/Electric Universe/Howell 150627 Electric Universe - Climate & Svensmark Milankovic, Dark matter - Veizer-Shaviv.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/Script for Icebreaker Unchained.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/
/home/bill/web/Personal/Crazy ideas/A Riddle by, for, of, on, and with the Mind.pdf
/home/bill/web/ProjMajor/Climate - Kyoto Premise fraud/Howell 150331 Lies, Damned Lies, and Scientists - Summary & context.pdf
/home/bill/web/Neural nets/MindCode/Howell 150225 - MindCode Manifesto.odt
/home/bill/web/ProjMajor/Electric Universe/Howell 141212 Don Scotts Birkeland current magnetic structure, review and comments.pdf
/home/bill/web/Personal/Howell 140930 - 2014 Summer vacation travel log.pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 140622 Sun & SAM (Southern Annular Mode).pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 140524 ERSST EOF 1234.pdf
/home/bill/web/webOther/Yaskell, Steven/Yaskell 140302 Letter to the Vermont state government - Climate history.pdf
/home/bill/web/Bill Howells videos/140214 Nazis saved Norwegians video/Nazis saved Norwegian lives.ogv
/home/bill/web/webOther/Vaughan, Paul/Vaughan 140203 Multidecadal Sun-Climate-Change 101, Solar-Terrestrial Spatiotemporal Aggregation Primer on Trivial Extension of Milankovitch.PDF
/home/bill/web/webOther/Vaughan, Paul/Vaughan 131220 Sun-Climate 101 Solar-Terrestrial Primer 2.pdf
/home/bill/web/Neural nets/Howell 131013 Robots, Signal Processing and Control Theory - Random, stray thoughts.pdf
/home/bill/web/Neural nets/Howell 131011 fMRI - Random, stray thoughts.pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 130804 Solar Terrestrial Climate 101.PDF
/home/bill/web/Personal/z_history/130726 Deer collison/Car collision with a deer.HtmWeb.html
/home/bill/web/Neural nets/Howell 131013 Robots, Signal Processing and Control Theory - Random, stray thoughts.pdf
/home/bill/web/Neural nets/Howell 131011 fMRI - Random, stray thoughts.pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 130804 Solar Terrestrial Climate 101.PDF
/home/bill/web/Personal/z_history/130726 Deer collison/Car collision with a deer.HtmWeb.html
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http://www.solarstation.ru/TL/PDF/tl_22.pdf
http://tallbloke.wordpress.com/2013/04/18/paul-vaughan-comparing-jupiter-earth-venus-alignment-cycles-with-variation-in-the-solar-rotation-period/
/home/bill/web/webOther/Vaughan, Paul/Vaughan 130316 - 60 Year Cycle of 27 Day Terracentric Solar Rotation.pdf
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/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Anderson 120725 Electric scarring of the Earth's surface - annotated & scanned.pdf
http://youtu.be/X9C4Tx2zC3c
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Howell 120903 Paul Andersons Electric scarring of the Earth.pdf
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https://www.eurekalert.org/pub_releases/2018-05/fda-nni052218.php
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http://www.suspicious0bservers.org/april-5-2016/
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https://www.tradingview.com/chart/SPX500/4mxAC2o8-Stock-prices-have-reached-a-permanently-high-plateau/
/home/bill/web/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html
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https://suspicious0bservers.org/
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http://safireproject.com/
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https://www.youtube.com/watch?v=RAV-3PhGU7k
https://www.nature.com/articles/d41586-018-05143-8
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http://www.doiserbia.nb.rs/img/doi/0354-9836/2014 OnLine-First/0354-98361400036R.pdf
https://www.youtube.com/watch?v=4emMZmbkYlA&feature=youtu.be
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https://en.wikipedia.org/wiki/Alexey_Grigorevich_Ivakhnenko
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http://people.idsia.ch/~juergen/deep-learning-overview.html
https://plus.google.com/100849856540000067209
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http://www.suspicious0bservers.org
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http://dx.doi.org/10.1016/j.chaos.2015.09.029
http://dx.doi.org/10.1016/j.chaos.2014.04.001
http://www.sciencedirect.com/science/article/pii/S0378375815001901
http://dx.doi.org/10.1016/j.chaos.2015.09.029
/home/bill/web/ProjMajor/Electric Universe/Howell 150627 Electric Universe - Climate & Svensmark Milankovic, Dark matter - Veizer-Shaviv.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/Script for Icebreaker Unchained.pdf
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http://dx.doi.org/10.1016/j.chaos.2015.09.029
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http://www.sciencedirect.com/science/article/pii/S0378375815001901
http://dx.doi.org/10.1016/j.chaos.2015.09.029
/home/bill/web/ProjMajor/Electric Universe/Howell 150627 Electric Universe - Climate & Svensmark Milankovic, Dark matter - Veizer-Shaviv.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/Script for Icebreaker Unchained.pdf
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/home/bill/web/webOther/Yaskell, Steven/Yaskell 140302 Letter to the Vermont state government - Climate history.pdf
/home/bill/web/Bill Howells videos/140214 Nazis saved Norwegians video/Nazis saved Norwegian lives.ogv
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/home/bill/web/Neural nets/Howell 131011 fMRI - Random, stray thoughts.pdf
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/home/bill/web/Personal/z_history/130726 Deer collison/Car collision with a deer.HtmWeb.html
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/home/bill/web/webOther/Vaughan, Paul/130501 Vaughan - Tlatov_Makarov_22a_JEV_R_Anim.gif
http://www.solarstation.ru/TL/PDF/tl_22.pdf
http://tallbloke.wordpress.com/2013/04/18/paul-vaughan-comparing-jupiter-earth-venus-alignment-cycles-with-variation-in-the-solar-rotation-period/
/home/bill/web/webOther/Vaughan, Paul/Vaughan 130316 - 60 Year Cycle of 27 Day Terracentric Solar Rotation.pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 130224 - Solar Terrestrial Volatility Waves.pdf
/home/bill/web/Personal/121225 Howells recent changes - career and location.pdf
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/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Anderson 120725 Electric scarring of the Earth's surface - annotated & scanned.pdf
http://youtu.be/X9C4Tx2zC3c
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Howell 120903 Paul Andersons Electric scarring of the Earth.pdf
/home/bill/web/ProjMajor/History/Howell 120729 Why was France's King Louis XIV called the Sun King.pdf
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/home/bill/web/ProjMajor/Electric Universe/Howell 120724 Gerald Pollack's Exclusion Zone water, Rob Johnson's electric bio-processes.pdf
/home/bill/web/ProjMini/Mythology/Howell 120514 Velikovsky, quack or genius.pdf
/home/bill/web/ProjMini/Mythology/Howell 120513 Ark of the Covenant.pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 120324 Solar-Terrestrial Resonance, Climate Shifts, & the Chandler Wobble Phase Reversal.pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 120324 The Solar Cycles Footprint on Terrestrial Climate.PDF
/home/bill/web/Bill Howells videos/120214 Venus et Mars, au dela d une histoire d amour/Mythology.ogv
/home/bill/web/ProjMini/Social media/Howell 111230 - Social graphs, social sets, and social media.pdf
/home/bill/web/ProjMini/Social media/Howell 111117 - How to set up & use data mining with Social media.pdf
/home/bill/web/ProjMini/Social media/Howell 110902 - Systems design issues for social media.pdf
/home/bill/web/ProjMini/Social media/Howell 111006 - Semantics beyond search.pdf
/home/bill/web/ProjMajor/Electric Universe/Howell 05-08Jul11 NPA-EU conference notes.pdf
/home/bill/web/Neural nets/Howell 110903 - Confabulation Theory, Plausible next sentence survey.pdf
/home/bill/web/ProjMajor/Lies, Damned Lies, and Scientists/Lies, damned lies, and scientists.HtmWeb.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - radioisotopes and history.jpg
/home/bill/web/ProjMajor/Climate - Kyoto Premise fraud/Howell - End of Enlightenment 25Sep09.pdf
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web /home/bill/web/Sun Charvatova/Radioisotopes/Howell Aug08 - Charvatova's hypothesis & Isotopic solar proxies.pdf
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/home/bill/web/webOther/Howell, Neil/_Neil Howell.HtmWeb.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Alta Potatoe Growers Association FULL version Nov07.pdf
/home/bill/web/ProjMajor/Sun climate/Howell 2007 - A Preliminary note on Holocene climate.pdf
/home/bill/web/ProjMajor/Sun climate/Howell - Glaciation models for the last 6 million years.pdf
/home/bill/web/ProjMajor/Sun model, forecast/Glaciation model 005/
/home/bill/web/ProjMajor/Sun model, forecast/Laskar etal model for solar insolation in QNial programming language/
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/webOther/Wickson, Steven/Wickson - Galactic Theory of Climate.pdf
/home/bill/web/ProjMajor/Sun pandemics, health/influenza/Tapping, Mathias, Surkan - Pandemics & solar activity.pdf
/home/bill/web/ProjMajor/Sun pandemics, health/_Pandemics, health, and the sun.HtmWeb.html
https://www.eurekalert.org/pub_releases/2018-05/fda-nni052218.php
https://youtu.be/JlyhCOJKOFU
http://www.suspicious0bservers.org/april-5-2016/
https://www.youtube.com/watch?v=u2wYS300pf0
https://quakewatch.net/
http://spaceweathernews.com/spf/
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https://www.mackinac.org/SP1998-01
https://www.marketwatch.com/story/investors-should-be-wary-of-warren-buffetts-crash-warning-2020-08-26?mod=newsviewer_click
https://www.tradingview.com/chart/SPX500/4mxAC2o8-Stock-prices-have-reached-a-permanently-high-plateau/
/home/bill/web/ProjMajor/Sun pandemics, health/corona virus/Howell - corona virus.HtmWeb.html
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https://suspicious0bservers.org/
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https://www.youtube.com/watch?v=u2wYS300pf0
http://safireproject.com/
https://www.youtube.com/user/Suspicious0bservers/videos
https://www.youtube.com/watch?v=u2wYS300pf0
https://www.youtube.com/watch?v=RAV-3PhGU7k
https://www.nature.com/articles/d41586-018-05143-8
/home/bill/web/ProjMajor/Electric Universe/Ginenthal 96mmdd Description of Great fire of Chicago.pdf
http://www.doiserbia.nb.rs/img/doi/0354-9836/2014 OnLine-First/0354-98361400036R.pdf
https://www.youtube.com/watch?v=4emMZmbkYlA&feature=youtu.be
/home/bill/web/Cool stuff/Hoyte & Schatten year - solar influence on climate & natural systems, graphs.pdf
https://en.wikipedia.org/wiki/Alexey_Grigorevich_Ivakhnenko
http://www.idsia.ch/~juergen/DeepLearning2July2014.pdf
http://people.idsia.ch/~juergen/deep-learning-overview.html
https://plus.google.com/100849856540000067209
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/Past & future worlds.html
/home/bill/web/Bill Howells videos/170930 Past and Future Worlds - a STEM for kids/
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/home/bill/web/Bill Howells videos/160901 Big Data, Deep Learning, and Safety/Howell 161220 Big Data, Deep Learning, Safety.ogv
/home/bill/web/webOther/Vaughan, Paul/Vaughan 160527 Ring_of_Fire_Volcano_Explosivity_El_Nino_La_Nina.png
http://www.suspicious0bservers.org
/home/bill/web/ProjMajor/Sun pandemics, health/Howell 151208 The Greatest of cycles - Human implications.docx
http://dx.doi.org/10.1016/j.chaos.2015.09.029
http://dx.doi.org/10.1016/j.chaos.2014.04.001
http://www.sciencedirect.com/science/article/pii/S0378375815001901
http://dx.doi.org/10.1016/j.chaos.2015.09.029
/home/bill/web/ProjMajor/Electric Universe/Howell 150627 Electric Universe - Climate & Svensmark Milankovic, Dark matter - Veizer-Shaviv.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/Script for Icebreaker Unchained.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/
/home/bill/web/Personal/Crazy ideas/A Riddle by, for, of, on, and with the Mind.pdf
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http://dx.doi.org/10.1016/j.chaos.2015.09.029
http://dx.doi.org/10.1016/j.chaos.2014.04.001
http://www.sciencedirect.com/science/article/pii/S0378375815001901
http://dx.doi.org/10.1016/j.chaos.2015.09.029
/home/bill/web/ProjMajor/Electric Universe/Howell 150627 Electric Universe - Climate & Svensmark Milankovic, Dark matter - Veizer-Shaviv.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/Script for Icebreaker Unchained.pdf
/home/bill/web/Bill Howells videos/150525 Icebreaker unchained  - We should have lost World War II/
/home/bill/web/Personal/Crazy ideas/A Riddle by, for, of, on, and with the Mind.pdf
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/home/bill/web/Neural nets/MindCode/Howell 150225 - MindCode Manifesto.odt
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/home/bill/web/Personal/Howell 140930 - 2014 Summer vacation travel log.pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 140622 Sun & SAM (Southern Annular Mode).pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 140524 ERSST EOF 1234.pdf
/home/bill/web/webOther/Yaskell, Steven/Yaskell 140302 Letter to the Vermont state government - Climate history.pdf
/home/bill/web/Bill Howells videos/140214 Nazis saved Norwegians video/Nazis saved Norwegian lives.ogv
/home/bill/web/webOther/Vaughan, Paul/Vaughan 140203 Multidecadal Sun-Climate-Change 101, Solar-Terrestrial Spatiotemporal Aggregation Primer on Trivial Extension of Milankovitch.PDF
/home/bill/web/webOther/Vaughan, Paul/Vaughan 131220 Sun-Climate 101 Solar-Terrestrial Primer 2.pdf
/home/bill/web/Neural nets/Howell 131013 Robots, Signal Processing and Control Theory - Random, stray thoughts.pdf
/home/bill/web/Neural nets/Howell 131011 fMRI - Random, stray thoughts.pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 130804 Solar Terrestrial Climate 101.PDF
/home/bill/web/Personal/z_history/130726 Deer collison/Car collision with a deer.HtmWeb.html
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/home/bill/web/Neural nets/Howell 131011 fMRI - Random, stray thoughts.pdf
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/home/bill/web/webOther/Vaughan, Paul/130501 Vaughan - Tlatov_Makarov_22a_JEV_R_Anim.gif
http://www.solarstation.ru/TL/PDF/tl_22.pdf
http://tallbloke.wordpress.com/2013/04/18/paul-vaughan-comparing-jupiter-earth-venus-alignment-cycles-with-variation-in-the-solar-rotation-period/
/home/bill/web/webOther/Vaughan, Paul/Vaughan 130316 - 60 Year Cycle of 27 Day Terracentric Solar Rotation.pdf
/home/bill/web/webOther/Vaughan, Paul/Vaughan 130224 - Solar Terrestrial Volatility Waves.pdf
/home/bill/web/Personal/121225 Howells recent changes - career and location.pdf
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Anderson 120725 Electric scarring of the Earth's surface - annotated & scanned.pdf
http://youtu.be/X9C4Tx2zC3c
/home/bill/web/ProjMajor/Electric Universe/Anderson - Electric scarring/Howell 120903 Paul Andersons Electric scarring of the Earth.pdf
/home/bill/web/ProjMajor/History/Howell 120729 Why was France's King Louis XIV called the Sun King.pdf
/home/bill/web/ProjMajor/History/Howell 120729 Fort St Louis in Quebec city - archaeological interpretation of food storage.pdf
/home/bill/web/ProjMajor/Electric Universe/Howell 120724 Gerald Pollack's Exclusion Zone water, Rob Johnson's electric bio-processes.pdf
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/home/bill/web/webWork/pMenuTopHelp.html#career
/home/bill/web/Cool stuff/Howell - Pandemics and the sun.pdf
/home/bill/web/ProjMajor/Climate - Kyoto Premise fraud/Howell - Are we ready for global cooling 14Mar06 longer version.pdf
/home/bill/web/Neural nets/Howell 2006 - Presentation, Genetic Specification of Recurrent Neural Networks Initial Thoughts.ppt
/home/bill/web/Neural nets/Howell 2006 - Genetic specification of neural networks, draft concepts and implications.pdf
/home/bill/web/Neural nets/Howell 2005 - Presentation, Junk DNA and Neural Networks conjecture on directions and implications.ppt
/home/bill/web/webWork/pMenuTopMenu.html#computer
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/home/bill/web/webWork/pMenuTopStatus.html#computer
/home/bill/web/webWork/pMenuTopCopyright.html#computer
/home/bill/web/webWork/pMenuTopHelp.html#computer
/home/bill/web/Qnial/
/home/bill/web/bin/
/home/bill/web/CompLangs/LibreOffice/Howell_codification_macLib/
/home/bill/web/webWork/pMenuTopMenu.html#computer
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/home/bill/web/webWork/pMenuTopStatus.html#computer
/home/bill/web/webWork/pMenuTopCopyright.html#computer
/home/bill/web/webWork/pMenuTopHelp.html#computer
/home/bill/web/Qnial/
/home/bill/web/bin/
/home/bill/web/CompLangs/LibreOffice/Howell_codification_macLib/
/home/bill/web/webWork/pMenuTopMenu.html#computer
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/home/bill/web/webWork/pMenuTopStatus.html#computer
/home/bill/web/webWork/pMenuTopCopyright.html#computer
/home/bill/web/webWork/pMenuTopHelp.html#computer
/home/bill/web/Qnial/
/home/bill/web/bin/
/home/bill/web/CompLangs/LibreOffice/Howell_codification_macLib/
/home/bill/web/webWork/pMenuTopMenu.html#computer
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/home/bill/web/webWork/pMenuTopStatus.html#computer
/home/bill/web/webWork/pMenuTopCopyright.html#computer
/home/bill/web/webWork/pMenuTopHelp.html#computer
/home/bill/web/Qnial/
/home/bill/web/bin/
/home/bill/web/CompLangs/LibreOffice/Howell_codification_macLib/
http://www.BillHowell.ca/Neural nets/
http://www.BillHowell.ca/ProjMini/
http://www.BillHowell.ca/ProjMajor/
http://www.BillHowell.ca/Neural nets/TrNNs_ART/
http://www.BillHowell.ca/
http://www.BillHowell.ca/ProjMini/PuetzUWS/
http://www.BillHowell.ca/Neural nets/
http://www.BillHowell.ca/ProjMini/
http://www.BillHowell.ca/ProjMajor/
http://www.BillHowell.ca/Neural nets/TrNNs_ART/
http://www.BillHowell.ca/
http://www.BillHowell.ca/ProjMini/PuetzUWS/
http://www.BillHowell.ca/Neural nets/
http://www.BillHowell.ca/ProjMini/
http://www.BillHowell.ca/ProjMajor/
http://www.BillHowell.ca/Neural nets/TrNNs_ART/
http://www.BillHowell.ca/
http://www.BillHowell.ca/ProjMini/PuetzUWS/
http://www.BillHowell.ca/Neural nets/
http://www.BillHowell.ca/ProjMini/
http://www.BillHowell.ca/ProjMajor/
http://www.BillHowell.ca/Neural nets/TrNNs_ART/
http://www.BillHowell.ca/
http://www.BillHowell.ca/ProjMini/PuetzUWS/
http://www.BillHowell.ca/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
http://www.BillHowell.ca/
http://www.BillHowell.ca/home.HtmWeb.html
http://www.BillHowell.ca/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
http://www.BillHowell.ca/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html
http://www.BillHowell.ca/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
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http://www.BillHowell.ca/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
http://www.BillHowell.ca/
http://www.BillHowell.ca/home.HtmWeb.html
http://www.BillHowell.ca/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
http://www.BillHowell.ca/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#personal
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/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
#WARNING: my [current situation, next steps], as of 04Apr2025
#Are [email, paper] responses composed by AI, with minimal [review, edit]?
#Serious Identity Theft [description, definition]
#Description
#My definition of 'Serious IDtheft cases'
#Low awareness of experts about what hackers can do : 
#Defensive measures
#Financial system fraud detection: the main line of defense
#[RCMP, police]: cannot help, but can file reports for their data
#My own defensive measures
#Heros, Mud&amp;Molasses, Bums
#My heros
#My mud&amp;molasses
#My bums: online tools for organized crime networks
#Surprise, surprise! funny, even if painful
#My : [stupid, lazy, naive, ignorant]; broken rules hypocrisy; Paranoia versus Naive
#My Broken rules hypocrisy
#My [stupid, lazy, naive, ignorant]
#My: Paranoia versus Naive versus Unimaginable reality
#One cannot grasp reality in this [virtual, hidden, criminal] world
#Time periods of the attack and my recovery
#IDtheft context: [GDP, population history]
#USA: IDtheft growth
#Canada: IDtheft growth
#[Wilhelm Able, David Fischer]: Price revolutions and the rythm of history
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html#Introduction
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html#[en,de]cryption bash programming system
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html#[cellPhone, email, internet, banking]
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html#Questions
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft: time periods of the attack and my recovery.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/letters/

https://en.wikipedia.org/wiki/The_Matrix
#My : [stupid, lazy, naive, ignorant]; broken rules hypocrisy; Paranoia versus Naive
https://www.equifax.com/
https://www.transunion.com/
https://antifraudcentre-centreantifraude.ca/index-eng.htm
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#My definition of 'serious IDtheft cases'
#My mud&amp;molasses
https://www.equifax.com/
https://www.transunion.com/
https://antifraudcentre-centreantifraude.ca/index-eng.htm
/home/bill/web/Personal/241115 IDtheft/images/Howell IDtheft.HtmWeb.html#Serious details beyond my [yap, arm wav]ing above
https://en.wikipedia.org/wiki/The_Big_Short_(film)
https://en.wikipedia.org/wiki/The_Big_Short_(film)
https://en.wikipedia.org/wiki/The_Big_Short
https://en.wikipedia.org/wiki/Charles_Dickens
https://en.wikipedia.org/wiki/Robert_William_Buss
https://en.wikipedia.org/wiki/Alice%27s_Adventures_in_Wonderland
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft: time periods of the attack and my recovery.HtmWeb.html
https://abruptearthchanges.com/2018/10/26/solar-history-ebook-out-now/
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/images/EU human [time, length, theme] multi-fractals.png
/home/bill/web/webWork/pMenuTopMenu.html#personal
./
/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
#WARNING: my [current situation, next steps], as of 04Apr2025
#Are [email, paper] responses composed by AI, with minimal [review, edit]?
#Serious Identity Theft [description, definition]
#Description
#My definition of 'Serious IDtheft cases'
#Low awareness of experts about what hackers can do : 
#Defensive measures
#Financial system fraud detection: the main line of defense
#[RCMP, police]: cannot help, but can file reports for their data
#My own defensive measures
#Heros, Mud&amp;Molasses, Bums
#My heros
#My mud&amp;molasses
#My bums: online tools for organized crime networks
#Surprise, surprise! funny, even if painful
#My : [stupid, lazy, naive, ignorant]; broken rules hypocrisy; Paranoia versus Naive
#My Broken rules hypocrisy
#My [stupid, lazy, naive, ignorant]
#My: Paranoia versus Naive versus Unimaginable reality
#One cannot grasp reality in this [virtual, hidden, criminal] world
#Time periods of the attack and my recovery
#IDtheft context: [GDP, population history]
#USA: IDtheft growth
#Canada: IDtheft growth
#[Wilhelm Able, David Fischer]: Price revolutions and the rythm of history
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html#Introduction
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html#[en,de]cryption bash programming system
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html#[cellPhone, email, internet, banking]
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft serious details.HtmWeb.html#Questions
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft: time periods of the attack and my recovery.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/letters/

https://en.wikipedia.org/wiki/The_Matrix
#My : [stupid, lazy, naive, ignorant]; broken rules hypocrisy; Paranoia versus Naive
https://www.equifax.com/
https://www.transunion.com/
https://antifraudcentre-centreantifraude.ca/index-eng.htm
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#My definition of 'serious IDtheft cases'
#My mud&amp;molasses
https://www.equifax.com/
https://www.transunion.com/
https://antifraudcentre-centreantifraude.ca/index-eng.htm
/home/bill/web/Personal/241115 IDtheft/images/Howell IDtheft.HtmWeb.html#Serious details beyond my [yap, arm wav]ing above
https://en.wikipedia.org/wiki/The_Big_Short_(film)
https://en.wikipedia.org/wiki/The_Big_Short_(film)
https://en.wikipedia.org/wiki/The_Big_Short
https://en.wikipedia.org/wiki/Charles_Dickens
https://en.wikipedia.org/wiki/Robert_William_Buss
https://en.wikipedia.org/wiki/Alice%27s_Adventures_in_Wonderland
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft: time periods of the attack and my recovery.HtmWeb.html
https://abruptearthchanges.com/2018/10/26/solar-history-ebook-out-now/
/home/bill/web/ProjMajor/Electric Universe/[time, length, theme] multifractals/images/EU human [time, length, theme] multi-fractals.png
/home/bill/web/Personal/241115 IDtheft/images/matrix-fields-and-towers-of-bodies2.jpg
/home/bill/web/Personal/241115 IDtheft/images/Keanu Reeves, human battery.jpeg
/home/bill/web/Personal/241115 IDtheft/images/Paramount Pictures, Adam McKay 2015 The Big Short.png
/home/bill/web/Personal/241115 IDtheft/images/Robert William Buss 1875- Dickens Dream.jpg
/home/bill/web/Personal/241115 IDtheft/images/Lewis Carroll 1865- Alices Adventures in Wonderland, Mad Tea Party.png
/home/bill/web/Personal/241115 IDtheft/images/identitytheft.org Total Fraud and Identity Theft Cases 2000-2024.png
/home/bill/web/Personal/241115 IDtheft/images/identitytheft.org Top three Identity Theft report types by year 2017-2021.png
/home/bill/web/Personal/241115 IDtheft/images/statista.com Canada Identity Theft rate 2012-2023.png

/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993 detrended.png
/home/bill/web/My sports & clubs/natural- Sacha Dobler/Sacha Dobler 26Oct2018 human deaths versus solar activity.jpg
/home/bill/web/My sports & clubs/natural- Suspicious/240815 Davidson geomag excursion events [6,12]ky periods back to 60 kyBP.png
/home/bill/web/Personal/241115 IDtheft/images/matrix-fields-and-towers-of-bodies2.jpg
/home/bill/web/Personal/241115 IDtheft/images/Keanu Reeves, human battery.jpeg
/home/bill/web/Personal/241115 IDtheft/images/Paramount Pictures, Adam McKay 2015 The Big Short.png
/home/bill/web/Personal/241115 IDtheft/images/Robert William Buss 1875- Dickens Dream.jpg
/home/bill/web/Personal/241115 IDtheft/images/Lewis Carroll 1865- Alices Adventures in Wonderland, Mad Tea Party.png
/home/bill/web/Personal/241115 IDtheft/images/identitytheft.org Total Fraud and Identity Theft Cases 2000-2024.png
/home/bill/web/Personal/241115 IDtheft/images/identitytheft.org Top three Identity Theft report types by year 2017-2021.png
/home/bill/web/Personal/241115 IDtheft/images/statista.com Canada Identity Theft rate 2012-2023.png

/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993.png
/home/bill/web/economics, markets/Fischer, David 1996 The Great Wave/Fig 0.01 Price of consumables in England 1201-1993 detrended.png
/home/bill/web/My sports & clubs/natural- Sacha Dobler/Sacha Dobler 26Oct2018 human deaths versus solar activity.jpg
/home/bill/web/My sports & clubs/natural- Suspicious/240815 Davidson geomag excursion events [6,12]ky periods back to 60 kyBP.png
/home/bill/web/webWork/pMenuTopMenu.html#personal
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/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#My [stupid, lazy, naive, ignorant]
/home/bill/web/bin/encrypt/
/home/bill/web/System_maintenance/security/241226 virus finds.txt
/home/bill/web/bin
/home/bill/web/bin/encrypt/Howell environmental variables.txt
/home/bill/web/webWork/pMenuTopMenu.html#personal
./
/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#My [stupid, lazy, naive, ignorant]
/home/bill/web/bin/encrypt/
/home/bill/web/System_maintenance/security/241226 virus finds.txt
/home/bill/web/bin
/home/bill/web/bin/encrypt/Howell environmental variables.txt
/home/bill/web/webWork/pMenuTopMenu.html#personal
./
/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#My [stupid, lazy, naive, ignorant]
/home/bill/web/bin/encrypt/
/home/bill/web/System_maintenance/security/241226 virus finds.txt
/home/bill/web/bin
/home/bill/web/bin/encrypt/Howell environmental variables.txt
/home/bill/web/webWork/pMenuTopMenu.html#personal
./
/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#My [stupid, lazy, naive, ignorant]
/home/bill/web/bin/encrypt/
/home/bill/web/System_maintenance/security/241226 virus finds.txt
/home/bill/web/bin
/home/bill/web/bin/encrypt/Howell environmental variables.txt
/home/bill/web/webWork/pMenuTopMenu.html#personal
./
/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#Time periods of the attack and my recovery
#???
#???
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#Time periods of the attack and my recovery
/home/bill/web/Personal/241115 IDtheft/timeLog of IDtheft [incomplete, censored].txt
/home/bill/web/webWork/pMenuTopMenu.html#personal
./
/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#Time periods of the attack and my recovery
#???
#???
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#Time periods of the attack and my recovery
/home/bill/web/Personal/241115 IDtheft/timeLog of IDtheft [incomplete, censored].txt
/home/bill/web/webWork/pMenuTopMenu.html#personal
./
/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#Time periods of the attack and my recovery
#???
#???
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#Time periods of the attack and my recovery
/home/bill/web/Personal/241115 IDtheft/timeLog of IDtheft [incomplete, censored].txt
/home/bill/web/webWork/pMenuTopMenu.html#personal
./
/home/bill/web/webWork/pMenuTopStatus.html#personal
/home/bill/web/webWork/pMenuTopCopyright.html#personal
/home/bill/web/webWork/pMenuTopHelp.html#personal
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#Time periods of the attack and my recovery
#???
#???
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html#Time periods of the attack and my recovery
/home/bill/web/Personal/241115 IDtheft/timeLog of IDtheft [incomplete, censored].txt
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.HtmWeb.html
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https://en.wikipedia.org/wiki/Johannes_Kepler
http://www.dailymail.co.uk/sciencetech/article-6008131/Great-Pyramid-Giza-focus-electromagnetic-energy-hidden-chambers.html
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
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https://arxiv.org/pdf/1708.07429.pdf
https://arxiv.org/pdf/1708.07429.pdf
https://pubs.aip.org/physicstoday/Online/29862/Quantum-mechanics-in-fractal-geometry
https://link.springer.com/article/10.1007/s11128-021-03171-4
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http://www.dailymail.co.uk/sciencetech/article-6008131/Great-Pyramid-Giza-focus-electromagnetic-energy-hidden-chambers.html
https://en.wikipedia.org/wiki/Roger_Joseph_Boscovich
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https://arxiv.org/pdf/1708.07429.pdf
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https://pubs.aip.org/physicstoday/Online/29862/Quantum-mechanics-in-fractal-geometry
https://link.springer.com/article/10.1007/s11128-021-03171-4
https://journals.aps.org/prd/abstract/10.1103/PhysRevD.96.105010
https://arxiv.org/pdf/2003.09604.pdf
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http://www.BillHowell.ca/References/Climate/Charbonneau 2002 - The rise and fall of the first sunspot model.pdf
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http://www.BillHowell.ca/References/Climate/Veizer etc cosmic review issi_helio.pdf
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http://www.BillHowell.ca/References/Climate/Charbonneau 2002 - The rise and fall of the first sunspot model.pdf
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/home/bill/web/???.HoNwp.html#Table of Contents
#???
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https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/
/home/bill/web/???.HoNwp.html#Table of Contents
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https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/
/home/bill/web/???.HoNwp.html#Table of Contents
#???
#???
https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/
/home/bill/web/???.HoNwp.html#Table of Contents
#???
#???
https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/
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#Table of Contents
#[myth, religion, philosophy, science]: [time, length, theme] multifractals [gnuplot, dataSets]
#Summary
#Introduction, Questions
#Follow-up questions
#[random, scattered, incomplete] comments
#Background context
#Calendar basis of [myth, religion, philosophy, science] (05Jan2026 email)
#Vedic Tradition (Ancient Hindu) Cycles of Yugas – A 12,000-Year Epoch
#Zoroastrian
#Jewish Torah
#Ivanka Charvatova's 'Solar Inertial Motion' (SIM)
#Stephen Puetz 'Universal Wave Series': not quite a fit
/home/bill/web/\1.dat
/home/bill/web/ProjMini/PuetzUWS/gnuplot/Puetz - Universal Waves Series 2011 tabTable.dat
/home/bill/web/ProjMini/PuetzUWS/gnuplot/Puetz - Universal Waves Series 2014 tabTable.dat
/home/bill/web/ProjMajor/Sun Charvatova/gnuplot/Charvatova Solar Inertial Motion, Milankovic [precession, obliquity, eccentricity] periods.dat
/home/bill/web/ProjMini/calendars/gnuplot/Vedic Yuga [human, divine] calendar series.dat
/home/bill/web/ProjMini/calendars/gnuplot/Howell divide by 360 Vedic Yuga human calendar series.dat
/home/bill/web/ProjMini/calendars/gnuplot/factor-of-7 geometric series.dat
/home/bill/web/ProjMini/calendars/gnuplot/Bible factor-of-7 disjointed series.dat
/home/bill/web/ProjMajor/Sun climate/Howell - sun-barycenter, climate periodicities.ods
#Introduction, Questions
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ben Shaggin
Follow-up questions
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#John Anthony West
the computability of symbol names
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ben Shaggin
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Gordon Freeman
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#wikipedia.org/wiki/Maya_calendar
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ken Johnson
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Stephen Howell
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Dwardu Cardona
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Simcha Jacobovici
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Stephen Puetz
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ivanka Charvatova
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html
/home/bill/web/ProjThink/Howell: Multiple Conflicting Hypothesis.HtmWeb.html
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ben Shaggin
https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/
https://en.wikipedia.org/wiki/Maya_calendar
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.HtmWeb.html
/home/bill/web/References/Climate/
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.html#Ivanka Charvatova and the limits of cycle analysis, real versus fairy tail
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.html#Charvatova [time, length, theme] multifractals: pattern matching
/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
/home/bill/web/ProjThink/Howell: Multiple Conflicting Hypothesis.HtmWeb.html
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/home/bill/web/???.HtmWeb.html#Table of Contents
#Table of Contents
#[myth, religion, philosophy, science]: [time, length, theme] multifractals [gnuplot, dataSets]
#Summary
#Introduction, Questions
#Follow-up questions
#[random, scattered, incomplete] comments
#Background context
#Calendar basis of [myth, religion, philosophy, science] (05Jan2026 email)
#Vedic Tradition (Ancient Hindu) Cycles of Yugas – A 12,000-Year Epoch
#Zoroastrian
#Jewish Torah
#Ivanka Charvatova's 'Solar Inertial Motion' (SIM)
#Stephen Puetz 'Universal Wave Series': not quite a fit
/home/bill/web/\1.dat
/home/bill/web/ProjMini/PuetzUWS/gnuplot/Puetz - Universal Waves Series 2011 tabTable.dat
/home/bill/web/ProjMini/PuetzUWS/gnuplot/Puetz - Universal Waves Series 2014 tabTable.dat
/home/bill/web/ProjMajor/Sun Charvatova/gnuplot/Charvatova Solar Inertial Motion, Milankovic [precession, obliquity, eccentricity] periods.dat
/home/bill/web/ProjMini/calendars/gnuplot/Vedic Yuga [human, divine] calendar series.dat
/home/bill/web/ProjMini/calendars/gnuplot/Howell divide by 360 Vedic Yuga human calendar series.dat
/home/bill/web/ProjMini/calendars/gnuplot/factor-of-7 geometric series.dat
/home/bill/web/ProjMini/calendars/gnuplot/Bible factor-of-7 disjointed series.dat
/home/bill/web/ProjMajor/Sun climate/Howell - sun-barycenter, climate periodicities.ods
#Introduction, Questions
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ben Shaggin
Follow-up questions
/home/bill/web/ProjMajor/Electric Universe/Peratt petroglyphs/Peratt - Auroral phenomena and petroglyphs.HtmWeb.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#John Anthony West
the computability of symbol names
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ben Shaggin
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Gordon Freeman
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#wikipedia.org/wiki/Maya_calendar
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ken Johnson
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Stephen Howell
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Dwardu Cardona
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Simcha Jacobovici
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Stephen Puetz
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ivanka Charvatova
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html
/home/bill/web/ProjThink/Howell: Multiple Conflicting Hypothesis.HtmWeb.html
/home/bill/web/References/mythology/5_references [Myth, Religion, Philosophy, Science] list.HtmWeb.html#Ben Shaggin
https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/
https://en.wikipedia.org/wiki/Maya_calendar
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.HtmWeb.html
/home/bill/web/References/Climate/
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.html#Ivanka Charvatova and the limits of cycle analysis, real versus fairy tail
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.html#Charvatova [time, length, theme] multifractals: pattern matching
/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html
/home/bill/web/ProjThink/Howell: Multiple Conflicting Hypothesis.HtmWeb.html
/home/bill/web/ProjMini/calendars/gnuplot/[Puetz UWS, Charvatova SIM, Vedic, Jewish Torah, Maya] periods.png
/home/bill/web/ProjMini/calendars/gnuplot/[Puetz UWS, Charvatova SIM, Vedic, Jewish Torah, Maya] periods.png
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/home/bill/web/ProjThink/Howell: Multiple Conflicting Hypothesis.HtmWeb.html
/home/bill/web/ProjThink/Howell: universal stupidity.HtmWeb.html
/home/bill/web/ProjThink/Howell: programmed intellectual robots.HtmWeb.html
/home/web/ProjMajor/Lies, Damned Lies, and Scientists/0_Howell: Dark optimism.HtmWeb.html




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/home/bill/web/ProjThink/Howell: Multiple Conflicting Hypothesis.HtmWeb.html
/home/bill/web/ProjThink/Howell: universal stupidity.HtmWeb.html
/home/bill/web/ProjThink/Howell: programmed intellectual robots.HtmWeb.html
/home/web/ProjMajor/Lies, Damned Lies, and Scientists/0_Howell: Dark optimism.HtmWeb.html




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/home/bill/web/ProjThink/Howell: Multiple Conflicting Hypothesis.HtmWeb.html
/home/bill/web/ProjThink/Howell: universal stupidity.HtmWeb.html
/home/bill/web/ProjThink/Howell: programmed intellectual robots.HtmWeb.html
/home/web/ProjMajor/Lies, Damned Lies, and Scientists/0_Howell: Dark optimism.HtmWeb.html




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/home/bill/web/ProjThink/Howell: Multiple Conflicting Hypothesis.HtmWeb.html
/home/bill/web/ProjThink/Howell: universal stupidity.HtmWeb.html
/home/bill/web/ProjThink/Howell: programmed intellectual robots.HtmWeb.html
/home/web/ProjMajor/Lies, Damned Lies, and Scientists/0_Howell: Dark optimism.HtmWeb.html




/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
/home/bill/web/Personal/241115 IDtheft/Howell IDtheft.html
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/home/bill/web/???.HoWeb.html#Table of Contents
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#???
/home/bill/web/ProjMini/Mythology/Cardona - gods are planets, planets are gods (plus Sun).HtmWeb.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.HtmWeb.html
/home/bill/web/References/Climate/Charvátová, Hejda Aug08 - A possible role of the solar inertial motion in climatic changes.pdf
/home/bill/web/References/Climate/Charvatova 1988 - The relations between solar motion and solar variability.pdf
/home/bill/web/References/Climate/Charvatova 1989 - On the relation between Solar motion and solar activity in the years 1730-80 and 1910-60 AD.pdf
/home/bill/web/References/Climate/Charvatova 1988 - The relations between solar motion and solar variability.pdf
/home/bill/web/References/Climate//Charvatova 2000 - Can origin of the 2400-year cycle of solar activity be caused by solar inertial motion.pdf
/home/bill/web/References/Climate/Charvatova, Strestik 2004 - Periodicities between 6 and 16 years in surface air temperature in possible relation to solar inertial motion.pdf
/home/bill/web/References/Climate/Charvátová, Hejda Aug08 - A possible role of the solar inertial motion in climatic changes.pdf
/home/bill/web/References/Climate/Charvatova 08Mar2008 list of publications.doc
/home/bill/web/References/Climate/Charvátová Nov08 - Long-term predictive assessments of solar and geomagnetic activities - 1840-1905 and 1980-2045.pdf
/home/bill/web/References/Climate/Charvátová Aug2010 - Long-term relations between the solar inertial motion (SIM) and solar, geomagnetic, volcanic activities and climate.ppt
/home/bill/web/References/Climate/Charvátová etal 2011 - Chinese tombs oriented by a compass, evidence from paleomagnetic changes versus the age of tombs.pdf
/home/bill/web/References/Climate/Charvátová, Hejda 21Feb2014 Responses of the basic cycles of 178.7 and 2402 yr in solar–terrestrial phenomena during the Holocene.pdf
https://www.youtube.com/watch?v=xOTDTs9lio0
/home/bill/web/ProjMini/Mythology/Freeman 2009 Canadas Stonehenge, Siksika territory, Howell review.pHtmWeb.html
/home/bill/web/My sports & clubs/natural- Suspicious/references/sunClimate- Precession and ice sheet control of hydroclimate in Arctic East Beringia over the past 240,000 years 22Jul2025 Fei Guo etal.pdf
/home/bill/web/ProjMajor/Sun civilisations/_Civilisations and the sun.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/ProjMajor/Sun civilisations/Howell - radioisotopes and history.jpg
/home/bill/web/webOther/Howell, Steve/Steve Howell: Genesis creation - in a beginning.HtmWeb.html
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 Exodus proofs, transcript.HtmWeb.html
???
/home/bill/web/webOther/Howell, Steve/0_Steve notes.txt

/home/bill/web/References/Climate/Landscheidt 1999 - Extrema in sunspot cycle linked to sun's motion.odt
/home/bill/web/References/Climate/Landscheidt 2002-2003 - New Little Ice Age Instead of Global Warming.pdf
/home/bill/web/References/Climate/Landscheidt 1999 - Extrema in sunspot cycle linked to sun's motion.odt

/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html

https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/


/home/bill/web/webOther/Vaughan, Paul/
/home/bill/web/webOther/Vaughan, Paul/Vaughan 09Jul09 - Chandler Period.png
/home/bill/web/webOther/Vaughan, Paul/Vaughan 01Jun09 to Charvatova, Howell - Sun-climate, Vienna.eml
/home/bill/web/webOther/Vaughan, Paul/Vaughan 090601 - Adjustment of figure 3 from Mursula & Zieger 2001 - Long-term north-south asymmetry in solar wind speed inferred from geomagnetic activity, GA Map XL.png
/home/bill/web/webOther/Vaughan, Paul/Vaughan 17Jul09 - Sunspot Cycle Period.PNG
https://www.magicalegypt.com
/home/bill/web/webOther/Wickson, Steven/900y cycle/Wickson 900 year [climate, history] cycle, Howell overview.HtmWeb.html
https://en.wikipedia.org/wiki/Maya_calendar
/home/bill/web/References/0_References.ods





/home/bill/web/ProjThink/Howell: my thinking is out of balance.HtmWeb.html
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/home/bill/web/???.HoWeb.html#Table of Contents
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/home/bill/web/ProjMini/Mythology/Cardona - gods are planets, planets are gods (plus Sun).HtmWeb.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.HtmWeb.html
/home/bill/web/References/Climate/Charvátová, Hejda Aug08 - A possible role of the solar inertial motion in climatic changes.pdf
/home/bill/web/References/Climate/Charvatova 1988 - The relations between solar motion and solar variability.pdf
/home/bill/web/References/Climate/Charvatova 1989 - On the relation between Solar motion and solar activity in the years 1730-80 and 1910-60 AD.pdf
/home/bill/web/References/Climate/Charvatova 1988 - The relations between solar motion and solar variability.pdf
/home/bill/web/References/Climate//Charvatova 2000 - Can origin of the 2400-year cycle of solar activity be caused by solar inertial motion.pdf
/home/bill/web/References/Climate/Charvatova, Strestik 2004 - Periodicities between 6 and 16 years in surface air temperature in possible relation to solar inertial motion.pdf
/home/bill/web/References/Climate/Charvátová, Hejda Aug08 - A possible role of the solar inertial motion in climatic changes.pdf
/home/bill/web/References/Climate/Charvatova 08Mar2008 list of publications.doc
/home/bill/web/References/Climate/Charvátová Nov08 - Long-term predictive assessments of solar and geomagnetic activities - 1840-1905 and 1980-2045.pdf
/home/bill/web/References/Climate/Charvátová Aug2010 - Long-term relations between the solar inertial motion (SIM) and solar, geomagnetic, volcanic activities and climate.ppt
/home/bill/web/References/Climate/Charvátová etal 2011 - Chinese tombs oriented by a compass, evidence from paleomagnetic changes versus the age of tombs.pdf
/home/bill/web/References/Climate/Charvátová, Hejda 21Feb2014 Responses of the basic cycles of 178.7 and 2402 yr in solar–terrestrial phenomena during the Holocene.pdf
https://www.youtube.com/watch?v=xOTDTs9lio0
/home/bill/web/ProjMini/Mythology/Freeman 2009 Canadas Stonehenge, Siksika territory, Howell review.pHtmWeb.html
/home/bill/web/My sports & clubs/natural- Suspicious/references/sunClimate- Precession and ice sheet control of hydroclimate in Arctic East Beringia over the past 240,000 years 22Jul2025 Fei Guo etal.pdf
/home/bill/web/ProjMajor/Sun civilisations/_Civilisations and the sun.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/ProjMajor/Sun civilisations/Howell - radioisotopes and history.jpg
/home/bill/web/webOther/Howell, Steve/Steve Howell: Genesis creation - in a beginning.HtmWeb.html
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 Exodus proofs, transcript.HtmWeb.html
???
/home/bill/web/webOther/Howell, Steve/0_Steve notes.txt

/home/bill/web/References/Climate/Landscheidt 1999 - Extrema in sunspot cycle linked to sun's motion.odt
/home/bill/web/References/Climate/Landscheidt 2002-2003 - New Little Ice Age Instead of Global Warming.pdf
/home/bill/web/References/Climate/Landscheidt 1999 - Extrema in sunspot cycle linked to sun's motion.odt

/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html

https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/


/home/bill/web/webOther/Vaughan, Paul/
/home/bill/web/webOther/Vaughan, Paul/Vaughan 09Jul09 - Chandler Period.png
/home/bill/web/webOther/Vaughan, Paul/Vaughan 01Jun09 to Charvatova, Howell - Sun-climate, Vienna.eml
/home/bill/web/webOther/Vaughan, Paul/Vaughan 090601 - Adjustment of figure 3 from Mursula & Zieger 2001 - Long-term north-south asymmetry in solar wind speed inferred from geomagnetic activity, GA Map XL.png
/home/bill/web/webOther/Vaughan, Paul/Vaughan 17Jul09 - Sunspot Cycle Period.PNG
https://www.magicalegypt.com
/home/bill/web/webOther/Wickson, Steven/900y cycle/Wickson 900 year [climate, history] cycle, Howell overview.HtmWeb.html
https://en.wikipedia.org/wiki/Maya_calendar
/home/bill/web/References/0_References.ods





/home/bill/web/ProjThink/Howell: my thinking is out of balance.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#<:class:>
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/home/bill/web/???.HoWeb.html#Table of Contents
#???
#???
/home/bill/web/ProjMini/Mythology/Cardona - gods are planets, planets are gods (plus Sun).HtmWeb.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.HtmWeb.html
/home/bill/web/References/Climate/Charvátová, Hejda Aug08 - A possible role of the solar inertial motion in climatic changes.pdf
/home/bill/web/References/Climate/Charvatova 1988 - The relations between solar motion and solar variability.pdf
/home/bill/web/References/Climate/Charvatova 1989 - On the relation between Solar motion and solar activity in the years 1730-80 and 1910-60 AD.pdf
/home/bill/web/References/Climate/Charvatova 1988 - The relations between solar motion and solar variability.pdf
/home/bill/web/References/Climate//Charvatova 2000 - Can origin of the 2400-year cycle of solar activity be caused by solar inertial motion.pdf
/home/bill/web/References/Climate/Charvatova, Strestik 2004 - Periodicities between 6 and 16 years in surface air temperature in possible relation to solar inertial motion.pdf
/home/bill/web/References/Climate/Charvátová, Hejda Aug08 - A possible role of the solar inertial motion in climatic changes.pdf
/home/bill/web/References/Climate/Charvatova 08Mar2008 list of publications.doc
/home/bill/web/References/Climate/Charvátová Nov08 - Long-term predictive assessments of solar and geomagnetic activities - 1840-1905 and 1980-2045.pdf
/home/bill/web/References/Climate/Charvátová Aug2010 - Long-term relations between the solar inertial motion (SIM) and solar, geomagnetic, volcanic activities and climate.ppt
/home/bill/web/References/Climate/Charvátová etal 2011 - Chinese tombs oriented by a compass, evidence from paleomagnetic changes versus the age of tombs.pdf
/home/bill/web/References/Climate/Charvátová, Hejda 21Feb2014 Responses of the basic cycles of 178.7 and 2402 yr in solar–terrestrial phenomena during the Holocene.pdf
https://www.youtube.com/watch?v=xOTDTs9lio0
/home/bill/web/ProjMini/Mythology/Freeman 2009 Canadas Stonehenge, Siksika territory, Howell review.pHtmWeb.html
/home/bill/web/My sports & clubs/natural- Suspicious/references/sunClimate- Precession and ice sheet control of hydroclimate in Arctic East Beringia over the past 240,000 years 22Jul2025 Fei Guo etal.pdf
/home/bill/web/ProjMajor/Sun civilisations/_Civilisations and the sun.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/ProjMajor/Sun civilisations/Howell - radioisotopes and history.jpg
/home/bill/web/webOther/Howell, Steve/Steve Howell: Genesis creation - in a beginning.HtmWeb.html
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 Exodus proofs, transcript.HtmWeb.html
???
/home/bill/web/webOther/Howell, Steve/0_Steve notes.txt

/home/bill/web/References/Climate/Landscheidt 1999 - Extrema in sunspot cycle linked to sun's motion.odt
/home/bill/web/References/Climate/Landscheidt 2002-2003 - New Little Ice Age Instead of Global Warming.pdf
/home/bill/web/References/Climate/Landscheidt 1999 - Extrema in sunspot cycle linked to sun's motion.odt

/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html

https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/


/home/bill/web/webOther/Vaughan, Paul/
/home/bill/web/webOther/Vaughan, Paul/Vaughan 09Jul09 - Chandler Period.png
/home/bill/web/webOther/Vaughan, Paul/Vaughan 01Jun09 to Charvatova, Howell - Sun-climate, Vienna.eml
/home/bill/web/webOther/Vaughan, Paul/Vaughan 090601 - Adjustment of figure 3 from Mursula & Zieger 2001 - Long-term north-south asymmetry in solar wind speed inferred from geomagnetic activity, GA Map XL.png
/home/bill/web/webOther/Vaughan, Paul/Vaughan 17Jul09 - Sunspot Cycle Period.PNG
https://www.magicalegypt.com
/home/bill/web/webOther/Wickson, Steven/900y cycle/Wickson 900 year [climate, history] cycle, Howell overview.HtmWeb.html
https://en.wikipedia.org/wiki/Maya_calendar
/home/bill/web/References/0_References.ods





/home/bill/web/ProjThink/Howell: my thinking is out of balance.HtmWeb.html
/home/bill/web/webWork/pMenuTopMenu.html#<:class:>
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/home/bill/web/???.HoWeb.html#Table of Contents
#???
#???
/home/bill/web/ProjMini/Mythology/Cardona - gods are planets, planets are gods (plus Sun).HtmWeb.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/ProjMajor/Sun Charvatova/_Charvatova - solar inertial motion & activity.HtmWeb.html
/home/bill/web/References/Climate/Charvátová, Hejda Aug08 - A possible role of the solar inertial motion in climatic changes.pdf
/home/bill/web/References/Climate/Charvatova 1988 - The relations between solar motion and solar variability.pdf
/home/bill/web/References/Climate/Charvatova 1989 - On the relation between Solar motion and solar activity in the years 1730-80 and 1910-60 AD.pdf
/home/bill/web/References/Climate/Charvatova 1988 - The relations between solar motion and solar variability.pdf
/home/bill/web/References/Climate//Charvatova 2000 - Can origin of the 2400-year cycle of solar activity be caused by solar inertial motion.pdf
/home/bill/web/References/Climate/Charvatova, Strestik 2004 - Periodicities between 6 and 16 years in surface air temperature in possible relation to solar inertial motion.pdf
/home/bill/web/References/Climate/Charvátová, Hejda Aug08 - A possible role of the solar inertial motion in climatic changes.pdf
/home/bill/web/References/Climate/Charvatova 08Mar2008 list of publications.doc
/home/bill/web/References/Climate/Charvátová Nov08 - Long-term predictive assessments of solar and geomagnetic activities - 1840-1905 and 1980-2045.pdf
/home/bill/web/References/Climate/Charvátová Aug2010 - Long-term relations between the solar inertial motion (SIM) and solar, geomagnetic, volcanic activities and climate.ppt
/home/bill/web/References/Climate/Charvátová etal 2011 - Chinese tombs oriented by a compass, evidence from paleomagnetic changes versus the age of tombs.pdf
/home/bill/web/References/Climate/Charvátová, Hejda 21Feb2014 Responses of the basic cycles of 178.7 and 2402 yr in solar–terrestrial phenomena during the Holocene.pdf
https://www.youtube.com/watch?v=xOTDTs9lio0
/home/bill/web/ProjMini/Mythology/Freeman 2009 Canadas Stonehenge, Siksika territory, Howell review.pHtmWeb.html
/home/bill/web/My sports & clubs/natural- Suspicious/references/sunClimate- Precession and ice sheet control of hydroclimate in Arctic East Beringia over the past 240,000 years 22Jul2025 Fei Guo etal.pdf
/home/bill/web/ProjMajor/Sun civilisations/_Civilisations and the sun.html
/home/bill/web/ProjMajor/Sun civilisations/Howell - Mega-Life, Mega-Death and the Sun, the rise and fall of civilisations.pdf
/home/bill/web/ProjMajor/Sun civilisations/Howell - radioisotopes and history.jpg
/home/bill/web/webOther/Howell, Steve/Steve Howell: Genesis creation - in a beginning.HtmWeb.html
/home/bill/web/ProjMajor/History/Jacobovici, Exodus plagues/Jacobovici 17Nov2018 Exodus proofs, transcript.HtmWeb.html
???
/home/bill/web/webOther/Howell, Steve/0_Steve notes.txt

/home/bill/web/References/Climate/Landscheidt 1999 - Extrema in sunspot cycle linked to sun's motion.odt
/home/bill/web/References/Climate/Landscheidt 2002-2003 - New Little Ice Age Instead of Global Warming.pdf
/home/bill/web/References/Climate/Landscheidt 1999 - Extrema in sunspot cycle linked to sun's motion.odt

/home/bill/web/ProjMini/PuetzUWS/Howell - Quick introduction to Puetz Universal Wave Series (UWS) fractal time.HtmWeb.html

https://spoileralert.au/cycles-of-12000-years-in-vedic-mayan-and-torah-traditions/


/home/bill/web/webOther/Vaughan, Paul/
/home/bill/web/webOther/Vaughan, Paul/Vaughan 09Jul09 - Chandler Period.png
/home/bill/web/webOther/Vaughan, Paul/Vaughan 01Jun09 to Charvatova, Howell - Sun-climate, Vienna.eml
/home/bill/web/webOther/Vaughan, Paul/Vaughan 090601 - Adjustment of figure 3 from Mursula & Zieger 2001 - Long-term north-south asymmetry in solar wind speed inferred from geomagnetic activity, GA Map XL.png
/home/bill/web/webOther/Vaughan, Paul/Vaughan 17Jul09 - Sunspot Cycle Period.PNG
https://www.magicalegypt.com
/home/bill/web/webOther/Wickson, Steven/900y cycle/Wickson 900 year [climate, history] cycle, Howell overview.HtmWeb.html
https://en.wikipedia.org/wiki/Maya_calendar
/home/bill/web/References/0_References.ods





/home/bill/web/ProjThink/Howell: my thinking is out of balance.HtmWeb.html
#The_Old_Testament_of_the_King_James_Version_of_the_Bible
#The_First_Book_of_Moses_Called_Genesis
#The_Second_Book_of_Moses_Called_Exodus
#The_Third_Book_of_Moses_Called_Leviticus
#The_Fourth_Book_of_Moses_Called_Numbers
#The_Fifth_Book_of_Moses_Called_Deuteronomy
#The_Book_of_Joshua
#The_Book_of_Judges
#The_Book_of_Ruth
#The_First_Book_of_Samuel
#The_Second_Book_of_Samuel
#The_First_Book_of_the_Kings
#The_Second_Book_of_the_Kings
#The_First_Book_of_the_Chronicles
#The_Second_Book_of_the_Chronicles
#Ezra
#The_Book_of_Nehemiah
#The_Book_of_Esther
#The_Book_of_Job
#The_Book_of_Psalms
#The_Proverbs
#Ecclesiastes
#The_Song_of_Solomon
#The_Book_of_the_Prophet_Isaiah
#The_Book_of_the_Prophet_Jeremiah
#The_Lamentations_of_Jeremiah
#The_Book_of_the_Prophet_Ezekiel
#The_Book_of_Daniel
#Hosea
#Joel
#Amos
#Obadiah
#Jonah
#Micah
#Nahum
#Habakkuk
#Zephaniah
#Haggai
#Zechariah
#Malachi
#The_New_Testament_of_the_King_James_Bible
#The_Gospel_According_to_Saint_Matthew
#The_Gospel_According_to_Saint_Mark
#The_Gospel_According_to_Saint_Luke
#The_Gospel_According_to_Saint_John
#The_Acts_of_the_Apostles
#The_Epistle_of_Paul_the_Apostle_to_the_Romans
#The_First_Epistle_of_Paul_the_Apostle_to_the_Corinthians
#The_Second_Epistle_of_Paul_the_Apostle_to_the_Corinthians
#The_Epistle_of_Paul_the_Apostle_to_the_Galatians
#The_Epistle_of_Paul_the_Apostle_to_the_Ephesians
#The_Epistle_of_Paul_the_Apostle_to_the_Philippians
#The_Epistle_of_Paul_the_Apostle_to_the_Colossians
#The_First_Epistle_of_Paul_the_Apostle_to_the_Thessalonians
#The_Second_Epistle_of_Paul_the_Apostle_to_the_Thessalonians
#The_First_Epistle_of_Paul_the_Apostle_to_Timothy
#The_Second_Epistle_of_Paul_the_Apostle_to_Timothy
#The_Epistle_of_Paul_the_Apostle_to_Titus
#The_Epistle_of_Paul_the_Apostle_to_Philemon
#The_Epistle_of_Paul_the_Apostle_to_the_Hebrews
#The_General_Epistle_of_James
#The_First_Epistle_General_of_Peter
#The_Second_General_Epistle_of_Peter
#The_First_Epistle_General_of_John
#The_Second_Epistle_General_of_John
#The_Third_Epistle_General_of_John
#The_General_Epistle_of_Jude
#The_Revelation_of_Saint_John_the_Divine
#The_Old_Testament_of_the_King_James_Version_of_the_Bible
#The_First_Book_of_Moses_Called_Genesis
#The_Second_Book_of_Moses_Called_Exodus
#The_Third_Book_of_Moses_Called_Leviticus
#The_Fourth_Book_of_Moses_Called_Numbers
#The_Fifth_Book_of_Moses_Called_Deuteronomy
#The_Book_of_Joshua
#The_Book_of_Judges
#The_Book_of_Ruth
#The_First_Book_of_Samuel
#The_Second_Book_of_Samuel
#The_First_Book_of_the_Kings
#The_Second_Book_of_the_Kings
#The_First_Book_of_the_Chronicles
#The_Second_Book_of_the_Chronicles
#Ezra
#The_Book_of_Nehemiah
#The_Book_of_Esther
#The_Book_of_Job
#The_Book_of_Psalms
#The_Proverbs
#Ecclesiastes
#The_Song_of_Solomon
#The_Book_of_the_Prophet_Isaiah
#The_Book_of_the_Prophet_Jeremiah
#The_Lamentations_of_Jeremiah
#The_Book_of_the_Prophet_Ezekiel
#The_Book_of_Daniel
#Hosea
#Joel
#Amos
#Obadiah
#Jonah
#Micah
#Nahum
#Habakkuk
#Zephaniah
#Haggai
#Zechariah
#Malachi
#The_New_Testament_of_the_King_James_Bible
#The_Gospel_According_to_Saint_Matthew
#The_Gospel_According_to_Saint_Mark
#The_Gospel_According_to_Saint_Luke
#The_Gospel_According_to_Saint_John
#The_Acts_of_the_Apostles
#The_Epistle_of_Paul_the_Apostle_to_the_Romans
#The_First_Epistle_of_Paul_the_Apostle_to_the_Corinthians
#The_Second_Epistle_of_Paul_the_Apostle_to_the_Corinthians
#The_Epistle_of_Paul_the_Apostle_to_the_Galatians
#The_Epistle_of_Paul_the_Apostle_to_the_Ephesians
#The_Epistle_of_Paul_the_Apostle_to_the_Philippians
#The_Epistle_of_Paul_the_Apostle_to_the_Colossians
#The_First_Epistle_of_Paul_the_Apostle_to_the_Thessalonians
#The_Second_Epistle_of_Paul_the_Apostle_to_the_Thessalonians
#The_First_Epistle_of_Paul_the_Apostle_to_Timothy
#The_Second_Epistle_of_Paul_the_Apostle_to_Timothy
#The_Epistle_of_Paul_the_Apostle_to_Titus
#The_Epistle_of_Paul_the_Apostle_to_Philemon
#The_Epistle_of_Paul_the_Apostle_to_the_Hebrews
#The_General_Epistle_of_James
#The_First_Epistle_General_of_Peter
#The_Second_General_Epistle_of_Peter
#The_First_Epistle_General_of_John
#The_Second_Epistle_General_of_John
#The_Third_Epistle_General_of_John
#The_General_Epistle_of_Jude
#The_Revelation_of_Saint_John_the_Divine
#The_Old_Testament_of_the_King_James_Version_of_the_Bible
#The_First_Book_of_Moses_Called_Genesis
#The_Second_Book_of_Moses_Called_Exodus
#The_Third_Book_of_Moses_Called_Leviticus
#The_Fourth_Book_of_Moses_Called_Numbers
#The_Fifth_Book_of_Moses_Called_Deuteronomy
#The_Book_of_Joshua
#The_Book_of_Judges
#The_Book_of_Ruth
#The_First_Book_of_Samuel
#The_Second_Book_of_Samuel
#The_First_Book_of_the_Kings
#The_Second_Book_of_the_Kings
#The_First_Book_of_the_Chronicles
#The_Second_Book_of_the_Chronicles
#Ezra
#The_Book_of_Nehemiah
#The_Book_of_Esther
#The_Book_of_Job
#The_Book_of_Psalms
#The_Proverbs
#Ecclesiastes
#The_Song_of_Solomon
#The_Book_of_the_Prophet_Isaiah
#The_Book_of_the_Prophet_Jeremiah
#The_Lamentations_of_Jeremiah
#The_Book_of_the_Prophet_Ezekiel
#The_Book_of_Daniel
#Hosea
#Joel
#Amos
#Obadiah
#Jonah
#Micah
#Nahum
#Habakkuk
#Zephaniah
#Haggai
#Zechariah
#Malachi
#The_New_Testament_of_the_King_James_Bible
#The_Gospel_According_to_Saint_Matthew
#The_Gospel_According_to_Saint_Mark
#The_Gospel_According_to_Saint_Luke
#The_Gospel_According_to_Saint_John
#The_Acts_of_the_Apostles
#The_Epistle_of_Paul_the_Apostle_to_the_Romans
#The_First_Epistle_of_Paul_the_Apostle_to_the_Corinthians
#The_Second_Epistle_of_Paul_the_Apostle_to_the_Corinthians
#The_Epistle_of_Paul_the_Apostle_to_the_Galatians
#The_Epistle_of_Paul_the_Apostle_to_the_Ephesians
#The_Epistle_of_Paul_the_Apostle_to_the_Philippians
#The_Epistle_of_Paul_the_Apostle_to_the_Colossians
#The_First_Epistle_of_Paul_the_Apostle_to_the_Thessalonians
#The_Second_Epistle_of_Paul_the_Apostle_to_the_Thessalonians
#The_First_Epistle_of_Paul_the_Apostle_to_Timothy
#The_Second_Epistle_of_Paul_the_Apostle_to_Timothy
#The_Epistle_of_Paul_the_Apostle_to_Titus
#The_Epistle_of_Paul_the_Apostle_to_Philemon
#The_Epistle_of_Paul_the_Apostle_to_the_Hebrews
#The_General_Epistle_of_James
#The_First_Epistle_General_of_Peter
#The_Second_General_Epistle_of_Peter
#The_First_Epistle_General_of_John
#The_Second_Epistle_General_of_John
#The_Third_Epistle_General_of_John
#The_General_Epistle_of_Jude
#The_Revelation_of_Saint_John_the_Divine
#The_Old_Testament_of_the_King_James_Version_of_the_Bible
#The_First_Book_of_Moses_Called_Genesis
#The_Second_Book_of_Moses_Called_Exodus
#The_Third_Book_of_Moses_Called_Leviticus
#The_Fourth_Book_of_Moses_Called_Numbers
#The_Fifth_Book_of_Moses_Called_Deuteronomy
#The_Book_of_Joshua
#The_Book_of_Judges
#The_Book_of_Ruth
#The_First_Book_of_Samuel
#The_Second_Book_of_Samuel
#The_First_Book_of_the_Kings
#The_Second_Book_of_the_Kings
#The_First_Book_of_the_Chronicles
#The_Second_Book_of_the_Chronicles
#Ezra
#The_Book_of_Nehemiah
#The_Book_of_Esther
#The_Book_of_Job
#The_Book_of_Psalms
#The_Proverbs
#Ecclesiastes
#The_Song_of_Solomon
#The_Book_of_the_Prophet_Isaiah
#The_Book_of_the_Prophet_Jeremiah
#The_Lamentations_of_Jeremiah
#The_Book_of_the_Prophet_Ezekiel
#The_Book_of_Daniel
#Hosea
#Joel
#Amos
#Obadiah
#Jonah
#Micah
#Nahum
#Habakkuk
#Zephaniah
#Haggai
#Zechariah
#Malachi
#The_New_Testament_of_the_King_James_Bible
#The_Gospel_According_to_Saint_Matthew
#The_Gospel_According_to_Saint_Mark
#The_Gospel_According_to_Saint_Luke
#The_Gospel_According_to_Saint_John
#The_Acts_of_the_Apostles
#The_Epistle_of_Paul_the_Apostle_to_the_Romans
#The_First_Epistle_of_Paul_the_Apostle_to_the_Corinthians
#The_Second_Epistle_of_Paul_the_Apostle_to_the_Corinthians
#The_Epistle_of_Paul_the_Apostle_to_the_Galatians
#The_Epistle_of_Paul_the_Apostle_to_the_Ephesians
#The_Epistle_of_Paul_the_Apostle_to_the_Philippians
#The_Epistle_of_Paul_the_Apostle_to_the_Colossians
#The_First_Epistle_of_Paul_the_Apostle_to_the_Thessalonians
#The_Second_Epistle_of_Paul_the_Apostle_to_the_Thessalonians
#The_First_Epistle_of_Paul_the_Apostle_to_Timothy
#The_Second_Epistle_of_Paul_the_Apostle_to_Timothy
#The_Epistle_of_Paul_the_Apostle_to_Titus
#The_Epistle_of_Paul_the_Apostle_to_Philemon
#The_Epistle_of_Paul_the_Apostle_to_the_Hebrews
#The_General_Epistle_of_James
#The_First_Epistle_General_of_Peter
#The_Second_General_Epistle_of_Peter
#The_First_Epistle_General_of_John
#The_Second_Epistle_General_of_John
#The_Third_Epistle_General_of_John
#The_General_Epistle_of_Jude
#The_Revelation_of_Saint_John_the_Divine
#endnote
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#r1
#r2
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#note1.1
#note1.5
#note1.6
#note1.7
#note1.8
#note1.9
#note1.10
#note1.11
#note1.12
#note1.13
#note1.14
#note1.15
#note1.16
#note1.17
#note1.18
#note1.19
#note1.20
#note1.21
#note1.22
#c_2.23
#note1.23
#pl01
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#nref1.2
#nref1.3
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#nref1.13
#nref1.14
#nref1.15
#nref1.16
#nref1.17
#nref1.18
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#nref1.20
#nref1.21
#nref1.22
#nref1.23
#r5
#r6
#r7
#r8
#r9
#note2.1
#note2.2
#note2.4
#nref2.1
#nref2.2
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#nref2.4
#nref2.5
#note3.1
#note3.2
#note3.3
#nref3.1
#nref3.2
#nref3.3
#note1.8
#note4.1
#note4.2
#nref4.1
#nref4.2
#note5.1
#note5.2
#note5.4
#nref5.1
#nref5.2
#nref5.3
#nref5.4
#nref5.5
#r10
#note6.1
#nref6.1
#note7.1
#note7.2
#nref7.1
#nref7.2
#note8.1
#note8.2
#note8.3
#nref8.1
#nref8.2
#nref8.3
#note9.1
#nref9.1
#note12.1
#note12.2
#note12.3
#note12.4
#nref12.1
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#note1.20
#nref12.4
#note13.1
#note13.2
#nref13.1
#nref13.2
#note14.1
#note14.2
#note14.3
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#note14.5
#note14.6
#nref14.1
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#nref14.6
#note15.1
#note15.2
#note15.3
#note15.4
#note15.5
#note15.6
#note15.7
#note15.8
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#note15.12
#note15.13
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#note15.15
#note15.16
#note15.17
#note15.18
#note15.19
#nref15.1
#nref15.2
#nref15.3
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#nref15.7
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#nref15.17
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#r11
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#note17.2
#note17.3
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#note17.5
#note17.6
#note17.7
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#note17.16
#note17.17
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#note17.21
#note17.22
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#note17.24
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#c_47.20
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#c_55.31
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#c_57.17
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#note19.1
#note19.2
#note19.3
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#note21.2
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#r34
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#c_61.23
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#c_74.27
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#r50
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#note35.1
#note35.2
#note35.3
#note35.4
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#note35.6
#nref35.1
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#c_83.27
#nref35.3
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#nref35.6
#pl12
#pl13
#note37.1
#nref37.1
#note38.1
#note38.2
#note38.3
#note38.4
#nref38.1
#nref38.2
#nref38.3
#nref38.4
#note125.15
#r52
#r53
#r54
#note39.1
#note39.2
#note39.3
#note39.4
#nref39.1
#note40.5
#nref39.2
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#nref39.4
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#r56
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#note40.2
#note40.3
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#note40.10
#nref40.1
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#r57
#r58
#r59
#r60
#note41.1
#note41.2
#note41.3
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#note41.6
#nref41.1
#pl15
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#nref41.3
#nref41.4
#nref41.5
#nref41.6
#pl15
#pl16
#note42.1
#note42.2
#note42.3
#note42.4
#note42.5
#note42.7
#note42.8
#c_92.60
#c_97.11
#note42.9
#note42.10
#note42.11
#note42.12
#note42.13
#nref42.1
#nref42.2
#nref42.3
#nref42.4
#nref42.5
#nref42.6
#nref42.7
#nref42.8
#nref42.9
#nref42.10
#nref42.11
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#nref42.13
#r61
#r62
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#nref44.1
#c_102.22
#r64
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#pl18
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#nref47.1
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#note50.2
#note50.3
#nref50.1
#nref50.2
#nref50.3
#r65
#note52.1
#note52.2
#note52.3
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#note54.2
#note54.3
#note54.4
#note54.5
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#nref54.2
#nref54.3
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#nref54.5
#note55.1
#note55.2
#nref55.1
#nref55.2
#note57.1
#note57.2
#note57.3
#note57.5
#note57.6
#note57.7
#nref57.1
#nref57.2
#nref57.3
#nref57.4
#nref57.5
#nref57.6
#nref57.7
#r66
#r67
#note58.1
#nref58.1
#nref58.2
#r68
#r69
#note61.1
#note61.2
#nref61.1
#nref61.2
#nref61.3
#note62.1
#nref62.1
#r70
#note63.1
#note63.2
#note63.3
#note63.4
#note63.5
#note63.6
#note63.7
#nref63.1
#nref63.2
#nref63.3
#nref63.4
#nref63.5
#nref63.6
#nref63.7
#r71
#r72
#note64.1
#note64.2
#note64.3
#note64.4
#note64.5
#note64.6
#note64.7
#note64.8
#note64.9
#note64.10
#note64.11
#note64.12
#note64.13
#note64.14
#note64.15
#note64.16
#note64.17
#note64.18
#note64.19
#note64.21
#note64.22
#note64.23
#note64.24
#note64.25
#nref64.1
#nref64.2
#nref64.3
#nref64.4
#nref64.5
#nref64.6
#nref64.7
#nref64.8
#nref64.9
#c_123.10
#nref64.10
#nref64.11
#nref64.12
#nref64.13
#nref64.14
#nref64.15
#nref64.16
#nref64.17
#nref64.18
#nref64.19
#nref64.20
#nref64.21
#nref64.22
#nref64.23
#nref64.24
#nref64.25
#r73
#r74
#r75
#r76
#r77
#r78
#r79
#r80
#c_127.26
#nref65.1
#note68.1
#note68.2
#note68.3
#note68.4
#nref68.1
#nref68.2
#nref68.3
#nref68.4
#note69.1
#note69.2
#note69.3
#note69.4
#note69.5
#note69.6
#r81
#note70.7
#note70.8
#nref69.1
#nref69.2
#nref69.3
#nref69.4
#nref69.5
#nref69.6
#nref70.7
#nref70.8
#r82
#note71.1
#note71.2
#note71.3
#note71.4
#note71.5
#note71.6
#note71.7
#note71.8
#note71.9
#nref71.1
#nref71.2
#nref71.3
#nref71.4
#nref71.5
#nref71.6
#nref71.7
#nref71.8
#c_136.7
#nref71.9
#note57.5
#r83
#r84
#r85
#pl19
#pl20
#note72.1
#note72.2
#note72.3
#note72.4
#note72.5
#c_136.22
#nref72.1
#nref72.2
#nref72.3
#nref72.4
#c_138.8
#nref72.5
#pl21
#pl22
#note74.1
#nref74.1
#r86
#r87
#note75.1
#note75.2
#note75.3
#nref75.1
#nref75.2
#nref75.3
#note76.1
#c_140.28
#nref76.1
#note77.1
#note77.2
#note77.3
#nref77.1
#nref77.2
#nref77.3
#note78.1
#note78.2
#note78.3
#note78.4
#note78.5
#c_143.25
#note78.6
#note78.7
#note78.8
#c_214.8
#note78.9
#note78.10
#note78.11
#nref78.1
#nref78.2
#nref78.3
#note72.3
#nref78.4
#nref78.5
#nref78.6
#nref78.7
#nref78.8
#nref78.9
#nref78.10
#nref78.11
#r88
#r89
#note80.1
#note80.2
#nref80.1
#nref80.2
#c_151.1
#note82.1
#note82.2
#nref82.1
#nref82.2
#r90
#note83.1
#c_151.29
#note83.2
#note83.3
#nref83.1
#nref83.2
#nref83.3
#note84.1
#note84.2
#note84.3
#note84.4
#nref84.1
#nref84.2
#nref84.3
#nref84.4
#note85.1
#note85.2
#c_154.15
#note85.3
#nref85.1
#nref85.2
#nref85.3
#note86.1
#note86.2
#nref86.1
#nref86.2
#note64.21
#c_157.9
#note88.1
#note88.2
#note88.3
#nref88.1
#nref88.2
#nref88.3
#note89.1
#note89.2
#nref89.1
#nref89.2
#note90.1
#note90.2
#note90.3
#nref90.1
#nref90.2
#nref90.3
#note91.1
#nref91.1
#pl23
#pl24
#note92.1
#note92.2
#note92.3
#nref92.1
#nref92.2
#nref92.3
#r91
#note93.1
#note93.3
#nref93.1
#nref93.2
#nref93.3
#note94.1
#note94.2
#note94.3
#nref94.1
#nref94.2
#nref94.3
#note95.1
#note95.3
#note95.4
#note95.5
#nref95.1
#nref95.2
#nref95.3
#nref95.4
#nref95.5
#pl25
#pl26
#note98.1
#note98.2
#note98.3
#note98.4
#note98.5
#note98.6
#nref98.1
#nref98.2
#nref98.3
#nref98.4
#nref98.5
#nref98.6
#c_167.3
#r92
#note99.1
#note99.3
#note99.4
#note99.5
#c_168.7
#note99.6
#note99.7
#note99.9
#note99.11
#nref99.1
#nref99.2
#nref99.3
#nref99.4
#nref99.5
#nref99.6
#nref99.7
#note15.9
#nref99.8
#nref99.9
#note71.1
#nref99.10
#nref99.11
#note100.1
#note100.2
#nref100.1
#nref100.2
#note101.1
#note101.2
#nref101.1
#nref101.2
#note102.1
#nref102.1
#note103.1
#nref103.1
#note104.1
#nref104.1
#note76.1
#note105.1
#note105.2
#note105.3
#note105.4
#nref105.1
#nref105.2
#nref105.3
#nref105.4
#note106.1
#note106.2
#nref106.1
#nref106.2
#pl27
#pl28
#note108.1
#note108.2
#note108.4
#note108.5
#note108.6
#note108.7
#note108.8
#nref108.1
#nref108.2
#c_180.2
#nref108.3
#nref108.4
#nref108.5
#nref108.6
#nref108.7
#nref108.8
#r93
#note109.1
#note109.2
#note109.3
#note109.4
#note109.5
#nref109.1
#nref109.2
#nref109.3
#nref109.4
#nref109.5
#r94
#pl29
#pl30
#note112.1
#note112.2
#note112.3
#note112.4
#note112.5
#note112.6
#note112.8
#nref112.1
#note18.6
#nref112.2
#nref112.3
#nref112.4
#nref112.5
#nref112.6
#nref112.7
#nref112.8
#r95
#note113.1
#note113.2
#note113.3
#note113.4
#nref113.1
#nref113.2
#nref113.3
#nref113.4
#pl31
#note114.1
#note114.3
#note114.5
#nref114.1
#nref114.2
#nref114.3
#nref114.4
#nref114.5
#r96
#r97
#note115.1
#note115.2
#note115.3
#note115.4
#note115.5
#nref115.1
#nref115.2
#nref115.3
#note125.33
#nref115.4
#nref115.5
#note110.1
#note110.2
#note110.3
#note110.4
#note110.5
#note110.6
#note110.7
#note110.8
#note110.9
#note110.10
#note110.11
#note110.12
#note110.13
#note110.14
#note110.15
#note110.16
#note110.17
#note110.18
#note110.19
#note40.1
#nref110.1
#nref110.2
#nref110.3
#nref110.4
#nref110.5
#nref110.6
#nref110.7
#nref110.8
#nref110.9
#nref110.10
#nref110.11
#nref110.12
#nref110.13
#nref110.14
#nref110.15
#note17.20
#nref110.16
#nref110.17
#note62.1
#nref110.18
#nref110.19
#r98
#r99
#r100
#r101
#r102
#r103
#r104
#c_198.32
#r105
#r106
#r107
#note117.1
#note117.2
#note117.3
#note117.4
#note117.5
#note117.6
#note117.7
#nref117.1
#nref117.2
#nref117.3
#nref117.4
#c_205.11
#nref117.5
#nref117.6
#nref117.7
#note118.1
#nref118.1
#note119.1
#nref119.1
#c_295.6
#nref119.2
#r108
#note123.1
#note123.2
#nref123.1
#note110.5
#nref123.2
#pl32
#pl33
#note124.1
#note124.2
#note124.3
#note124.4
#note124.5
#nref124.1
#nref124.2
#nref124.3
#nref124.4
#nref124.5
#r109
#r110
#pl34
#pl35
#pl36
#note125.1
#note125.2
#note125.3
#note125.4
#note125.5
#note125.6
#note125.7
#note125.8
#note125.9
#note125.10
#note125.11
#note125.12
#note125.13
#note125.14
#note125.15
#note125.16
#note125.17
#note125.18
#note125.19
#note125.20
#note125.21
#note125.22
#note125.23
#note125.24
#note125.25
#note125.26
#note125.27
#note125.28
#note125.29
#note125.30
#pl37
#note125.31
#note125.32
#note125.33
#note125.34
#note125.35
#note125.36
#note125.37
#note125.38
#note125.39
#note125.40
#note125.41
#note125.43
#note125.44
#note125.45
#note125.46
#c_208.10
#note125.47
#note125.48
#c_218.28
#note125.49
#note125.50
#note125.51
#note125.52
#note125.53
#note125.54
#pl38
#pl39
#c_222.19
#c_224.2
#nref125.1
#nref125.2
#pl33
#pl31
#pl34
#pl35
#nref125.3
#c_230.7
#nref125.4
#nref125.5
#nref125.6
#nref125.7
#nref125.8
#pl36
#nref125.9
#nref125.10
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#nref125.16
#nref125.17
#pl23
#nref125.18
#nref125.19
#note40.10
#nref125.20
#nref125.21
#nref125.22
#nref125.23
#nref125.24
#nref125.25
#nref125.26
#nref125.27
#nref125.28
#nref125.29
#nref125.30
#nref125.31
#nref125.32
#nref125.33
#note110.3
#nref125.34
#nref125.35
#note63.6
#nref125.36
#nref125.37
#nref125.38
#note26.3
#nref125.39
#nref125.40
#nref125.41
#nref125.42
#nref125.43
#nref125.44
#nref125.45
#nref125.46
#nref125.47
#nref125.48
#nref125.49
#nref125.50
#nref125.51
#nref125.52
#nref125.53
#nref125.54
#note125.2
#r111
#pl32
#r112
#r113
#r114
#r115
#r116
#c_227.35
#r117
#r118
#r119
#r120
#c_230.34
#r121
#r122
#r123
#r124
#r125
#r126
#r127
#r128
#r129
#r130
#r131
#r132
#r133
#r134
#pl40
#pl41
#note126.1
#note126.2
#note126.3
#note126.4
#note126.5
#note126.6
#note126.7
#nref126.1
#note5.5
#nref126.2
#nref126.3
#nref126.4
#note40.6
#nref126.5
#c_247.30.1
#nref126.6
#nref126.7
#r135
#r136
#r137
#c_246.31
#r138
#note127.1
#note127.3
#nref127.1
#c_249.32
#nref127.2
#nref127.3
#note126.4
#r139
#note128.1
#note128.2
#note128.3
#note128.4
#note128.5
#note128.6
#note128.7
#note128.8
#note128.9
#note128.10
#note128.11
#note128.12
#nref128.1
#nref128.2
#nref128.3
#note18.3
#nref128.4
#note1.2
#nref128.5
#nref128.6
#note18.2
#nref128.7
#nref128.8
#nref128.9
#note22.2
#nref128.10
#note78.4
#nref128.11
#nref128.12
#r140
#r141
#r142
#c_255.25
#r143
#note130.1
#note130.2
#note130.3
#note130.4
#note130.5
#note130.6
#note130.7
#note130.8
#note130.9
#note130.10
#note130.11
#nref130.1
#c_259.20
#note130.11
#nref130.2
#nref130.3
#nref130.4
#nref130.5
#nref130.6
#nref130.7
#note130.5
#nref130.8
#nref130.9
#nref130.10
#nref130.11
#c_260.28
#note131.1
#note131.2
#note131.3
#note131.4
#note131.5
#note131.6
#nref131.1
#nref131.2
#nref131.3
#nref131.4
#nref131.5
#note64.13
#nref131.6
#note132.1
#note132.2
#note132.3
#nref132.1
#nref132.2
#nref132.3
#note133.1
#note133.2
#note133.3
#note133.4
#note133.5
#note133.6
#note133.7
#note133.8
#nref133.1
#nref133.2
#nref133.3
#nref133.4
#nref133.5
#nref133.6
#c_267.13
#nref133.7
#nref133.8
#r144
#note134.1
#note134.2
#note134.3
#note134.4
#nref134.1
#nref134.2
#nref134.3
#nref134.4
#note135.1
#note135.2
#nref135.1
#nref135.2
#note136.1
#note136.3
#note136.5
#note136.6
#note136.7
#note136.8
#note136.9
#note136.11
#nref136.1
#nref136.2
#c_272.24
#c_273.8
#nref136.3
#nref136.4
#note74.1
#nref136.5
#nref136.6
#nref136.7
#nref136.8
#nref136.9
#nref136.10
#nref136.11
#r145
#r146
#r147
#pl42
#pl43
#note137.1
#note137.2
#note137.3
#note137.4
#note137.5
#nref137.1
#nref137.2
#nref137.3
#nref137.4
#c_276.25
#nref137.5
#nref137.6
#note138.1
#note138.2
#note138.3
#note138.4
#note138.5
#c_277.12
#note138.6
#note138.7
#note138.8
#nref138.1
#c_277.21
#nref138.2
#nref138.3
#nref138.4
#nref138.5
#nref138.6
#nref138.7
#nref138.8
#c_278.19
#note140.1
#note140.2
#Page_214
#nref140.1
#nref140.2
#pl44
#pl45
#pl46
#pl47
#note141.1
#note141.2
#note141.3
#note141.4
#note141.5
#note141.6
#note141.7
#c_286.8
#nref141.1
#nref141.2
#note133.1
#nref141.3
#nref141.4
#note130.5
#nref141.5
#nref141.6
#nref141.7
#note144.1
#note144.2
#note144.3
#note144.4
#note144.5
#note144.6
#note144.8
#note144.9
#note144.10
#c_290.1
#nref144.1
#nref144.2
#nref144.3
#nref144.4
#nref144.5
#nref144.6
#nref144.7
#nref144.8
#c_292.1
#nref144.9
#nref144.10
#pl48
#pl49
#note145.1
#note145.2
#c_292.27
#note145.3
#note145.4
#note145.5
#note145.6
#nref145.1
#nref145.2
#nref145.3
#nref145.4
#nref145.5
#nref145.6
#pl50
#pl51
#note147.1
#note147.2
#note147.3
#note147.4
#note147.5
#note147.6
#note147.7
#note147.8
#nref147.1
#nref147.2
#nref147.3
#nref147.4
#nref147.5
#nref147.6
#nref147.7
#nref147.8
#note148.1
#note148.3
#note148.4
#nref148.1
#nref148.2
#nref148.3
#nref148.4
#pl52
#pl53
#note149.1
#note149.2
#note149.3
#note149.4
#note149.5
#note149.6
#note149.7
#note149.8
#note149.9
#note149.10
#note149.11
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#note149.13
#note149.14
#note149.15
#note149.16
#nref149.1
#nref149.2
#nref149.3
#nref149.4
#nref149.5
#nref149.6
#nref149.7
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#r49
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#note35.1
#note35.2
#note35.3
#note35.4
#note35.5
#note35.6
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#c_83.27
#nref35.3
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#nref37.1
#note38.1
#note38.2
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#note39.2
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#note42.2
#note42.3
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#note42.5
#note42.7
#note42.8
#c_92.60
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#note42.9
#note42.10
#note42.11
#note42.12
#note42.13
#nref42.1
#nref42.2
#nref42.3
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#nref42.5
#nref42.6
#nref42.7
#nref42.8
#nref42.9
#nref42.10
#nref42.11
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#r65
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#note52.2
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#note54.2
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#nref54.2
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#nref54.5
#note55.1
#note55.2
#nref55.1
#nref55.2
#note57.1
#note57.2
#note57.3
#note57.5
#note57.6
#note57.7
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#nref57.2
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#nref57.6
#nref57.7
#r66
#r67
#note58.1
#nref58.1
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#r69
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#nref61.1
#nref61.2
#nref61.3
#note62.1
#nref62.1
#r70
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#note63.2
#note63.3
#note63.4
#note63.5
#note63.6
#note63.7
#nref63.1
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#nref63.7
#r71
#r72
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#note64.2
#note64.3
#note64.4
#note64.5
#note64.6
#note64.7
#note64.8
#note64.9
#note64.10
#note64.11
#note64.12
#note64.13
#note64.14
#note64.15
#note64.16
#note64.17
#note64.18
#note64.19
#note64.21
#note64.22
#note64.23
#note64.24
#note64.25
#nref64.1
#nref64.2
#nref64.3
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#nref64.5
#nref64.6
#nref64.7
#nref64.8
#nref64.9
#c_123.10
#nref64.10
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#nref64.12
#nref64.13
#nref64.14
#nref64.15
#nref64.16
#nref64.17
#nref64.18
#nref64.19
#nref64.20
#nref64.21
#nref64.22
#nref64.23
#nref64.24
#nref64.25
#r73
#r74
#r75
#r76
#r77
#r78
#r79
#r80
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#note68.1
#note68.2
#note68.3
#note68.4
#nref68.1
#nref68.2
#nref68.3
#nref68.4
#note69.1
#note69.2
#note69.3
#note69.4
#note69.5
#note69.6
#r81
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#note70.8
#nref69.1
#nref69.2
#nref69.3
#nref69.4
#nref69.5
#nref69.6
#nref70.7
#nref70.8
#r82
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#note71.2
#note71.3
#note71.4
#note71.5
#note71.6
#note71.7
#note71.8
#note71.9
#nref71.1
#nref71.2
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#nref71.4
#nref71.5
#nref71.6
#nref71.7
#nref71.8
#c_136.7
#nref71.9
#note57.5
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#note72.2
#note72.3
#note72.4
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#nref72.5
#pl21
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#nref74.1
#r86
#r87
#note75.1
#note75.2
#note75.3
#nref75.1
#nref75.2
#nref75.3
#note76.1
#c_140.28
#nref76.1
#note77.1
#note77.2
#note77.3
#nref77.1
#nref77.2
#nref77.3
#note78.1
#note78.2
#note78.3
#note78.4
#note78.5
#c_143.25
#note78.6
#note78.7
#note78.8
#c_214.8
#note78.9
#note78.10
#note78.11
#nref78.1
#nref78.2
#nref78.3
#note72.3
#nref78.4
#nref78.5
#nref78.6
#nref78.7
#nref78.8
#nref78.9
#nref78.10
#nref78.11
#r88
#r89
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#nref80.1
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#note82.1
#note82.2
#nref82.1
#nref82.2
#r90
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#note83.2
#note83.3
#nref83.1
#nref83.2
#nref83.3
#note84.1
#note84.2
#note84.3
#note84.4
#nref84.1
#nref84.2
#nref84.3
#nref84.4
#note85.1
#note85.2
#c_154.15
#note85.3
#nref85.1
#nref85.2
#nref85.3
#note86.1
#note86.2
#nref86.1
#nref86.2
#note64.21
#c_157.9
#note88.1
#note88.2
#note88.3
#nref88.1
#nref88.2
#nref88.3
#note89.1
#note89.2
#nref89.1
#nref89.2
#note90.1
#note90.2
#note90.3
#nref90.1
#nref90.2
#nref90.3
#note91.1
#nref91.1
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#pl24
#note92.1
#note92.2
#note92.3
#nref92.1
#nref92.2
#nref92.3
#r91
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#note93.3
#nref93.1
#nref93.2
#nref93.3
#note94.1
#note94.2
#note94.3
#nref94.1
#nref94.2
#nref94.3
#note95.1
#note95.3
#note95.4
#note95.5
#nref95.1
#nref95.2
#nref95.3
#nref95.4
#nref95.5
#pl25
#pl26
#note98.1
#note98.2
#note98.3
#note98.4
#note98.5
#note98.6
#nref98.1
#nref98.2
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#nref98.4
#nref98.5
#nref98.6
#c_167.3
#r92
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#note99.4
#note99.5
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#note99.6
#note99.7
#note99.9
#note99.11
#nref99.1
#nref99.2
#nref99.3
#nref99.4
#nref99.5
#nref99.6
#nref99.7
#note15.9
#nref99.8
#nref99.9
#note71.1
#nref99.10
#nref99.11
#note100.1
#note100.2
#nref100.1
#nref100.2
#note101.1
#note101.2
#nref101.1
#nref101.2
#note102.1
#nref102.1
#note103.1
#nref103.1
#note104.1
#nref104.1
#note76.1
#note105.1
#note105.2
#note105.3
#note105.4
#nref105.1
#nref105.2
#nref105.3
#nref105.4
#note106.1
#note106.2
#nref106.1
#nref106.2
#pl27
#pl28
#note108.1
#note108.2
#note108.4
#note108.5
#note108.6
#note108.7
#note108.8
#nref108.1
#nref108.2
#c_180.2
#nref108.3
#nref108.4
#nref108.5
#nref108.6
#nref108.7
#nref108.8
#r93
#note109.1
#note109.2
#note109.3
#note109.4
#note109.5
#nref109.1
#nref109.2
#nref109.3
#nref109.4
#nref109.5
#r94
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#pl30
#note112.1
#note112.2
#note112.3
#note112.4
#note112.5
#note112.6
#note112.8
#nref112.1
#note18.6
#nref112.2
#nref112.3
#nref112.4
#nref112.5
#nref112.6
#nref112.7
#nref112.8
#r95
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#note113.2
#note113.3
#note113.4
#nref113.1
#nref113.2
#nref113.3
#nref113.4
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#note114.1
#note114.3
#note114.5
#nref114.1
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#nref114.5
#r96
#r97
#note115.1
#note115.2
#note115.3
#note115.4
#note115.5
#nref115.1
#nref115.2
#nref115.3
#note125.33
#nref115.4
#nref115.5
#note110.1
#note110.2
#note110.3
#note110.4
#note110.5
#note110.6
#note110.7
#note110.8
#note110.9
#note110.10
#note110.11
#note110.12
#note110.13
#note110.14
#note110.15
#note110.16
#note110.17
#note110.18
#note110.19
#note40.1
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#nref110.7
#nref110.8
#nref110.9
#nref110.10
#nref110.11
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#nref110.13
#nref110.14
#nref110.15
#note17.20
#nref110.16
#nref110.17
#note62.1
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#note117.2
#note117.3
#note117.4
#note117.5
#note117.6
#note117.7
#nref117.1
#nref117.2
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#nref117.5
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#note119.1
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#note125.7
#note125.8
#note125.9
#note125.10
#note125.11
#note125.12
#note125.13
#note125.14
#note125.15
#note125.16
#note125.17
#note125.18
#note125.19
#note125.20
#note125.21
#note125.22
#note125.23
#note125.24
#note125.25
#note125.26
#note125.27
#note125.28
#note125.29
#note125.30
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#note125.32
#note125.33
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#note125.37
#note125.38
#note125.39
#note125.40
#note125.41
#note125.43
#note125.44
#note125.45
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#note125.47
#note125.48
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#note125.50
#note125.51
#note125.52
#note125.53
#note125.54
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#nref125.28
#nref125.29
#nref125.30
#nref125.31
#nref125.32
#nref125.33
#note110.3
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#nref125.35
#note63.6
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#nref125.38
#note26.3
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#nref125.40
#nref125.41
#nref125.42
#nref125.43
#nref125.44
#nref125.45
#nref125.46
#nref125.47
#nref125.48
#nref125.49
#nref125.50
#nref125.51
#nref125.52
#nref125.53
#nref125.54
#note125.2
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#note126.7
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#note5.5
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#note40.6
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#nref126.6
#nref126.7
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#note126.4
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#note128.2
#note128.3
#note128.4
#note128.5
#note128.6
#note128.7
#note128.8
#note128.9
#note128.10
#note128.11
#note128.12
#nref128.1
#nref128.2
#nref128.3
#note18.3
#nref128.4
#note1.2
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#note18.2
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#nref128.8
#nref128.9
#note22.2
#nref128.10
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#note130.1
#note130.2
#note130.3
#note130.4
#note130.5
#note130.6
#note130.7
#note130.8
#note130.9
#note130.10
#note130.11
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#nref130.6
#nref130.7
#note130.5
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#nref130.9
#nref130.10
#nref130.11
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#note131.1
#note131.2
#note131.3
#note131.4
#note131.5
#note131.6
#nref131.1
#nref131.2
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#nref131.5
#note64.13
#nref131.6
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#note132.2
#note132.3
#nref132.1
#nref132.2
#nref132.3
#note133.1
#note133.2
#note133.3
#note133.4
#note133.5
#note133.6
#note133.7
#note133.8
#nref133.1
#nref133.2
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#nref133.5
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#nref133.7
#nref133.8
#r144
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#note134.3
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#nref134.2
#nref134.3
#nref134.4
#note135.1
#note135.2
#nref135.1
#nref135.2
#note136.1
#note136.3
#note136.5
#note136.6
#note136.7
#note136.8
#note136.9
#note136.11
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#nref136.8
#nref136.9
#nref136.10
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#r145
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#nref137.4
#c_276.25
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#note138.1
#note138.2
#note138.3
#note138.4
#note138.5
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#note138.6
#note138.7
#note138.8
#nref138.1
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#nref138.2
#nref138.3
#nref138.4
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#nref138.6
#nref138.7
#nref138.8
#c_278.19
#note140.1
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#nref140.1
#nref140.2
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#pl47
#note141.1
#note141.2
#note141.3
#note141.4
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#note141.7
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#note130.5
#nref141.5
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#note144.1
#note144.2
#note144.3
#note144.4
#note144.5
#note144.6
#note144.8
#note144.9
#note144.10
#c_290.1
#nref144.1
#nref144.2
#nref144.3
#nref144.4
#nref144.5
#nref144.6
#nref144.7
#nref144.8
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#nref144.9
#nref144.10
#pl48
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#note145.1
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#nref145.1
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#nref145.3
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#nref145.5
#nref145.6
#pl50
#pl51
#note147.1
#note147.2
#note147.3
#note147.4
#note147.5
#note147.6
#note147.7
#note147.8
#nref147.1
#nref147.2
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#r46
#note30.1
#note30.2
#note30.3
#note30.4
#c_74.27
#note30.5
#note30.6
#note30.7
#note30.8
#note30.9
#note30.10
#nref30.1
#nref30.2
#nref30.3
#nref30.4
#nref30.5
#nref30.6
#nref30.7
#nref30.8
#nref30.9
#nref30.10
#r47
#r48
#note31.1
#note31.2
#note31.3
#note31.4
#note31.5
#note31.6
#note31.7
#nref31.1
#nref31.2
#nref31.3
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#nref31.5
#nref31.6
#nref31.7
#r49
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#note32.2
#note32.3
#note32.4
#note32.5
#note32.6
#note32.7
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#nref32.3
#note15.3
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#note130.7
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#nref32.7
#r50
#r51
#note34.1
#note34.2
#note34.4
#nref34.1
#nref34.2
#nref34.3
#nref34.4
#note35.1
#note35.2
#note35.3
#note35.4
#note35.5
#note35.6
#nref35.1
#nref35.2
#c_83.27
#nref35.3
#nref35.4
#nref35.5
#nref35.6
#pl12
#pl13
#note37.1
#nref37.1
#note38.1
#note38.2
#note38.3
#note38.4
#nref38.1
#nref38.2
#nref38.3
#nref38.4
#note125.15
#r52
#r53
#r54
#note39.1
#note39.2
#note39.3
#note39.4
#nref39.1
#note40.5
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#r56
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#note40.2
#note40.3
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#note40.5
#note40.6
#note40.7
#note40.9
#note40.10
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#nref40.2
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#nref40.5
#nref40.6
#nref40.7
#nref40.8
#nref40.9
#nref40.10
#r57
#r58
#r59
#r60
#note41.1
#note41.2
#note41.3
#note41.4
#note41.6
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#nref41.3
#nref41.4
#nref41.5
#nref41.6
#pl15
#pl16
#note42.1
#note42.2
#note42.3
#note42.4
#note42.5
#note42.7
#note42.8
#c_92.60
#c_97.11
#note42.9
#note42.10
#note42.11
#note42.12
#note42.13
#nref42.1
#nref42.2
#nref42.3
#nref42.4
#nref42.5
#nref42.6
#nref42.7
#nref42.8
#nref42.9
#nref42.10
#nref42.11
#nref42.12
#nref42.13
#r61
#r62
#r63
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#pl18
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#nref47.1
#note50.1
#note50.2
#note50.3
#nref50.1
#nref50.2
#nref50.3
#r65
#note52.1
#note52.2
#note52.3
#nref52.1
#nref52.2
#nref52.3
#note54.1
#note54.2
#note54.3
#note54.4
#note54.5
#nref54.1
#nref54.2
#nref54.3
#nref54.4
#nref54.5
#note55.1
#note55.2
#nref55.1
#nref55.2
#note57.1
#note57.2
#note57.3
#note57.5
#note57.6
#note57.7
#nref57.1
#nref57.2
#nref57.3
#nref57.4
#nref57.5
#nref57.6
#nref57.7
#r66
#r67
#note58.1
#nref58.1
#nref58.2
#r68
#r69
#note61.1
#note61.2
#nref61.1
#nref61.2
#nref61.3
#note62.1
#nref62.1
#r70
#note63.1
#note63.2
#note63.3
#note63.4
#note63.5
#note63.6
#note63.7
#nref63.1
#nref63.2
#nref63.3
#nref63.4
#nref63.5
#nref63.6
#nref63.7
#r71
#r72
#note64.1
#note64.2
#note64.3
#note64.4
#note64.5
#note64.6
#note64.7
#note64.8
#note64.9
#note64.10
#note64.11
#note64.12
#note64.13
#note64.14
#note64.15
#note64.16
#note64.17
#note64.18
#note64.19
#note64.21
#note64.22
#note64.23
#note64.24
#note64.25
#nref64.1
#nref64.2
#nref64.3
#nref64.4
#nref64.5
#nref64.6
#nref64.7
#nref64.8
#nref64.9
#c_123.10
#nref64.10
#nref64.11
#nref64.12
#nref64.13
#nref64.14
#nref64.15
#nref64.16
#nref64.17
#nref64.18
#nref64.19
#nref64.20
#nref64.21
#nref64.22
#nref64.23
#nref64.24
#nref64.25
#r73
#r74
#r75
#r76
#r77
#r78
#r79
#r80
#c_127.26
#nref65.1
#note68.1
#note68.2
#note68.3
#note68.4
#nref68.1
#nref68.2
#nref68.3
#nref68.4
#note69.1
#note69.2
#note69.3
#note69.4
#note69.5
#note69.6
#r81
#note70.7
#note70.8
#nref69.1
#nref69.2
#nref69.3
#nref69.4
#nref69.5
#nref69.6
#nref70.7
#nref70.8
#r82
#note71.1
#note71.2
#note71.3
#note71.4
#note71.5
#note71.6
#note71.7
#note71.8
#note71.9
#nref71.1
#nref71.2
#nref71.3
#nref71.4
#nref71.5
#nref71.6
#nref71.7
#nref71.8
#c_136.7
#nref71.9
#note57.5
#r83
#r84
#r85
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#pl20
#note72.1
#note72.2
#note72.3
#note72.4
#note72.5
#c_136.22
#nref72.1
#nref72.2
#nref72.3
#nref72.4
#c_138.8
#nref72.5
#pl21
#pl22
#note74.1
#nref74.1
#r86
#r87
#note75.1
#note75.2
#note75.3
#nref75.1
#nref75.2
#nref75.3
#note76.1
#c_140.28
#nref76.1
#note77.1
#note77.2
#note77.3
#nref77.1
#nref77.2
#nref77.3
#note78.1
#note78.2
#note78.3
#note78.4
#note78.5
#c_143.25
#note78.6
#note78.7
#note78.8
#c_214.8
#note78.9
#note78.10
#note78.11
#nref78.1
#nref78.2
#nref78.3
#note72.3
#nref78.4
#nref78.5
#nref78.6
#nref78.7
#nref78.8
#nref78.9
#nref78.10
#nref78.11
#r88
#r89
#note80.1
#note80.2
#nref80.1
#nref80.2
#c_151.1
#note82.1
#note82.2
#nref82.1
#nref82.2
#r90
#note83.1
#c_151.29
#note83.2
#note83.3
#nref83.1
#nref83.2
#nref83.3
#note84.1
#note84.2
#note84.3
#note84.4
#nref84.1
#nref84.2
#nref84.3
#nref84.4
#note85.1
#note85.2
#c_154.15
#note85.3
#nref85.1
#nref85.2
#nref85.3
#note86.1
#note86.2
#nref86.1
#nref86.2
#note64.21
#c_157.9
#note88.1
#note88.2
#note88.3
#nref88.1
#nref88.2
#nref88.3
#note89.1
#note89.2
#nref89.1
#nref89.2
#note90.1
#note90.2
#note90.3
#nref90.1
#nref90.2
#nref90.3
#note91.1
#nref91.1
#pl23
#pl24
#note92.1
#note92.2
#note92.3
#nref92.1
#nref92.2
#nref92.3
#r91
#note93.1
#note93.3
#nref93.1
#nref93.2
#nref93.3
#note94.1
#note94.2
#note94.3
#nref94.1
#nref94.2
#nref94.3
#note95.1
#note95.3
#note95.4
#note95.5
#nref95.1
#nref95.2
#nref95.3
#nref95.4
#nref95.5
#pl25
#pl26
#note98.1
#note98.2
#note98.3
#note98.4
#note98.5
#note98.6
#nref98.1
#nref98.2
#nref98.3
#nref98.4
#nref98.5
#nref98.6
#c_167.3
#r92
#note99.1
#note99.3
#note99.4
#note99.5
#c_168.7
#note99.6
#note99.7
#note99.9
#note99.11
#nref99.1
#nref99.2
#nref99.3
#nref99.4
#nref99.5
#nref99.6
#nref99.7
#note15.9
#nref99.8
#nref99.9
#note71.1
#nref99.10
#nref99.11
#note100.1
#note100.2
#nref100.1
#nref100.2
#note101.1
#note101.2
#nref101.1
#nref101.2
#note102.1
#nref102.1
#note103.1
#nref103.1
#note104.1
#nref104.1
#note76.1
#note105.1
#note105.2
#note105.3
#note105.4
#nref105.1
#nref105.2
#nref105.3
#nref105.4
#note106.1
#note106.2
#nref106.1
#nref106.2
#pl27
#pl28
#note108.1
#note108.2
#note108.4
#note108.5
#note108.6
#note108.7
#note108.8
#nref108.1
#nref108.2
#c_180.2
#nref108.3
#nref108.4
#nref108.5
#nref108.6
#nref108.7
#nref108.8
#r93
#note109.1
#note109.2
#note109.3
#note109.4
#note109.5
#nref109.1
#nref109.2
#nref109.3
#nref109.4
#nref109.5
#r94
#pl29
#pl30
#note112.1
#note112.2
#note112.3
#note112.4
#note112.5
#note112.6
#note112.8
#nref112.1
#note18.6
#nref112.2
#nref112.3
#nref112.4
#nref112.5
#nref112.6
#nref112.7
#nref112.8
#r95
#note113.1
#note113.2
#note113.3
#note113.4
#nref113.1
#nref113.2
#nref113.3
#nref113.4
#pl31
#note114.1
#note114.3
#note114.5
#nref114.1
#nref114.2
#nref114.3
#nref114.4
#nref114.5
#r96
#r97
#note115.1
#note115.2
#note115.3
#note115.4
#note115.5
#nref115.1
#nref115.2
#nref115.3
#note125.33
#nref115.4
#nref115.5
#note110.1
#note110.2
#note110.3
#note110.4
#note110.5
#note110.6
#note110.7
#note110.8
#note110.9
#note110.10
#note110.11
#note110.12
#note110.13
#note110.14
#note110.15
#note110.16
#note110.17
#note110.18
#note110.19
#note40.1
#nref110.1
#nref110.2
#nref110.3
#nref110.4
#nref110.5
#nref110.6
#nref110.7
#nref110.8
#nref110.9
#nref110.10
#nref110.11
#nref110.12
#nref110.13
#nref110.14
#nref110.15
#note17.20
#nref110.16
#nref110.17
#note62.1
#nref110.18
#nref110.19
#r98
#r99
#r100
#r101
#r102
#r103
#r104
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#r105
#r106
#r107
#note117.1
#note117.2
#note117.3
#note117.4
#note117.5
#note117.6
#note117.7
#nref117.1
#nref117.2
#nref117.3
#nref117.4
#c_205.11
#nref117.5
#nref117.6
#nref117.7
#note118.1
#nref118.1
#note119.1
#nref119.1
#c_295.6
#nref119.2
#r108
#note123.1
#note123.2
#nref123.1
#note110.5
#nref123.2
#pl32
#pl33
#note124.1
#note124.2
#note124.3
#note124.4
#note124.5
#nref124.1
#nref124.2
#nref124.3
#nref124.4
#nref124.5
#r109
#r110
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#pl36
#note125.1
#note125.2
#note125.3
#note125.4
#note125.5
#note125.6
#note125.7
#note125.8
#note125.9
#note125.10
#note125.11
#note125.12
#note125.13
#note125.14
#note125.15
#note125.16
#note125.17
#note125.18
#note125.19
#note125.20
#note125.21
#note125.22
#note125.23
#note125.24
#note125.25
#note125.26
#note125.27
#note125.28
#note125.29
#note125.30
#pl37
#note125.31
#note125.32
#note125.33
#note125.34
#note125.35
#note125.36
#note125.37
#note125.38
#note125.39
#note125.40
#note125.41
#note125.43
#note125.44
#note125.45
#note125.46
#c_208.10
#note125.47
#note125.48
#c_218.28
#note125.49
#note125.50
#note125.51
#note125.52
#note125.53
#note125.54
#pl38
#pl39
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#nref125.2
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#nref125.4
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#nref125.7
#nref125.8
#pl36
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#nref125.13
#nref125.14
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#nref125.17
#pl23
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#note40.10
#nref125.20
#nref125.21
#nref125.22
#nref125.23
#nref125.24
#nref125.25
#nref125.26
#nref125.27
#nref125.28
#nref125.29
#nref125.30
#nref125.31
#nref125.32
#nref125.33
#note110.3
#nref125.34
#nref125.35
#note63.6
#nref125.36
#nref125.37
#nref125.38
#note26.3
#nref125.39
#nref125.40
#nref125.41
#nref125.42
#nref125.43
#nref125.44
#nref125.45
#nref125.46
#nref125.47
#nref125.48
#nref125.49
#nref125.50
#nref125.51
#nref125.52
#nref125.53
#nref125.54
#note125.2
#r111
#pl32
#r112
#r113
#r114
#r115
#r116
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#r117
#r118
#r119
#r120
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#r121
#r122
#r123
#r124
#r125
#r126
#r127
#r128
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#r130
#r131
#r132
#r133
#r134
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#pl41
#note126.1
#note126.2
#note126.3
#note126.4
#note126.5
#note126.6
#note126.7
#nref126.1
#note5.5
#nref126.2
#nref126.3
#nref126.4
#note40.6
#nref126.5
#c_247.30.1
#nref126.6
#nref126.7
#r135
#r136
#r137
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#r138
#note127.1
#note127.3
#nref127.1
#c_249.32
#nref127.2
#nref127.3
#note126.4
#r139
#note128.1
#note128.2
#note128.3
#note128.4
#note128.5
#note128.6
#note128.7
#note128.8
#note128.9
#note128.10
#note128.11
#note128.12
#nref128.1
#nref128.2
#nref128.3
#note18.3
#nref128.4
#note1.2
#nref128.5
#nref128.6
#note18.2
#nref128.7
#nref128.8
#nref128.9
#note22.2
#nref128.10
#note78.4
#nref128.11
#nref128.12
#r140
#r141
#r142
#c_255.25
#r143
#note130.1
#note130.2
#note130.3
#note130.4
#note130.5
#note130.6
#note130.7
#note130.8
#note130.9
#note130.10
#note130.11
#nref130.1
#c_259.20
#note130.11
#nref130.2
#nref130.3
#nref130.4
#nref130.5
#nref130.6
#nref130.7
#note130.5
#nref130.8
#nref130.9
#nref130.10
#nref130.11
#c_260.28
#note131.1
#note131.2
#note131.3
#note131.4
#note131.5
#note131.6
#nref131.1
#nref131.2
#nref131.3
#nref131.4
#nref131.5
#note64.13
#nref131.6
#note132.1
#note132.2
#note132.3
#nref132.1
#nref132.2
#nref132.3
#note133.1
#note133.2
#note133.3
#note133.4
#note133.5
#note133.6
#note133.7
#note133.8
#nref133.1
#nref133.2
#nref133.3
#nref133.4
#nref133.5
#nref133.6
#c_267.13
#nref133.7
#nref133.8
#r144
#note134.1
#note134.2
#note134.3
#note134.4
#nref134.1
#nref134.2
#nref134.3
#nref134.4
#note135.1
#note135.2
#nref135.1
#nref135.2
#note136.1
#note136.3
#note136.5
#note136.6
#note136.7
#note136.8
#note136.9
#note136.11
#nref136.1
#nref136.2
#c_272.24
#c_273.8
#nref136.3
#nref136.4
#note74.1
#nref136.5
#nref136.6
#nref136.7
#nref136.8
#nref136.9
#nref136.10
#nref136.11
#r145
#r146
#r147
#pl42
#pl43
#note137.1
#note137.2
#note137.3
#note137.4
#note137.5
#nref137.1
#nref137.2
#nref137.3
#nref137.4
#c_276.25
#nref137.5
#nref137.6
#note138.1
#note138.2
#note138.3
#note138.4
#note138.5
#c_277.12
#note138.6
#note138.7
#note138.8
#nref138.1
#c_277.21
#nref138.2
#nref138.3
#nref138.4
#nref138.5
#nref138.6
#nref138.7
#nref138.8
#c_278.19
#note140.1
#note140.2
#Page_214
#nref140.1
#nref140.2
#pl44
#pl45
#pl46
#pl47
#note141.1
#note141.2
#note141.3
#note141.4
#note141.5
#note141.6
#note141.7
#c_286.8
#nref141.1
#nref141.2
#note133.1
#nref141.3
#nref141.4
#note130.5
#nref141.5
#nref141.6
#nref141.7
#note144.1
#note144.2
#note144.3
#note144.4
#note144.5
#note144.6
#note144.8
#note144.9
#note144.10
#c_290.1
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#note26.3
#note26.4
#note26.5
#nref26.1
#pl10
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#nref26.3
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#nref26.5
#r41
#r42
#r43
#r44
#note27.1
#note27.2
#note27.3
#nref27.1
#nref27.2
#nref27.3
#note28.1
#note28.2
#note28.3
#note28.4
#note28.5
#note28.6
#nref28.1
#nref28.2
#nref28.3
#nref28.4
#nref28.5
#nref28.6
#note29.1
#note29.2
#note29.3
#pl30
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#nref29.2
#nref29.3
#r45
#r46
#note30.1
#note30.2
#note30.3
#note30.4
#c_74.27
#note30.5
#note30.6
#note30.7
#note30.8
#note30.9
#note30.10
#nref30.1
#nref30.2
#nref30.3
#nref30.4
#nref30.5
#nref30.6
#nref30.7
#nref30.8
#nref30.9
#nref30.10
#r47
#r48
#note31.1
#note31.2
#note31.3
#note31.4
#note31.5
#note31.6
#note31.7
#nref31.1
#nref31.2
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#nref31.5
#nref31.6
#nref31.7
#r49
#note32.1
#note32.2
#note32.3
#note32.4
#note32.5
#note32.6
#note32.7
#nref32.1
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#nref32.3
#note15.3
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#note130.7
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#note34.1
#note34.2
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#nref34.2
#nref34.3
#nref34.4
#note35.1
#note35.2
#note35.3
#note35.4
#note35.5
#note35.6
#nref35.1
#nref35.2
#c_83.27
#nref35.3
#nref35.4
#nref35.5
#nref35.6
#pl12
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#note37.1
#nref37.1
#note38.1
#note38.2
#note38.3
#note38.4
#nref38.1
#nref38.2
#nref38.3
#nref38.4
#note125.15
#r52
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#note39.1
#note39.2
#note39.3
#note39.4
#nref39.1
#note40.5
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#note40.2
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#note40.9
#note40.10
#nref40.1
#nref40.2
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#nref40.5
#nref40.6
#nref40.7
#nref40.8
#nref40.9
#nref40.10
#r57
#r58
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#note41.1
#note41.2
#note41.3
#note41.4
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#nref41.3
#nref41.4
#nref41.5
#nref41.6
#pl15
#pl16
#note42.1
#note42.2
#note42.3
#note42.4
#note42.5
#note42.7
#note42.8
#c_92.60
#c_97.11
#note42.9
#note42.10
#note42.11
#note42.12
#note42.13
#nref42.1
#nref42.2
#nref42.3
#nref42.4
#nref42.5
#nref42.6
#nref42.7
#nref42.8
#nref42.9
#nref42.10
#nref42.11
#nref42.12
#nref42.13
#r61
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#note50.1
#note50.2
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#r65
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#note52.2
#note52.3
#nref52.1
#nref52.2
#nref52.3
#note54.1
#note54.2
#note54.3
#note54.4
#note54.5
#nref54.1
#nref54.2
#nref54.3
#nref54.4
#nref54.5
#note55.1
#note55.2
#nref55.1
#nref55.2
#note57.1
#note57.2
#note57.3
#note57.5
#note57.6
#note57.7
#nref57.1
#nref57.2
#nref57.3
#nref57.4
#nref57.5
#nref57.6
#nref57.7
#r66
#r67
#note58.1
#nref58.1
#nref58.2
#r68
#r69
#note61.1
#note61.2
#nref61.1
#nref61.2
#nref61.3
#note62.1
#nref62.1
#r70
#note63.1
#note63.2
#note63.3
#note63.4
#note63.5
#note63.6
#note63.7
#nref63.1
#nref63.2
#nref63.3
#nref63.4
#nref63.5
#nref63.6
#nref63.7
#r71
#r72
#note64.1
#note64.2
#note64.3
#note64.4
#note64.5
#note64.6
#note64.7
#note64.8
#note64.9
#note64.10
#note64.11
#note64.12
#note64.13
#note64.14
#note64.15
#note64.16
#note64.17
#note64.18
#note64.19
#note64.21
#note64.22
#note64.23
#note64.24
#note64.25
#nref64.1
#nref64.2
#nref64.3
#nref64.4
#nref64.5
#nref64.6
#nref64.7
#nref64.8
#nref64.9
#c_123.10
#nref64.10
#nref64.11
#nref64.12
#nref64.13
#nref64.14
#nref64.15
#nref64.16
#nref64.17
#nref64.18
#nref64.19
#nref64.20
#nref64.21
#nref64.22
#nref64.23
#nref64.24
#nref64.25
#r73
#r74
#r75
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#r77
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#r80
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#note68.1
#note68.2
#note68.3
#note68.4
#nref68.1
#nref68.2
#nref68.3
#nref68.4
#note69.1
#note69.2
#note69.3
#note69.4
#note69.5
#note69.6
#r81
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#note70.8
#nref69.1
#nref69.2
#nref69.3
#nref69.4
#nref69.5
#nref69.6
#nref70.7
#nref70.8
#r82
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#note71.2
#note71.3
#note71.4
#note71.5
#note71.6
#note71.7
#note71.8
#note71.9
#nref71.1
#nref71.2
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#nref71.4
#nref71.5
#nref71.6
#nref71.7
#nref71.8
#c_136.7
#nref71.9
#note57.5
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#note72.3
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#nref72.2
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#pl21
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#r87
#note75.1
#note75.2
#note75.3
#nref75.1
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#nref75.3
#note76.1
#c_140.28
#nref76.1
#note77.1
#note77.2
#note77.3
#nref77.1
#nref77.2
#nref77.3
#note78.1
#note78.2
#note78.3
#note78.4
#note78.5
#c_143.25
#note78.6
#note78.7
#note78.8
#c_214.8
#note78.9
#note78.10
#note78.11
#nref78.1
#nref78.2
#nref78.3
#note72.3
#nref78.4
#nref78.5
#nref78.6
#nref78.7
#nref78.8
#nref78.9
#nref78.10
#nref78.11
#r88
#r89
#note80.1
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#nref80.1
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#note82.1
#note82.2
#nref82.1
#nref82.2
#r90
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#note83.2
#note83.3
#nref83.1
#nref83.2
#nref83.3
#note84.1
#note84.2
#note84.3
#note84.4
#nref84.1
#nref84.2
#nref84.3
#nref84.4
#note85.1
#note85.2
#c_154.15
#note85.3
#nref85.1
#nref85.2
#nref85.3
#note86.1
#note86.2
#nref86.1
#nref86.2
#note64.21
#c_157.9
#note88.1
#note88.2
#note88.3
#nref88.1
#nref88.2
#nref88.3
#note89.1
#note89.2
#nref89.1
#nref89.2
#note90.1
#note90.2
#note90.3
#nref90.1
#nref90.2
#nref90.3
#note91.1
#nref91.1
#pl23
#pl24
#note92.1
#note92.2
#note92.3
#nref92.1
#nref92.2
#nref92.3
#r91
#note93.1
#note93.3
#nref93.1
#nref93.2
#nref93.3
#note94.1
#note94.2
#note94.3
#nref94.1
#nref94.2
#nref94.3
#note95.1
#note95.3
#note95.4
#note95.5
#nref95.1
#nref95.2
#nref95.3
#nref95.4
#nref95.5
#pl25
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#note98.1
#note98.2
#note98.3
#note98.4
#note98.5
#note98.6
#nref98.1
#nref98.2
#nref98.3
#nref98.4
#nref98.5
#nref98.6
#c_167.3
#r92
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#note99.3
#note99.4
#note99.5
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#note99.6
#note99.7
#note99.9
#note99.11
#nref99.1
#nref99.2
#nref99.3
#nref99.4
#nref99.5
#nref99.6
#nref99.7
#note15.9
#nref99.8
#nref99.9
#note71.1
#nref99.10
#nref99.11
#note100.1
#note100.2
#nref100.1
#nref100.2
#note101.1
#note101.2
#nref101.1
#nref101.2
#note102.1
#nref102.1
#note103.1
#nref103.1
#note104.1
#nref104.1
#note76.1
#note105.1
#note105.2
#note105.3
#note105.4
#nref105.1
#nref105.2
#nref105.3
#nref105.4
#note106.1
#note106.2
#nref106.1
#nref106.2
#pl27
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#note108.1
#note108.2
#note108.4
#note108.5
#note108.6
#note108.7
#note108.8
#nref108.1
#nref108.2
#c_180.2
#nref108.3
#nref108.4
#nref108.5
#nref108.6
#nref108.7
#nref108.8
#r93
#note109.1
#note109.2
#note109.3
#note109.4
#note109.5
#nref109.1
#nref109.2
#nref109.3
#nref109.4
#nref109.5
#r94
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#note112.1
#note112.2
#note112.3
#note112.4
#note112.5
#note112.6
#note112.8
#nref112.1
#note18.6
#nref112.2
#nref112.3
#nref112.4
#nref112.5
#nref112.6
#nref112.7
#nref112.8
#r95
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#note113.2
#note113.3
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#note114.3
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#nref114.1
#nref114.2
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#nref114.4
#nref114.5
#r96
#r97
#note115.1
#note115.2
#note115.3
#note115.4
#note115.5
#nref115.1
#nref115.2
#nref115.3
#note125.33
#nref115.4
#nref115.5
#note110.1
#note110.2
#note110.3
#note110.4
#note110.5
#note110.6
#note110.7
#note110.8
#note110.9
#note110.10
#note110.11
#note110.12
#note110.13
#note110.14
#note110.15
#note110.16
#note110.17
#note110.18
#note110.19
#note40.1
#nref110.1
#nref110.2
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#nref110.5
#nref110.6
#nref110.7
#nref110.8
#nref110.9
#nref110.10
#nref110.11
#nref110.12
#nref110.13
#nref110.14
#nref110.15
#note17.20
#nref110.16
#nref110.17
#note62.1
#nref110.18
#nref110.19
#r98
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#note117.1
#note117.2
#note117.3
#note117.4
#note117.5
#note117.6
#note117.7
#nref117.1
#nref117.2
#nref117.3
#nref117.4
#c_205.11
#nref117.5
#nref117.6
#nref117.7
#note118.1
#nref118.1
#note119.1
#nref119.1
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#nref119.2
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#note124.4
#note124.5
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#nref124.2
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#note125.2
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#note125.5
#note125.6
#note125.7
#note125.8
#note125.9
#note125.10
#note125.11
#note125.12
#note125.13
#note125.14
#note125.15
#note125.16
#note125.17
#note125.18
#note125.19
#note125.20
#note125.21
#note125.22
#note125.23
#note125.24
#note125.25
#note125.26
#note125.27
#note125.28
#note125.29
#note125.30
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#note125.31
#note125.32
#note125.33
#note125.34
#note125.35
#note125.36
#note125.37
#note125.38
#note125.39
#note125.40
#note125.41
#note125.43
#note125.44
#note125.45
#note125.46
#c_208.10
#note125.47
#note125.48
#c_218.28
#note125.49
#note125.50
#note125.51
#note125.52
#note125.53
#note125.54
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#nref125.22
#nref125.23
#nref125.24
#nref125.25
#nref125.26
#nref125.27
#nref125.28
#nref125.29
#nref125.30
#nref125.31
#nref125.32
#nref125.33
#note110.3
#nref125.34
#nref125.35
#note63.6
#nref125.36
#nref125.37
#nref125.38
#note26.3
#nref125.39
#nref125.40
#nref125.41
#nref125.42
#nref125.43
#nref125.44
#nref125.45
#nref125.46
#nref125.47
#nref125.48
#nref125.49
#nref125.50
#nref125.51
#nref125.52
#nref125.53
#nref125.54
#note125.2
#r111
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#note126.2
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#note126.6
#note126.7
#nref126.1
#note5.5
#nref126.2
#nref126.3
#nref126.4
#note40.6
#nref126.5
#c_247.30.1
#nref126.6
#nref126.7
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#r138
#note127.1
#note127.3
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#c_249.32
#nref127.2
#nref127.3
#note126.4
#r139
#note128.1
#note128.2
#note128.3
#note128.4
#note128.5
#note128.6
#note128.7
#note128.8
#note128.9
#note128.10
#note128.11
#note128.12
#nref128.1
#nref128.2
#nref128.3
#note18.3
#nref128.4
#note1.2
#nref128.5
#nref128.6
#note18.2
#nref128.7
#nref128.8
#nref128.9
#note22.2
#nref128.10
#note78.4
#nref128.11
#nref128.12
#r140
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#r143
#note130.1
#note130.2
#note130.3
#note130.4
#note130.5
#note130.6
#note130.7
#note130.8
#note130.9
#note130.10
#note130.11
#nref130.1
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#note130.11
#nref130.2
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#nref130.4
#nref130.5
#nref130.6
#nref130.7
#note130.5
#nref130.8
#nref130.9
#nref130.10
#nref130.11
#c_260.28
#note131.1
#note131.2
#note131.3
#note131.4
#note131.5
#note131.6
#nref131.1
#nref131.2
#nref131.3
#nref131.4
#nref131.5
#note64.13
#nref131.6
#note132.1
#note132.2
#note132.3
#nref132.1
#nref132.2
#nref132.3
#note133.1
#note133.2
#note133.3
#note133.4
#note133.5
#note133.6
#note133.7
#note133.8
#nref133.1
#nref133.2
#nref133.3
#nref133.4
#nref133.5
#nref133.6
#c_267.13
#nref133.7
#nref133.8
#r144
#note134.1
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#note134.3
#note134.4
#nref134.1
#nref134.2
#nref134.3
#nref134.4
#note135.1
#note135.2
#nref135.1
#nref135.2
#note136.1
#note136.3
#note136.5
#note136.6
#note136.7
#note136.8
#note136.9
#note136.11
#nref136.1
#nref136.2
#c_272.24
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#nref136.3
#nref136.4
#note74.1
#nref136.5
#nref136.6
#nref136.7
#nref136.8
#nref136.9
#nref136.10
#nref136.11
#r145
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#note137.1
#note137.2
#note137.3
#note137.4
#note137.5
#nref137.1
#nref137.2
#nref137.3
#nref137.4
#c_276.25
#nref137.5
#nref137.6
#note138.1
#note138.2
#note138.3
#note138.4
#note138.5
#c_277.12
#note138.6
#note138.7
#note138.8
#nref138.1
#c_277.21
#nref138.2
#nref138.3
#nref138.4
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