#] #] ********************* #] "$d_web"'Neural nets/callerID-SNNs/0_callerID_SNNs neuroscience.txt' # www.BillHowell.ca 08Jun2015 initial, 26Feb2020 initial - did I lose the original file? # view in text editor, using constant-width font (eg courier), tabWidth = 3 see also : "$d_web"'Neural nets/MindCode/0_MindCode & callerID-SNNs.links.txt' #48************************************************48 #24************************24 # Table of Contents, generate with : # $ grep "^#]" "$d_web"'Neural nets/callerID-SNNs/0_callerID_SNNs neuroscience.txt' | sed "s/^#\]/ /" # ********************* "$d_web"'Neural nets/callerID-SNNs/0_callerID_SNNs neuroscience.txt' 30Oct2023 search "mycellium neural networks" from neighbor Sabrina 28Oct2023 Awesome references : my review of NCAA-D-23-06459 Guo,Liu,Wu,Xu: FPGA small-world 30Nov2021 INNS newsletter: SNNs references 29Aug2020 Garcia etal - Small Universal Spiking Neural P Systems (delays & feedback) 24Feb2020 QNial basis 21Feb2020 Initial QNial programming (no more [yap, arm-waving]) 12Aug2019 Spikeless information transfer? 25Jul2019 Alice Parker. Spiking Neural Networks & DNA. USC. Los Angeles #24************************24 # Setup, ToDos, #08********08 #] ??Nov2023 #08********08 #] ??Nov2023 #08********08 #] ??Nov2023 #08********08 #] ??Nov2023 #08********08 #] ??Nov2023 #08********08 #] ??Nov2023 #08********08 #] 30Oct2023 search "mycellium neural networks" from neighbor Sabrina +-----+ https://www.nature.com/articles/d41586-023-03272-3 25 October 2023 AI ‘breakthrough’: neural net has human-like ability to generalize language A neural-network-based artificial intelligence outperforms ChatGPT at quickly folding new words into its lexicon, a key aspect of human intelligence. Scientists have created a neural network with the human-like ability to make generalizations about language1. The artificial intelligence (AI) system performs about as well as humans at folding newly learned words into an existing vocabulary and using them in fresh contexts, which is a key aspect of human cognition known as systematic generalization. The researchers gave the same task to the AI model that underlies the chatbot ChatGPT, and found that it performs much worse on such a test than either the new neural net or people, despite the chatbot’s uncanny ability to converse in a human-like manner. The work, published on 25 October in Nature, could lead to machines that interact with people more naturally than do even the best AI systems today. Although systems based on large language models, such as ChatGPT, are adept at conversation in many contexts, they display glaring gaps and inconsistencies in others. The neural network’s human-like performance suggests there has been a “breakthrough in the ability to train networks to be systematic”, says Paul Smolensky, a cognitive scientist who specializes in language at Johns Hopkins University in Baltimore, Maryland. ... But this ability does not come innately to neural networks, a method of emulating human cognition that has dominated artificial-intelligence research, says Brenden Lake, a cognitive computational scientist at New York University and co-author of the study. Unlike people, neural nets struggle to use a new word until they have been trained on many sample texts that use that word. AI researchers have sparred for nearly 40 years as to whether neural networks could ever be a plausible model of human cognition if they cannot demonstrate this type of systematicity. +-----+ https://microdose-journey.com/mycelium-networks/ Mycelium networks: The Earth’s neural connections +-----+ https://en.wikipedia.org/wiki/Mycorrhizal_network Mycorrhizal network A mycorrhizal network (also known as a common mycorrhizal network or CMN) is an underground network found in forests and other plant communities, created by the hyphae of mycorrhizal fungi joining with plant roots. This network connects individual plants together. Mycorrhizal relationships are most commonly mutualistic, with both partners benefiting, but can be commensal or parasitic, and a single partnership may change between any of the three types of symbiosis at different times.[1] The formation and nature of these networks is context-dependent, and can be influenced by factors such as soil fertility, resource availability, host or mycosymbiont genotype, disturbance and seasonal variation.[2] Some plant species, such as buckhorn plantain, a common lawn and agricultural weed, benefit from mycorrhizal relationships in conditions of low soil fertility, but are harmed in higher soil fertility.[3] Both plants and fungi associate with multiple symbiotic partners at once, and both plants and fungi are capable of preferentially allocating resources to one partner over another.[4] Referencing an analogous function served by the World Wide Web in human communities, the many roles that mycorrhizal networks appear to play in woodland have earned them a colloquial nickname: the Wood Wide Web.[5][6] Many of the claims made about common mycorrhizal networks, including that they are ubiquitous in forests, that resources are transferred between plants through them, and that they are used to transfer warnings between trees, have been criticised as being not strongly supported by evidence.[7][8][9] #08********08 #] 28Oct2023 Awesome references : my review of NCAA-D-23-06459 Guo,Liu,Wu,Xu: FPGA small-world /home/bill/PROJECTS/My Reviews/NCAA-D-23-06459 b Guo,Liu,Wu,Xu: FPGA-based small-world spiking neural network with anti-interference ability under external noise.txt [30] Lubeiro A, Fatjó-Vilas M, Guardiola M, et al (2020) Analysis of KCNH2 and CACNA1C schizophrenia risk genes on EEG functional network modulation during an auditory odd-ball task. European Archives of Psychiatry and Clinical Neuroscience 270:433–442. https://doi.org/10.1007/s00406-018-0977-0 [31] Zhang Y, Ren J, Qin Y, et al (2020) Altered topological organization of functional brain networks in drug-naive patients with paroxysmal kinesigenic dyskinesia. Journal of the Neurological Sciences 411:116702. https://doi.org/10.1016/j.jns. 2020.116702 [32] Zhu Y, Lu T, Xie C, et al (2020) Functional disorganization of small-world brain networks in patients with ischemic leukoaraiosis. Frontiers in Aging Neuroscience 12:203. https://doi.org/10.3389/fnagi.2020.00203 [33] Kawai Y, Park J, Asada M (2019) A small-world topology enhances the echo state property and signal propagation in reservoir computing. Neural Networks 112:15–23. https://doi.org/10.1016/j.neunet.2019.01.002 [34] Guo L, Hou L, Wu Y, et al (2020) Encoding specificity of scale-free spiking neural network under different external stimulations. Neurocomputing 418:126– 138. https://doi.org/10.1016/j.neucom.2020.07.111 [35] Wen S, Hu R, Yang Y, et al (2019) Memristor-based echo state network with online least mean square. IEEE Transactions on Systems, Man, and Cybernetics: Systems 49:1787–1796. https://doi.org/10.1109/TSMC.2018.2825021 [36] Deng B, Zhu Z, Yang S, et al (2016) FPGA implementation of motifs-based neuronal network and synchronization analysis. Physica A: Statistical Mechanics and its Applications 451:388–402. https://doi.org/10.1016/j.physa.2016.01.052 08********08 #] 11Aug2023 have been thinking of spike-time-arrival distribution at synapses +-----+ https://www.nature.com/articles/srep39682 Published: 03 January 2017 Dendritic and Axonal Propagation Delays Determine Emergent Structures of Neuronal Networks with Plastic Synapses Mojtaba Madadi Asl, Alireza Valizadeh & Peter A. Tass Scientific Reports volume 7, Article number: 39682 (2017) Abstract Spike-timing-dependent plasticity (STDP) modifies synaptic strengths based on the relative timing of pre- and postsynaptic spikes. The temporal order of spikes turned out to be crucial. We here take into account how propagation delays, composed of dendritic and axonal delay times, may affect the temporal order of spikes. In a minimal setting, characterized by neglecting dendritic and axonal propagation delays, STDP eliminates bidirectional connections between two coupled neurons and turns them into unidirectional connections. In this paper, however, we show that depending on the dendritic and axonal propagation delays, the temporal order of spikes at the synapses can be different from those in the cell bodies and, consequently, qualitatively different connectivity patterns emerge. In particular, we show that for a system of two coupled oscillatory neurons, bidirectional synapses can be preserved and potentiated. Intriguingly, this finding also translates to large networks of type-II phase oscillators and, hence, crucially impacts on the overall hierarchical connectivity patterns of oscillatory neuronal networks. Although this interesting property can explain the emergence of feedforward networks 2,25,26, it is in contradiction to the prevalence of recurrent connections between pairs of neurons in cortical networks 27,28. Second, STDP inherently is an unstable process, since it provides a positive feedback interaction among synaptic modification between two neurons and changes in their relative spike times, i.e. the more stronger the connection from neuron 1 to neuron 2, the more likely neuron 2 will fire shortly after the firing of neuron 1, leading to more potentiation of the corresponding synapse. The same argument can be brought forward for the depression of the synapses, and taken together, STDP leads to a bimodal distribution of the synaptic strengths when hard boundaries limit the upper and lower values of synaptic strengths2,29,30. This result also does not comply with the unimodal distribution of cortical synaptic efficacies reported for cortical networks28,31. Several variations of the STDP rule have been proposed in recent years and each of them amend some of the inconsistencies between the spike-timing based plasticity models and experimental results2,9,19,29,32,33,34. /home/bill/web/ProjMajor/MindCode/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 Mojtaba Madadi Asl, Alireza Valizadeh, Peter A. Tass 2017 "Dendritic and Axonal Propagation Delays Determine Emergent Structures of Neuronal Networks with Plastic Synapses" Scientific Reports volume 7, Article number: 39682 https://www.nature.com/articles/srep39682 /home/bill/web/References/Neural Nets/MindCode/Asl, Valizadeh, Tass 2017 Dendritic and Axonal Propagation Delays Determine Emergent Structures of Neuronal Networks with Plastic Synapses.pdf +-----+ https://www.jneurosci.org/content/40/21/4185 Modeling the Short-Term Dynamics of in Vivo Excitatory Spike Transmission Abed Ghanbari, Naixin Ren, Christian Keine, Carl Stoelzel, Bernhard Englitz, Harvey A. Swadlow and Ian H. Stevenson Journal of Neuroscience 20 May 2020, 40 (21) 4185-4202; DOI: https://doi.org/10.1523/JNEUROSCI.1482-19.2020 Abstract : ... Here, we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and nonsynaptic effects based only on observed presynaptic and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a pair of connected neurons within thalamus in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multielectrode recordings to illustrate how such an approach has the potential to reveal cell type-specific differences in spike transmission in vivo. Although STP parameters estimated from ongoing presynaptic and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses. 08********08 #] 30Nov2021 INNS newsletter: SNNs references https://files.constantcontact.com/16470081701/7b098635-f270-4530-887c-323ad01a19da.pdf +-----+ https://www.researchgate.net/project/Modelling-of-voluntary-saccadic-eye-movements-during-decision-making https://nest-initiative.org/ The NEST Initiative has advanced computational neuroscience since 2001 by pushing the limits of large-scale simulations of biologically realistic neuronal networks. Since 2012, the NEST Initiative is incorporated as a non-profit member-based organization promoting scientfic collaboration in computational neuroscience. The Board and Members govern the NEST Initiative in accordance to its Statutes. What we do As a community of developers: We coordinate and guide the development of the NEST Simulator. We regularly publish on simulation technology, data structures and algorithms for large-scale neuronal network simulation: Latest Publications [DOI] Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M and Kunkel S (2018) Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Frontiers in Neuroinformatics [DOI] Krishnan J, Porta Mana P, Helias M, Diesmann M and Di Napoli E (2018) Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons. Frontiers in Neuroinformatics [DOI] Ippen T, Eppler JM., Plesser HE and Diesmann M (2017) Constructing neuronal network models in massively parallel environments. Front. Neuroinform. [DOI] Plesser H, Diesmann M, Gewaltig M, Morrison A (2015) Nest: the neural simulation tool. In Encyclopedia of computational neuroscience, ed. Jaeger D Jung R 1849-1852Springer New York. . [DOI] Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M (2015) A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers in neuroinformatics 9:22. [DOI] Kunkel S, Schmidt M, Eppler J M, Plesser H E, Masumoto G, Igarashi J, Ishii S, Fukai T, Morrison A, Diesmann M, Helias M (2014) Spiking network simulation code for petascale computers. Frontiers in neuroinformatics 8:78. +-----+ New Book by INNS Fellow on Psychology and Society-By Daniel S. Levine, University of Texas at ArlingtonRoutledge published in December, 2020 (with a 2021 copyright) a book for a general audience by Daniel S. Levine, a Fellow of INNS who served in 1998 as INNS President and was General Co-Chair of the 2013 IJCNN. The book is entitled Healing the Reason-Emotion Split, with the subtitle Scarecrows, Tin Woodmen, and the Wizard.The main argument of the book is that the common cultural idea that emotion and reason are opposites, with reason considered superior to emotion, is not supported by science and is harmful to society. Evidence supporting this argument is presented from the findings of Antonio Damasio, Luiz Pessoa, and other neuroscientists that emotion is essential to effective decision making and that emotional regions of the brain are subject to attentional control. Evidence is also presented from various experimental psychology laboratories showing that emotion and cognition are deeply interconnected and that optimal cognitive function requires emotional stimulation rather than quiescence. The book is not primarily a book about neural networks but there is some discussion of biologically realistic neural network models by the author, Stephen Grossberg, and a few other researchers that incorporate these emotional-cognitive interactions.The book reviews historical movements such as the Enlightenment that privileged reason over emotion, and other historical movements such as Romanticism and the 1960s counterculture that did the opposite. At the end it calls for synthesizing the best of both the Enlightenment and INNS Members News5Romanticism to promote optimal brain functioning as needed to solve the societal problems caused by globalization, climate change, and pandemics.The book is 146 pages long including bibliography and index, and available in paperback, hardback, e-book, and Kindle. Ordering information, the table of contents, and endorsements are available from https://www.amazon.com/Healing-Reason-Emotion-Split-Scarecrows-Woodmen/dp/0367856840/ref=sr_1_1?dchild=1&keywords=Healing%20the%20Reason-Emotion%20Split&qid=1607268654&sr=8-1&fbclid=IwAR1UzioSDkmlCvYuxEi3vpCh6jy-D5FClzhcqUFlj7QtKyFaiLUJQbfWZOE https://www.routledge.com/Healing-the-Reason-Emotion-Split-Scarecrows-Tin-Woodmen-and-the-Wizard/Levine/p/book/9780367856830 #08********08 #] 29Aug2020 Garcia etal - Small Universal Spiking Neural P Systems (delays & feedback) NEUNET-D-20-00790 p Garcia etal - Small Universal Spiking Neural P Systems with dendritic-axonal delays and dendritic trunk-feedback.pdf Uses a simple "spike inventory in the soma" count to represent integers, and mmust spike n time for the number n etc... >> use as an alternate conflicting hypothesis #08********08 #] 24Feb2020 QNial basis see link d_QNial_mine 'MindCode/1_MindCode summary ref.txt' to test spiking, I must build some microNNs but first build a few basic arithmetic operators #08********08 #] 21Feb2020 Initial QNial programming (no more [yap, arm-waving]) Izhikevich-like neuron - with [DNA-mRNA-epi, addressable synapses] loaddefs link d_QNial_mine 'MindCode/Izhikevich-like neuron - with [DNA-mRNA-epi, addressable synapses].ndf' #08********08 #] 12Aug2019 Spikeless information transfer? - are current concepts too focused on spikes? - can synapses transfer information (states) without a spike? - if so, what does a spike do? When does a neuron spike? - build-up of membrane potential might be driven by [multiple conflicting states, neighboring neuron membrane potentials, spiking in region]? - but a neuron may have MANY co-existing states - states can relate a neuron to many other neurons - are synaptic changes very [fast, short-lived]? - spiking may "clear the states"?, signal that a state is confirmed, ... all of the above Synaptic information is different for same neuron but [different, same] synapse - as allowed by many different mRNA at synapse Multiple conflicting states - states may relate to completely different functionalities for the same neuron - logic gates have small number of states, low diversity of [broad, general] interest - numbers - arithmetic, functions, - dynamics - calculus - ADP, solution to simultaneous equations, - sensory, attention, conciousness - pattern matching (can subsume logic, etc) - [morphing, overloading] of neurons to be much more powerful within a class of [similar, related] [functions, patterns, etc] architectures - perceptron - of very general use - kernels (Johan Suyken's comment for Deep Learning - convexity) #08********08 #] 25Jul2019 Alice Parker. Spiking Neural Networks & DNA. USC. Los Angeles Alice Parker. Spiking Neural Networks & DNA. USC. Los Angeles. USA - Mindcode Lunch at restaurant after IJCNN2019 in Budapest I sent an email about DNA-SNNs /media/bill/SWAPPER/Projects - big/MindCode/Howell 050824 Junk DNA & NeuralNetworks, conjecture on directions and implications, IJCNN05 workshop panel presentation.ppt /media/bill/SWAPPER/Projects - big/MindCode/Howell 060215 Genetic specification of neural networks, draft concepts and implications.odt /media/bill/SWAPPER/Projects - big/MindCode/Howell 060215 Genetic specification of neural networks, draft concepts and implications.pdf /media/bill/SWAPPER/Projects - big/MindCode/Howell 060716 Genetic specification of recurrent neural networks, Initial thoughts, WCCI 2006 paper 1341.ppt /media/bill/SWAPPER/Projects - big/MindCode/Howell 060721 Genetic Specification of Recurrent Neural Networks Initial Thoughts, WCCI 2006 presentation.ppt /media/bill/SWAPPER/Projects - big/MindCode/Howell 150225 - MindCode Manifesto.odt /media/bill/SWAPPER/Neural Nets/Confabulation/Howell 110903 - Confabulation Theory, Plausible next sentence survey.pdf /media/bill/SWAPPER/Website/Social media/Howell 110902 – Systems design issues for social media.pdf /media/bill/SWAPPER/Website/Social media/Howell 111006 – Semantics beyond search.pdf /media/bill/SWAPPER/Website/Social media/Howell 111117 - How to set up & use data mining with Social media.pdf /media/bill/SWAPPER/Website/Social media/Howell 111230 – Social graphs, social sets, and social media.pdf http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20050824%20Junk%20DNA%20&%20NeuralNetworks,%20conjecture%20on%20directions%20and%20implications,%20IJCNN05%20workshop%20panel%20presentation.ppt http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20060215%20Genetic%20specification%20of%20neural%20networks,%20draft%20concepts%20and%20implications.odt http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20060215%20Genetic%20specification%20of%20neural%20networks,%20draft%20concepts%20and%20implications.pdf http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20060716%20Genetic%20specification%20of%20recurrent%20neural%20networks,%20Initial%20thoughts,%20WCCI%202006%20paper%201341.pdf http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20060721%20Genetic%20Specification%20of%20Recurrent%20Neural%20Networks%20Initial%20Thoughts,%20WCCI%202006%20presentation.ppt http://www.billhowell.ca/Neural%20nets/MindCode/Howell%20150225%20-%20MindCode%20Manifesto.odt http://www.billhowell.ca/Social%20media/Howell%20111230%20–%20Social%20graphs,%20social%20sets,%20and%20social%20media.pdf http://www.billhowell.ca/Social%20media/Howell%20110902%20–%20Systems%20design%20issues%20for%20social%20media.pdf http://www.billhowell.ca/Social%20media/Howell%20111006%20-%20SPINE,%20Semantics%20beyond%20search.pdf http://www.billhowell.ca/Social%20media/Howell%20111117%20-%20How%20to%20set%20up%20&%20use%20data%20mining%20with%20Social%20media.pdf # enddoc