[core, fun, strange] concepts of Grossberg

Thematic collections of Grossberg's [core, fun, strange] concepts

Introduction
principles, architecture, function, process
[stable, robust, adaptive] learning
family of ART.....base, linguistic
family of ART ....visual, auditory
consciousness
random fun themes
Strange themes
Successful [datamodelling, applications] showing effectiveness of cART etc
- [bio, neuro, psycho]logy data
- [software, engineering, other] applications

Introduction

Below are examples of "thematic collections" from Stephen Grossberg's book "Conscious Mind, Resonant brain". Explanations are provided in the Help menu, as per :

The "grepStr" (used by linux grep) listed at the start of each theme shows the keywords that were used.

principles, architecture, function, process

Principles, Principia

grepStr = Principles-Principia|behavior-mind-brain link|cycle of model evolution

Grossberg 2021 p081-83 sub-section: How does one evolve a computational brain?
- Behavioral Success drives::>> Behavioral Data -> Art of Modelling -> Design Principles -> Mathematical Models -> Method of Minimal Anatomies (Occam's Razor) -> Behavioral Predictions -> explain Neural Data -> novel Brain Predictions <<::links behavior-mind-brain
- "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything.
- >characterize the boundary between the known and unknown: the shape of this boundary helps to direct attention to new design principles
(keys : behavior-mind-brain link)
Grossberg 2021 p081-83 The cycle of model evolution :
- The next step is to show how these new design principles can be incorporated into the evolved model in a self-consistent way, without undermining its previous mechanisms, thereby leading to a progressively more realistic model, one that can explain and predict ever more behavioral and neural data.
- In this way, the model undergoes a type of evolutionary development, as it becomes able to cope behaviorally with environmental constraints of ever increasing subtlety and complexity.
- The Method of Minimal Anatomies may hereby be viewed as way to functionally understand how increasingly demanding combinations of environmental pressures were incorporated into brains during the evolutionary process.
(keys : brain evolution, cycle of model evolution)
Grossberg 2021 p081c2h0.66 The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved
(keys : mind-body -classical- problem)
Grossberg 2021 p081c2h0.66 sub-section: How does one evolve a computational brain?
The above discussion illustrates that no single step of theoretical derivation can derive a whole brain. One needs a method for deriving a brain in stages, or cycles, much as evolution has incrementally discovered ever more complex brains over many thousands of years. The following theoretical method has been successfully applied many times since I first used it in 1957. It embodies a kind of conceptual evolutionary process for deriving a brain.

Because "brain evolution needs to achieve behavioural success", we need to start with data that embodiey indices of behavioral success. That is why, as illustrated in Figure 2.37 Modelling method and cycle, one starts with Behavioral Data from scores or hundreds of psychological experiments. These data are analyszed as the result of an individual adapting autonomously in real time to a changing world. This is the Arty of Modeling. It requires that one be able to infer from static data curves the dynamical processes that control individual behaviors occuring in real time. One of the hardest things that I teach to my students to do is "how to think in real time" to be able to carry out this speculative leap.

Properly carried out, this analysis leads to the discovery of new Design Principles that are embodied by these behavioral processes. The Design Principles highlight the functional meaning of the data, and clarify how individual behaviors occurring in real time give rise to these static data curves.

These principles are then converted into the simplest Mathematical Model using a method of minimal anatomies, which is a form of Occam's Razor, or principle of parsimony. Such a mathematical model embodies the psychological principles using the simplest possible differential equations. By "simplest" I mean that, if any part of the derived model is removed, then a significant fraction of the targeted data could no longer be explained. One then analyzes the model mathematically and simulates it on the computer, showing along the way how variations on the minimal anatomy can realize the design principles in different individuals or species.

This analysis has always provided functional explanations and Behavioral Predictions for much larger behavioral data bases than those used to discover the Design Principles. The most remarkable fact is, however, that the behaviorally derived model always looks like part of a brain, thereby explaining a body of challenging Neural Data and making novel Brain Predictions.

The derivation hereby links mind to brain via psychological organizational principles and their mechanistic realization as a mathematically defined neural network. This startling fact is what I first experienced as a college Freshman taking Introductory Psychology, and it changed my life forever.

I conclude from having had this experience scores of times since 1957 that brains look the way they do because they embody a natural computational realization for controlling autonomous adaptation in real-time to a changing world. Moreover, the Behavior -> Principles -> Model -> Neural derivation predicts new functional roles for both known and unknown brain mechanisms by linking the brain data to how it helps to ensure behavioral success. As I noted above, the power of this method is illustrated by the fact that scores of these predictions about brain and behavior have been supported by experimental data 5-30 years after they were first published.

Having made the link from behavior to brain, one can then "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything. At each stage of development, the model can cope with certain environmental challenges but not others. An important part of the mathematical and computational analysis is to characterize the boundary between the known and unknown; that is which challenges the model can cope with and which it cannot. The shape of this boundary between the known and unknown helps to direct the theorist's attention to new design principles that have been omitted from previous analysis.

The next step is to show how these new design principles can be incorporated into the evolved model in a self-consistent way, without undermining its previous mechanisms, thereby leading to a progressively more realistic model, one that can explain and predict ever more behavioral and neural data. In this way, the model undergoes a type of evolutionary development, as it becomes able to cope behaviorally with environmental constraints of ever increasing subtlety and complexity. The Method of Minimal Anatomies may hereby be viewed as way to functionally understand how increasingly demanding combinations of environmental pressures were incorporated into brains during the evolutionary process.

If such an Embedding Principle cannot be carried out - that is, if the model cannot be unlumped or refined in a self-consistent way - then the previous model was, put simply, wrong, and one needs to figure out which parts must be discarded. Such a model is, as it were, an evolutionary dead end. Fortunately, this has not happened to me since I began my work in 1957 because the theoretical method is so conservative. No theoretical addition is made unless it is supported by multiple experiments that cannot be explained in its absence. Where multiple mechanistic instantiations of some Design Principles were possible, they were all developed in models to better underestand their explanatory implications. Not all of these instantiations could survive the pressure of the evolutionary method, but some always could. As a happy result, all earlier models have been capable of incremental refinement and expansion.

The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved.

Howell: bold added for emphasis.
(keys : Principles-Principia, behavior-mind-brain link, brain evolution, cycle of model evolution)
see also quotes: Charles William Lucas "Universal Force" and others (not retyped yet).

equations of the [brain, mind]

grepStr = equation

p046sec Equations, modules, and modal architectures
p055sec Why differential equations? The case of phonemic restoration
p067sec Hartline-Ratliff equation for the horseshoe crab retina
p068sec Hodgkin-Huxley equations for the giant sqid axon
p075sec Deriving the membrane equations of neurophysiology from a thought experiment
image p059fig02.05 Bowed serial position curve. This kind of data emphasizes the importance of modelling how our brains give rise to our minds using nonlinear systems of differential equations.
|| Effects of [inter, intra]trial intervals (Hovland 1938). # of errors vs list position. [w (sec), W (sec)] = (2 6) (4 6) (2 126) (4 126). Nonoccurance of future items reduces the number of errors in response to past items. Thes data require a real-time theory for their explanation! that is, DIFFERENTIAL equations.
image p064fig02.10 The Shunting Model includes upper and lower bounds on neuronal activities. These bound have the effect of multiplying additive terms by excitatory and inhibitory automatic gain terms that enable such models to preserve their sensitivity to inputs whose size may vary greatly in size through time, while also approximately normalizing their total activities.
|| STM: Shunting Model (Grossberg, PNAS 1967, 1968). Mass action in membrane equations. Bi/Ci -> xi(t) -> O -> -Fi/Ei. Bounded activations, automatic gain control. d[dt: xi(t)] = - Ai*xi + (Bi - Ci*xi)sum[j=1 to n: fj(xj(t))*Dji*yji*zji + Ii] - (Ei*Xi + Fi)*sum[j=1 to n: gj(xj)*Gji*Yji*Zji + Ji]. Includes the Additive Model.
image p064fig02.11 Medium-Term Memory (MTM) and Long-Term Memory (LTM) equations complement the Additive and Shunting Models of STM. MTM is typically defined by a chemical transmitter that is released from the synaptic knobs of a neuron (Figure 2.03). Its release or inactivation in an activity-dependent way is also called habituation. LTM defines how associative learning occurs between a pair of neurons whose activities are approximately correlated through time. See the text for details.
|| Medium and Long Term memory.
MTM habituative transmitter gate d[dt: yki(t)] = H*(K - yki) - L*fk(xk)*yki
LTM gated steepest descent learning d[dt: zki(t)] = Mk*fk(xk)*(hi(xi) - zki)
image p068fig02.14 Hodgkin and Huxley developed a model to explain how spikes travel down the squid giant axon.
|| Neurophysiology (single cell): spike potentials in squid giant axon (Hodgekin, Huxley 1952, Nobel Prize). time -> (dendrites -> cell body -> axon).
C*dp[dt: V] = α*dp^2[dX^2: V] + (V(+) - V)*g(+) + (V(-) - V)*g(-) + (V^p - V)*g^p
g(+) = G(+)(m,h), g(-) = G(-)(n), G^p = const, [m, h, n] - ionic processes, V - voltage
Precursor of Shunting network model (Rail 1962). (Howell: see p075fig02.24 Membrane equations of neurophysiology. Shunting equation

image p074fig02.23 The equations for a shunting on-center off-surround network. Shunting terms lead to many beautiful and important properties of these networks, which are found ubiquitously, in one form or another, in all cellular tissues.
|| Shunting on-center off-surround network.

Mass action:	d[dt: xi] =	-A*xi	+(B - xi)*Ii	-xi*sum[k≠i: Ik]
			Turn on unexcited sites	Turn off excited sites

At equilibrium:
0 = d[dt: xi] = -(A + Ii + sum[k≠i: Ik])*xi + B*Ii = -(A + I)*xi + B*Ii

xi = BIi/(A + I) = BθiI/(A + I) = θi B*I/(A + I)	No saturation! Infinite dynamical range Automatic gain control Compute ratio scale Weber law
x = sum[k-1 to n: xk] = B*I/(A + I) ≤ B	Conserve total activity NORMALIZATION Limited capacty Real-time probability

image p075fig02.24 The membrane equations of neurophysiology describe how cell voltages change in response to excitatory, inhibitory, and passive input channels. Each channel is described by a potential difference multiplied by a conductance. With the special choices shown in the lower right-hand corner, this equation defines a feedforward shuntin on-center off-surround network.
|| Membrane equations of neurophysiology.
C*dp[dt] = (V(+) - V)*g(+) +(V(-) - V)*g(-) +(V(p) - V)*g(p)
Shunting equation (not additive)
V Voltage
V(+), V(-), V(p) Saturating voltages
g(+), g(-), g(p) Conductances
V(+) = B, C = 1; V(-) = V(p) = 0; g(+) = Ii; g(-) = sum[k≠i: Ik];
lower V: V(+) = V(p) Silent inhibition, upper V: V(+). (Howell: see p068fig02.14 Grossberg's comment that Hodgkin&Huxley model was a "... Precursor of Shunting network model (Rail 1962) ...").
image p079fig02.32 Matching amplifies the matched pattern due to automatic gain control. See terms I and J in the equation.
|| Substrate of resonance. Match (in phase) of BU and TD input patterns AMPLIFIES matched pattern due to automatic gain control by shunting terms. J = sum[i: Ji], I = sum[i: Ii], θi = (Ii + Ji)/(I + J)
xi = (B + C)*(I + J)/(A + I + J)*[θi -C/(B + C)]
Need top-down expectations to be MODULATORY.
image p202fig05.17 This figure summarizes the simplest equations whereby the adaptive weights of a winning category learn the input pattern that drove it to win, or more generally a time-average of all the input patterns that succeeded in doing so.
|| Geometry of choice and learning, learning trains the closest LTM vector
image p359fig10.07 The two bottom-up pathways from LGN to layer 4 of V1 can together activate layer 4 and contrast-normalize layer 4 responses.
|| Bottom-up contrast normalization (Grossberg 1968, 1973; Sperling, Sondhi 1968; Heeger 1992; Douglas etal 1995; Shapley etal 2004). Together, direct LGN-to-4 path and 6-to-4 on-center off-surround provide contrast normalization if cells obey shunting or membrane equation dynamics.
image p501fig13.26 A simple differential equation describes the processes of transmitter accumulation and release that do their best, at a finite rate, to carry out unbiased transduction.
|| Transmitter accumulation and release. Transmitter y cannot be restored at an infinite rate: T = S*ym y ~= B, Differential equations: d[dt: y] = A*(B - y) - S*y = accumulate - release. Transmitter y tries to recover to ensure unbiased transduction. What if it falls behind? Evolution has exploited the good properties that happen then.
image p505fig13.33 An unexpected event can disconfirm ongoing processing by triggering a burst of nonspecific arousal that causes antagonistic rebounds in currently active gated dipoles, whether cognitive or affective.
|| Novelty reset: rebound to arousal onset. 1. Equilibrate to I and J: S1 = f(I+J); y1 = A*B/(A+S1); S2 = f(I+J); y2 = A*B/(A+S2);. 2. Keep phasic input J fixed; increase arousal I to I* = I + ∆I: (a) OFF reaction if T1 < T2; OFF = T2 - T1 = f(I*+J)*y2 - f(I*)*y1 = { A*B*(f(I*) - f(I*+J)) - B*(f(I*)*f(I+J) - f(I)*f(I*+J)) } / (A+f(I)) / (A + f(I+J)). 3. How to interpret this complicated equation?
image p580fig16.05 Macrocircuit of the GridPlaceMap model, which can learn both 2D grid cells and place cells in response to realistic trajectories of navigating rats using a hierarchy of SOMs with identical equations.
|| GridPlaceMap model: rate-based and spiking (Pilly, Grossberg 2012). Pre-wired 1D stripe cells, learns both 2D frid and place cells! Same laws for both; both select most frequent and energetic inputs. Place cells emerge gradually in response to developing grid cells. [place-> grid-> stripe] cells-> path integration-> vestibular signals
image p586fig16.16 In the place cell learning model of (Gorchetnikov, Grossberg 2007), three populations of five cells each of entorhinal grid cells (only two are shown) with different spatial periods input to the model's dentate gyrus. The grid cells are one-dimensional and defined algorithmically. A model dentate gyrus granule cell that receives strong projections from all three grid cell scales fires (green cell) and activates a recurrent inhibitory interneuron that inhibits other granule cells. It also generates back-propagating action potentials that trigger learning in the adaptive weights of the projections from the grid cells, thereby causing learning of place cell receptive fields.
|| Grid-to-place Self-Organizing map (Gorchetnikov, Grossberg 2007). Formation of place cell fields via grid-to-place cell learning. Least common multiple: [grid (cm), place (m)] scales: [40, 50, 60 (cm); 6m], [50, 60, 70 (cm); 21m], [41, 53, 59 (cm); 1.282 km]. Our simulations: [40, 50 (cm); 2m], [44, 52 (cm); 5.72m]. Our SOM: Spiking Hodgkin-Huxley membrane equations; Nonlinear choice by contrast-enhancing recurrent on-center off-surround net;. Choice triggers back-propagating action potentials that induce STDP-modulated learning on cell dendrites.
image p593fig16.25 Spectral Spacing Model STM, MTM, and LTM equations. The rate spectrum that determines the dorsoventral gradient of multiple grid cell properties is defined by μm.
|| Spectral Spacing Model equations. [STM, MTM, LTM]. μm = rate spectrum.
Grossberg 2021 p081c2h0.66 sub-section: How does one evolve a computational brain?
The above discussion illustrates that no single step of theoretical derivation can derive a whole brain. One needs a method for deriving a brain in stages, or cycles, much as evolution has incrementally discovered ever more complex brains over many thousands of years. The following theoretical method has been successfully applied many times since I first used it in 1957. It embodies a kind of conceptual evolutionary process for deriving a brain.

Because "brain evolution needs to achieve behavioural success", we need to start with data that embodiey indices of behavioral success. That is why, as illustrated in Figure 2.37 Modelling method and cycle, one starts with Behavioral Data from scores or hundreds of psychological experiments. These data are analyszed as the result of an individual adapting autonomously in real time to a changing world. This is the Arty of Modeling. It requires that one be able to infer from static data curves the dynamical processes that control individual behaviors occuring in real time. One of the hardest things that I teach to my students to do is "how to think in real time" to be able to carry out this speculative leap.

Properly carried out, this analysis leads to the discovery of new Design Principles that are embodied by these behavioral processes. The Design Principles highlight the functional meaning of the data, and clarify how individual behaviors occurring in real time give rise to these static data curves.

These principles are then converted into the simplest Mathematical Model using a method of minimal anatomies, which is a form of Occam's Razor, or principle of parsimony. Such a mathematical model embodies the psychological principles using the simplest possible differential equations. By "simplest" I mean that, if any part of the derived model is removed, then a significant fraction of the targeted data could no longer be explained. One then analyzes the model mathematically and simulates it on the computer, showing along the way how variations on the minimal anatomy can realize the design principles in different individuals or species.

This analysis has always provided functional explanations and Behavioral Predictions for much larger behavioral data bases than those used to discover the Design Principles. The most remarkable fact is, however, that the behaviorally derived model always looks like part of a brain, thereby explaining a body of challenging Neural Data and making novel Brain Predictions.

The derivation hereby links mind to brain via psychological organizational principles and their mechanistic realization as a mathematically defined neural network. This startling fact is what I first experienced as a college Freshman taking Introductory Psychology, and it changed my life forever.

I conclude from having had this experience scores of times since 1957 that brains look the way they do because they embody a natural computational realization for controlling autonomous adaptation in real-time to a changing world. Moreover, the Behavior -> Principles -> Model -> Neural derivation predicts new functional roles for both known and unknown brain mechanisms by linking the brain data to how it helps to ensure behavioral success. As I noted above, the power of this method is illustrated by the fact that scores of these predictions about brain and behavior have been supported by experimental data 5-30 years after they were first published.

Having made the link from behavior to brain, one can then "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything. At each stage of development, the model can cope with certain environmental challenges but not others. An important part of the mathematical and computational analysis is to characterize the boundary between the known and unknown; that is which challenges the model can cope with and which it cannot. The shape of this boundary between the known and unknown helps to direct the theorist's attention to new design principles that have been omitted from previous analysis.

The next step is to show how these new design principles can be incorporated into the evolved model in a self-consistent way, without undermining its previous mechanisms, thereby leading to a progressively more realistic model, one that can explain and predict ever more behavioral and neural data. In this way, the model undergoes a type of evolutionary development, as it becomes able to cope behaviorally with environmental constraints of ever increasing subtlety and complexity. The Method of Minimal Anatomies may hereby be viewed as way to functionally understand how increasingly demanding combinations of environmental pressures were incorporated into brains during the evolutionary process.

If such an Embedding Principle cannot be carried out - that is, if the model cannot be unlumped or refined in a self-consistent way - then the previous model was, put simply, wrong, and one needs to figure out which parts must be discarded. Such a model is, as it were, an evolutionary dead end. Fortunately, this has not happened to me since I began my work in 1957 because the theoretical method is so conservative. No theoretical addition is made unless it is supported by multiple experiments that cannot be explained in its absence. Where multiple mechanistic instantiations of some Design Principles were possible, they were all developed in models to better underestand their explanatory implications. Not all of these instantiations could survive the pressure of the evolutionary method, but some always could. As a happy result, all earlier models have been capable of incremental refinement and expansion.

The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved.

Howell: bold added for emphasis.
(keys : Principles-Principia, behavior-mind-brain link, brain evolution, cycle of model evolution)
see also quotes: Charles William Lucas "Universal Force" and others (not retyped yet).
p586fig16.16 Howell: note use of term "back-propagating", albeit perhaps somewhat different from standard back-propagation in ANNs.
Grossberg: In the place cell learning model of (Gorchetnikov, Grossberg 2007), three populations of five cells each of entorhinal grid cells (only two are shown) with different spatial periods input to the model's dentate gyrus. The grid cells are one-dimensional and defined algorithmically. A model dentate gyrus granule cell that receives strong projections from all three grid cell scales fires (green cell) and activates a recurrent inhibitory interneuron that inhibits other granule cells. It also generates back-propagating action potentials that trigger learning in the adaptive weights of the projections from the grid cells, thereby causing learning of place cell receptive fields. || Grid-to-place Self-Organizing map (Gorchetnikov, Grossberg 2007). Formation of place cell fields via grid-to-place cell learning. Least common multiple: [grid (cm), place (m)] scales: [40, 50, 60 (cm); 6m], [50, 60, 70 (cm); 21m], [41, 53, 59 (cm); 1.282 km]. Our simulations: [40, 50 (cm); 2m], [44, 52 (cm); 5.72m]. Our SOM: Spiking Hodgkin-Huxley membrane equations; Nonlinear choice by contrast-enhancing recurrent on-center off-surround net;. Choice triggers back-propagating action potentials that induce STDP-modulated learning on cell dendrites.

modules & modal architectures ([micro, macro]-circuits)

grepStr = modules|microcircuit|modal architecture|macrocircuit

p000XIsec Mind-body problem: Brain theories assemble laws and modules into modal architectures
p032sec Complementary computing vs independent modules
p046sec Equations, modules, and modal architectures
p098sec Special-purpose modules or general-purpose modal systems?
p593sec Development of grid cell modules
image pxvifig00.01 Macrocircuit of the visual system
image p087fig03.01 A macrocircuit of key visual processes (in green) and the cortical areas in which they primarily occur (in red), from the retina to the Prefrontal Cortex (PFC), including both the What and Where cortical streams. The [bottom-up, horizontal, and top-down] interactions help each of these processes to overcome computationally complementary processing deficiencies that they would experience without them, and aso to read-out top-down expectations that help to stabilize learning while they focus attention on salient objects and positions.
|| Emerging unified theory of visual intelligence. [What, Where] streams. Bottom-up and top-down interactions overcome COMPLEMENTARY processing deficiencies.

image p168fig04.44 Macrocircuit of the main boundary and surface formation stages that take place from the lateral geniculate nucleus, or LGN, through cortical areas [V1, V2, V4]. See the text for details.
||

left eye		binocular	right eye
		V4 binocular surface
V2 monocular surface		V2 layer 2/3 binocular boundary		V2 monocular surface
		V2 layer 4 binocular boundary
V1 monocular surface	V1 monocular boundary	V1 binocular boundary	V1 monocular boundary	V1 monocular surface
	LGN		LGN

image p228fig05.37 A macrocircuit of the neurotrophic Spectrally Timed ART, or nSTART, model. I developed nSTART with my PhD student Daniel Franklin. It proposes how adaptively timed learning in the hippocampus, bolstered by Brain Derived Neurotrophic Factor, or BDNF, helps to ensure normal memory consolidation.
|| habituative gates, CS, US, Thalamus (sensory cortex, category learning, conditioned reinforcer learning, adaptively time learning and BDNF), Amygdala (incentive motivation learning), Hippocampus (BDNF), Prefrontal Cortex (attention), Pontine nuclei, Cerebellum (adaptively timed motor learning)
image p246fig05.48 Microcircuits of the LAMINART model that I developed with Rajeev Raizada. See the text for details of how they integrate bottom-up adaptive filtering, horizontal bipole grouping, and top-down attentional matching that satisfied the ART Matching Rule.
||
image p346fig09.16 A macrocircuit of some of the main brain regions that are used to move the eyes. Black boxes denote areas belonging to the saccadic eye movement systes (SAC), white boxes the smooth pursuit eye system (SPEM), and gray boxes, both systems. The abbreviations for the different brain regions are: LIP - Lateral Intra-Parietal area; FPA - Frontal Pursuit Area; MST - Middle Superior Temporal area; MT - Middle Temporal area; FEF - Frontal Eye Fields; NRPT - Nucleus Reticularis Tegmenti Pontis; DLPN - Dorso-Lateral Pontine Nuclei; SC - Superior Colliculus; CBM - CereBelluM; MVN/rLVN - Medial and Rostro-Lateral Vestibular Nucleii; PPRF - a Peri-Pontine Reticular Formation; TN - Tonic Neurons
||
image p436fig12.30 The conscious ARTWORD, or cARTWORD, laminar cortical speech model simulates how future context can disambiguate noisy past speech sounds in such a way that the completed percept is consciously heard to proceed from past to future as a feature-item-list resonant wave propagates through time.
|| cARTWORD: Laminar cortical model macrocircuit (Grossberg, Kazerounian 2011) Simulates PHONEMIC RESTORATION: Cognitive Working Memory (processed item sequences) - [Excitatory-> inhibitory-> habituative-> adaptive filter-> adaptive filter-> adaptive filter with depletable synapse-> Acoustic [item, feature]
image p474fig12.70 The kind of model macrocircuit that was used in (Grossberg, Stone 1986) to explain lexical decision task data.
|| inputs-> A1 <-> A2 oconic sensory features <-> A3 item and order in sensory STM <-> A4 list parsing in STM (masking field) <-> A5 semantic network (self-feedback). [A4, A5] <-> V* visual object recognition system. M1-> [outputs, A1]. M1 <-> M2 iconic motor features <-> M3 item and order in motor STM. A2-> M2. A3-> M3.
image p481fig13.01 Macrocircuit of the functional stages and anatomical interpretations of the Cognitive-Emotional-Motor, or CogEM, model.
|| Drive-> hypothalamus value categories <-> amygdala incentive motivational learning-> Orbitofrontal cortex- object-value categories <-> sensory cortex- invariant object categories- conditioned reinforcer learning-> amygdala-> hypothalamus.
image p520fig14.02 Macrocircuit of the main brain regions, and connections between them, that are modelled in the unified predictive Adaptive Resonance Theory (pART) of cognitive-emotional and working memory dynamics. Abbreviations in red denote brain regions used in cognitive-emotional dynamics. Those in green denote brain regions used in working memory dynamics. Black abbreviations denote brain regions that carry out visual perception, learning and recognition of visual object categories, and motion perception, spatial representation and target tracking. Arrow denote non-excitatory synapses. Hemidiscs denote adpative excitatory synapses. Many adaptive synapses are bidirectional, thereby supporting synchronous resonant dynamics among multiple cortical regions. The output signals from the basal ganglia that regulate reinforcement learning and gating of multiple cortical areas are not shown. Also not shown are output signals from cortical areas to motor responses. V1: striate, or primary, visual cortex; V2 and V4: areas of prestriate visual cortex; MT: Middle Temporal cortex; MST: Medial Superior Temporal area; ITp: posterior InferoTemporal cortex; ITa: anterior InferoTemporal cortex; PPC: Posterior parietal Cortex; LIP: Lateral InterParietal area; VPA: Ventral PreArculate gyrus; FEF: Frontal Eye Fields; PHC: ParaHippocampal Cortex; DLPFC: DorsoLateral Hippocampal Cortex; HIPPO: hippocampus; LH: Lateral Hypothalamus; BG: Basal Ganglia; AMYG: AMYGdala; OFC: OrbitoFrontal Cortex; PRC: PeriRhinal Cortex; VPS: Ventral bank of the Principal Sulcus; VLPFC: VentroLateral PreFrontal Cortex. See the text for further details.
||
image p532fig14.08 Macrocircuit of the ARTSCENE Search neural model for learning to search for desired objects by using the sequences of already experienced objects and their locations to predict what and where the desired object is. V1 = First visual area or primary visual cortex; V2 = Second visual area; V4 = Fourth visual area; PPC = Posterior Parietal Cortex; ITp = posterior InferoTemporal cortex; ITa = anterior InferoTemporal cortex; MTL = Medial Temporal Lobe; PHC = ParaHippoCampal cortex; PRC = PeriRhinal Cortex; PFC = PreFrontal Cortex; DLPFC = DorsoLateral PreFrontal Cortex; VPFC = Ventral PFC; SC = Superior Colliculus.
||
image p580fig16.05 Macrocircuit of the GridPlaceMap model, which can learn both 2D grid cells and place cells in response to realistic trajectories of navigating rats using a hierarchy of SOMs with identical equations.
|| GridPlaceMap model: rate-based and spiking (Pilly, Grossberg 2012). Pre-wired 1D stripe cells, learns both 2D frid and place cells! Same laws for both; both select most frequent and energetic inputs. Place cells emerge gradually in response to developing grid cells. [place-> grid-> stripe] cells-> path integration-> vestibular signals
image p599fig16.35 Data (a) and simulations (b,c) about anatomically overlapping grid cell modules. (a) shows the anatomical distribution of grid cells belonging to different modules in one animal. DV location (mm) vs postrhinal border. (b) shows the simulated distribution of learned grid cell spacings from two stripe cell scales. frequency (%) vs grid spacing (cm). mu = [1, 0.6]. (c) shows what happens when half the cells respond with one rate and half another rate. (d) shows the same with three rates. (e-g) show spatial maps and autocorrelograms of grid cells that arise from the different rates in (d). [rate map, autocorelogram] vs [score [1.07, 0.5, 0.67], spacing (cm) [23.58, 41, 63.64]].
||
image p612fig16.42 Macrocircuit of the main SOVEREIGN subsystems.
|| [reward input, drive input, drive representation (DR), visual working memory and planning system (VWMPS), visual form and motion system (VFMS), motor approach and orienting system (MAOS), visual input (VisIn), motor working memory and planning system (MWMPS), motor approach and orienting system (MAOS), motor plant (MotP), Proprioceptive Input (PropIn), Vestibular Input (VesIn), Environmental feedback (EnvFB). DR [incentive motivational learning-> [VWMPS, MWMPS], -> VFMS, -> MAOS], VWMPS [conditioned reinforcer learning-> DR, MAOS], VFMS [visual object categories-> VWMPS, reactive movement commands-> MAOS], MWMPS [conditioned reinforcer learning-> DR, planned movement commands-> MAOS], MAOS [motor map positions-> MWMPS, motor outflow-> MotP], VisIn-> VFMS, VesIn-> MAOS, EnvFB-> [VisIn, MotP, VesIn].
p040 Howell 24Jul2023, Grossberg 2021 p040c2h0.80 : It appears to me that the same [microcircuit, modal architecture] in the brain may be conscious in one situation (for example when producing results for simple decision-making), and non-conscious in another (when the outputs must be considered in the context of more complex, multiple [modes, environmental scenarios]?).

p190 Howell: [neural microcircuits, modal architectures] used in ART -

bottom-up filters	top-down expectations	purpose
instar learning	outstar learning	p200fig05.13 Expectations focus attention: [in, out]star often used to learn the adaptive weights. top-down expectations to select, amplify, and synchronize expected patterns of critical features, while suppressing unexpected features
LGN Lateral Geniculate Nucleus	V1 cortical area	p192fig05.06 focus attention on expected critical features
EC-III entorhinal stripe cells	CA1 hippocampal place cells	p600fig16.36 entorhinal-hippocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories
auditory orienting arousal	auditory category	p215fig05.28 How a mismatch between bottom-up and top-down patterns can trigger activation of the orienting system A and, with it, a burst of nonspecific arousal to the category level. ART Matching Rule: TD mismatch can suppress a part of F1 STM pattern, F2 is reset if degree of match < vigilance
auditory stream with/without [silence, noise] gaps	perceived sound continues?	p419fig12.17 The auditory continuity illusion: Backwards in time - How does a future sound let past sound continue through noise? Resonance!
visual perception, learning and recognition of visual object categories	motion perception, spatial representation and target tracking	p520fig14.02 pART predictive Adaptive Resonance Theory. Many adaptive synapses are bidirectional, thereby supporting synchronous resonant dynamics among multiple cortical regions. The output signals from the basal ganglia that regulate reinforcement learning and gating of multiple cortical areas are not shown. red - cognitive-emotional dynamics green - working memory dynamics black - see [bottom-up, top-down] lists
EEG with mismatch, arousal, and STM reset events	expected [P120, N200, P300] event-related potentials (ERPs)	p211fig05.21 Sequences of P120, N200, and P300 event-related potentials (ERPs) occur during oddball learning EEG experiments under conditions that ART predicted should occur during sequences of mismatch, arousal, and STM reset events, respectively.
Cognitive	Emotional	p541fig15.02 nSTART neurotrophic Spectrally Timed Adaptive Resonance Theory: Hippocampus can sustain a Cognitive-Emotional resonance: that can support "the feeling of what happens" and knowing what event caused that feeling. Hippocampus enables adaptively timed learning that can bridge a trace conditioning gap, or other temporal gap between Conditioned Stimuluus (CS) and Unconditioned Stimulus (US).

backgound colours in the table signify :

white	general microcircuit : a possible component of ART architecture
lime green	sensory perception [attention, expectation, learn]. Table includes [see, hear, !!must add touch example!!], no Grossberg [smell, taste] yet?
light blue	post-perceptual cognition?
pink	"the feeling of what happens" and knowing what event caused that feeling

CogEM Cognitive-Emotional-Motor model

grepStr = CogEM|Cognitive-Emotional-Motor

p484sec CogEM: Cognitive-Emotional-Motor model of conditioning
p487sec How CogEM explains attentional blocking
p489sec A polyvalent orbitofrontal processing stage helps CogEM to work properly
p490sec CogEM type circuits are an ancient design!
p491sec A thought experiment about cognitive-emotional interactions that leads to CogEM
image p025fig01.16 (left panel) The main processing stages of the Cognitive-Emotional-Motor (CogEM) model have anatomical interpretations in terms of sensory cortex, amygdala, and prefrontal cortex. Chapter 13 will describe in greater detail how CS cues activate invariant object categories in the sensory cortex, value categories in the amygdala, and object-value categories in the prefrontal cortex, notably the orbitofrontal cortex. The amygdala is also modulated by internal drive inputs like hunger and satiety. (right panel) Anatomical data support this circuit, as do many neurophysiological data.
|| drive -> amygdala -> prefrontal cortex <-> sensory cortex -> amygdala. [visual, somatosensory, auditory, gustatory, olfactory] cortex -> [amygdala, Orbital Prefrontal Cortex]. amygdala -> Lateral Prefrontal Cortex
image p481fig13.01 Macrocircuit of the functional stages and anatomical interpretations of the Cognitive-Emotional-Motor, or CogEM, model.
|| Drive-> hypothalamus value categories <-> amygdala incentive motivational learning-> Orbitofrontal cortex- object-value categories <-> sensory cortex- invariant object categories- conditioned reinforcer learning-> amygdala-> hypothalamus.
image p483fig13.02 The object-value categories in the orbitofrontal cortex require converging specific inputs from the sensory cortex and nonspecific incentive motivational inputs from the amygdala in order to fire. When the orbitofrontal cortex fires, it can deliver top-down ART Matching Rule priming signals to the sensory cortical area by which it was activated, thereby helping to choose the active recognition categories there that have the most emotional support, while suppressing others, leading to attentional blocking of irrelevant cues.
|| Cognitive-Emotional-Motor (CogEM) model. Drive-> amygdala incentive motivational learning-> orbitofrontal cortex- need converging cue and incentive iputs to fire <-> sensory cortex- conditioned renforcer learning-> amygdala. CS-> sensory cortex. Motivated attention closes the cognitive-emotional feedback loop, focuses on relevant cues, and causes blocking of irrelevant cues.
image p483fig13.03 The predicted processing stages of CogEM have been supported by anatomical studies of connections between sensory cortices, amygdala, and orbitofrontal cortex.
|| Adapted from (Barbas 1995). sensory cortices = [visual, somatosensory, auditory, gustatory, olfactory]. sensory cortices-> amygdala-> orbital prefrontal cortex. sensory cortices-> orbital prefrontal cortex. [visual cortex, amygdala]-> lateral prefrontal cortex.
image p484fig13.04 The top-down feedback from the orbitofrontal cortex closes a feedback loop that supports a cognitive-emotional resonance. If this resonance can be sustained long enough, it enables us to have feelings at the same time that we experience the categories that caused them.
|| Cognitive-Emotional resonance. Basis of "core consciousness" and "the feeling of what happens". (Damasio 1999) derives heuristic version of CogEM model from his clinical data. Drive-> amygdala-> prefrontal cortex-> sensory cortex, resonance around the latter 3. How is this resonance maintained long enough to become conscious?
image p487fig13.11 The three main properties of CogEM that help to explain how attentional blocking occurs.
|| CogEM explanation of attentional blocking. Internal drive input <-> Conditioned reinforcer learning (self-recurrent) <-> Competition for STM <- Motor learning. 1. Sensory representations compete for limited capacity STM. 2. Previously reinforced cues amplify their STM via positive feedback. 3. Other dues lose STM via competition.
image p489fig13.13 (top row) If a positive ISI separates onset of a CS and US, then the CS can sample the consequences of the US during the time interval before it is inhibited by it. (bottom row) A CogEM simulation of the inverted-U in conditioning as a function of the ISI betweeen CS and US.
|| Positive ISI and conditioning.
image p490fig13.15 The CogEM circuit is an ancient design that is found even in mollusks like Aplysia. See the text for details.
|| Aplysia (Buononamo, Baxter, Byrne, Neural Networks 1990; Grossberg, Behavioral and Brain Sciences 1983). Facilitator neuron ~ drive representation.
image p494fig13.19 (left column, top row) Secondary conditioning of both arousal and a specific response are now possible. (bottom row) The CogEM circuit may be naturally extended to include multiple drive representations and inputs. (right column, top row) The incentive motivational pathways is also conditionable in order to enable motivational sets to be learned.
|| Secondary conditioning. Homology: conditionable incentive motivation. Multiple drive representations and inputs.
image p514fig13.44 Analog of the COgEM model in Figure 6.1 of (Damasio 1999).
|| (a) map of object X-> map of proto-self at inaugural instant-> [, map of proto-self modified]-> assembly of second-order map. (b) map of object X enhanced-> second-order map imaged.
image p523fig14.03 (a) The MOTIVATOR neural model generalizes CogEM by also including the basal ganglia. It can hereby explain and simulate complementary functions of the amygdala and basal ganglia (SNc) during conditioning and learned performance. The basal ganglia generate Now Print signals in response to unexpected rewards. These signals modulate learning of new associations in many brain regions. The amygdala supports motivated attention to trigger actions that are expected to occur in response to conditioned or unconditioned stimuli. Object Categories represent visual or gustatory inputs in anterior inferotemporal (ITA) and rhinal (RHIN) cortices, respectively. Value Categories represent the value of anticipated outcomes on the basis of hunger and satiety inputs, in amygdala (AMYG) and lateral hypothalamus (LH). Object-Value Categories resolve the value of competing perceptual stimuli in medial (MORB) and lateral (ORB) orbitofrontal cortex. The Reward Expectation Filter detects the omission or delivery of rewards using a circuit that spans ventral striatum (VS), ventral pallidum (VP), striosomal delay (SD) cells in the ventral striatum, the pedunculopontine nucleus (PPTN) and midbrain dopaminergic neurons of the substantia nigra pars compacta/ventral tegmental area (SNc/VTA). The circuit that processes CS-related visual information (ITA, AMTG, ORB) operates in parallel with a circuit that processes US-related visual and gustatory information (RHIN, AMYG, MORB). (b) Reciprocal adaptive connections between hypothalamus and amygdala enable amygdala cells to become learned value categories. The bottom region represents hypothalmic cells, which receive converging taste and metabolite inputs whereby they become taste-drive cells. Bottom-up signals from activity patterns across these cells activate competing value category, or US Value Representations, in the amygdala. A winning value category learns to respond selectively to specific combinations of taste-drive activity patterns and sends adaptive top-down priming signals back to the taste-drive cells that activated it. CS-activated conditioned reinforcer signals are also associatively linked to value categories. Adpative connections end in (approximately) hemidiscs. See the text for details.
||
image p548fig15.16 Homologous recognition learning and reinforcement learning macrocicuits enable adaptively timed conditioning in the reinforcement learning circuit to increase inhibition of the orienting system at times when a mismatch in the recognition system would have reduced inhibition of it.
|| Homolog between ART and CogEM model, complementary systems. [Recognition, Reinforcement] learning vs [Attentional, Orienting] system. Reinforcement: timing, drive representation.
Grossberg 2021 p048c2h0.90 Perhaps the theory of the highly influential Portugese-American neuroscientist and author, Antonio Damasio, comes closest to theoretically linking brain to mind in his beautifully written 1999 book with the title The Feeling of What Happens: Body and Emotion in the making of consciousness (Damasio 1999). ... Damasio used the cliical data that he elegantly summarized in his book to guide him to what is, in effect, a heuristic derivation of the Cognitive-Emotional-Motor, or CogEM, model that I had first published in 1991. ... Unlike CogEM and its refinements, Damasio's theory provides no mechanistic account, and could therefore provide no data simulations or predictions based on the model's emergent properties. Nor could he situate his heuristic concepts within a larger theory of how brain resonances and conciousness may be linked.
p523fig14.03 Howell: Reminds me of Asim Roy, Arizona State University: posted comments on Connectionists over 2-5 years(?) regarding [concept, grandmother] cells, or the question if a neuron could represent a concept, as opposed to a "holographic law" that would prevent that. Interesting argument exchanges.
Grossberg: (a) The MOTIVATOR neural model generalizes CogEM ... Reciprocal adaptive connections btween hypothalamus and amygdala enable amygdala cells to become learned value categories. ...

[intra, inter]-cellular process

grepStr = intracellular|extracellular

image p080fig02.33 An opposite-attracts rule during the development of intracellular connections can lead to a mature network that realizes informational noise suppression.
|| How do noise suppression parameters arise? Symmetry-breaking during morphogenesis? Opposites attract rule.
Intracellular parameters C/B = 1/(1 - n) Intercellular parameters
Predicts that:
- Intracellular excitatory and inhibitory saturation points can control the growth during development of :
- Intercellular excitatory and inhibitory connections.

informational noise suppression

grepStr = noise suppression|rate spectra

p012sec Sigmoid signals enable noise suppression without winner-take-all choice
p079sec Informational noise suppression and bottom-up/top-down pattern matching
p079sec How does informational noise suppression arise during development? Opposites attrract?
p080sec From a global off-surround to a distance-dependent one that preserves noise suppression
p509sec Informational noise suppression prevents LTM saturation
p591sec Does a calcium gradient enable mGluR to generate all rate spectra?
image p012fig01.08 A sigmoidal signal function is a hybrid signal that combines the best properties of [faster, same, slower]-than linear signals. It can suppress noise and store a partially contrast-enhanced activity pattern. slower-than-linear saturates pattern; approximately linear- preserves pattern and normalizes; faster-than-linear- noise suppression and contrast-enhancement.
|| Sigmoidal signal: a hybrid. (upper) saturates pattern- slower-than-linear; (middle) preserves pattern and normalizes- approximately linear. (lower) noise suppression and contrast enhancement- faster-than-linear.
image p078fig02.30 Choosing the adaptation level to achieve informational noise suppression.
|| Noise suppression. Attenuate Zero Spatial frequency patterns: no information. Ii vs i (flat line), xi vs i (flat line at zero)
B >> C: Try B = (n - 1)*C or C/(B + C) = 1/n
Choose a uniform input pattern (no distinctive features): All θi = 1/n
xi = (B + C)*I/(A + I)*[θi -C/(B + C)] = 0 no matter how intense I is.
image p078fig02.31 How noise suppression enables matching of bottom-up and top-down input patterns.
|| Noise suppression -> pattern matching. mismatch (out of phase) suppressed, match (in phase) amplifies pattern.
image p080fig02.33 An opposite-attracts rule during the development of intracellular connections can lead to a mature network that realizes informational noise suppression.
|| How do noise suppression parameters arise? Symmetry-breaking during morphogenesis? Opposites attract rule.
Intracellular parameters C/B = 1/(1 - n) Intercellular parameters
Predicts that:
- Intracellular excitatory and inhibitory saturation points can control the growth during development of :
- Intercellular excitatory and inhibitory connections.
image p080fig02.34 How to achieve informational noise suppression in a network with multiple parallel processing channels.
|| Symmetry-breaking: dynamics and anatomy.
Dynamics:
- excitatory range is amplified
- inhibitory range is compressed
Anatomy:
- narrow on-center
- broad off-surround
Noise suppression: attenuates uniform patterns
Contour direction: enhances pattern gradients
image p081fig02.36 Informational noise suppression in network with Gaussian on-center and off-surround function as contour detectors that are sensitive to ratio-contrast.
|| Noise suppression and contour detection.
If B*sum[k=1 to n: Cki] <= D*sum[k=1 to n: Eki] then:
- uniform patterns are suppressed
- contrasts are selectively enhanced
- contours are detected
Ii vs i, xi vs i
Responses are selective to [REFLECTANCE, SPATIAL SCALE], eg color [feature, surface] contours.
image p510fig13.39 Shunting competition and informational noise suppression in affective gated dipoles, plus back-propagating action potentials for teaching signals, enable the net normalized adaptive weights to be learned. They never saturate!
|| Learn net dipole output pattern. Opponent "decision" controls learning. Cf. competitive learning. Learning signal, opponent extinction.
p079 Howell: grepStr 'noise suppression|rate spectra' - Gerald Pollack: likes attract likes, speaking of ions (effect of inter-layer)
p591 Howell: grepStr 'noise suppression|rate spectra' - Gerald Pollack: cell membranes are NOT needed for ionic differences [in, out]side of cells, it's possibly gel phase of water?! (4th phase after [solid, liquid, gas], ignoring plasma)

[stable, robust, adaptive] learning

on-center off-surround

grepStr = on-center|off-surround|on center|off surround

p074sec On-center off-surround anatomies can compute inpute ratios
p074sec Shunting on-center off-surround networks solve the noise saturation dilemma
p076sec Shift property and shunting on-center off-surround network in the mudpuppy retina
p080sec From a global off-surround to a distance-dependent one that preserves noise suppression
p247sec Recurrent off-surround normalizes cell responses to converging sources
p359sec Layer 6-to-4 on-center is modulatory
p439sec Item and order working memories are recurrent shunting on-center off-surround networks
image p009fig01.06 Primacy gradient of activity stored in working memory within a recurrent shunting on-center off-surround network. Rehersal is controlled by a nonspecific rehersal wave and self-inhibitory feedback of the item that is currently being rehearsed. Rehearsal is controlled by a nonspecific rehearsal wave and self-inhibitory feedback of the item that is currently being rehearsed. Green = excitatory, red = inhibitory
|| inputs? -> item and order WM storage -> competitive selection-> rehearsal wave -> outputs
image p024fig01.15 A REcurrent Associative Dipole, or READ, circuit is a recurrent shunting on-center off-surround network with habituative transmitter gates. Sensory cues sample it with LTM traces and thereby become conditioned reinforcers.
||
image p038fig01.25 The ART Matching Rule stabilizes real time learning using a [top-down, modulatory on-center, off-surround] network. Object attention is realized by such a network. See text for additional discussion.
|| ART Matching Rule [volition, categories, features]. [one, two] against one.
image p073fig02.22 An on-center off-surround network is capable of computing input ratios.
|| Computing with patterns.
How to compute the pattern-sensitive variable: θi = Ii / sum[k=1 to n: Ik]?
Needs interactions! What type? θi = Ii / sum[k ≠ i: Ik]
Ii↑ ⇒ θi↑ excitation, Ik↑ ⇒ θk↓, k ≠ i inhibition
On-center off-surround network.

Mass action:	d[dt: xi] =	-A*xi	+(B - xi)*Ii	-xi*sum[k≠i: Ik]
			Turn on unexcited sites	Turn off excited sites

At equilibrium:
0 = d[dt: xi] = -(A + Ii + sum[k≠i: Ik])*xi + B*Ii = -(A + I)*xi + B*Ii

xi = BIi/(A + I) = BθiI/(A + I) = θi B*I/(A + I)	No saturation! Infinite dynamical range Automatic gain control Compute ratio scale Weber law
x = sum[k-1 to n: xk] = B*I/(A + I) ≤ B	Conserve total activity NORMALIZATION Limited capacty Real-time probability

image p075fig02.24 The membrane equations of neurophysiology describe how cell voltages change in response to excitatory, inhibitory, and passive input channels. Each channel is described by a potential difference multiplied by a conductance. With the special choices shown in the lower right-hand corner, this equation defines a feedforward shuntin on-center off-surround network.
|| Membrane equations of neurophysiology.
C*dp[dt] = (V(+) - V)*g(+) +(V(-) - V)*g(-) +(V(p) - V)*g(p)
Shunting equation (not additive)
V Voltage
V(+), V(-), V(p) Saturating voltages
g(+), g(-), g(p) Conductances
V(+) = B, C = 1; V(-) = V(p) = 0; g(+) = Ii; g(-) = sum[k≠i: Ik];
lower V: V(+) = V(p) Silent inhibition, upper V: V(+). (Howell: see p068fig02.14 Grossberg's comment that Hodgkin&Huxley model was a "... Precursor of Shunting network model (Rail 1962) ...").
image p076fig02.25 An on-center off-surround network can respond to increasing on-center excitatory inputs without a loss of sensitivity. Instead, as the off-surround input increases, the region of a cell's maximal sensitivity to an increasing on-center input shifts to a range of larger inputs. This is because the off-surround divides the effect of the on-center input, an effect that is often called a Weber law.
|| Web er law, adaptation, and shift property (Grossberg 1963).
Convert to logarithmic coordinates:
K = ln(Ii), Ii = e^K, J = sum[k≠i: Ik]
xi(K,J) = B*Ii/(A + Ii + J) = B*e^K/(A + e^K + J)
x(K + S, J1) = x(K, J2), S = ln((A + J1)/(A + J2)) size of SHIFT.
image p076fig02.26 The mudpuppy retina exhibits the shift property that occurs in the feedforward shunting on-center off-surround network in Figure 2.25. As a result, its sensitivity also shifts in response to different background off-surrounds, and therefore exhibits no compression (dashed purple lines).
|| Mudpuppy retina neurophysiology.
I center, J background
a) Relative figure-to-ground
b) Weber-Fechner I*(A + J)^(-I)
c) No hyperpolarization, SHUNT: Silent inhibition
d) Shift property(Werblin 1970) xi(K,J) vs K = ln(I)
Adaptation- sensitivity shifts for different backgrounds. NO COMPRESSION.
image p077fig02.27 A schematic of the on-center off-surround network that occurs in the mudpuppy retina, including three main cell types: receptors, horizontal cells, and bipolar cells.
|| Mechanism: cooperative-competitive dynamics.
On-center off-surround (Kuffler 1953) cat retina
Subtractive lateral inhibition (Hartline, Ratcliff 1956/7+) limulus retina.
R receptor -> H horizontal -> B bipolar (Werblin, Dowling, etal 1969+) mudpuppy retina.
image p080fig02.34 How to achieve informational noise suppression in a network with multiple parallel processing channels.
|| Symmetry-breaking: dynamics and anatomy.
Dynamics:
- excitatory range is amplified
- inhibitory range is compressed
Anatomy:
- narrow on-center
- broad off-surround
Noise suppression: attenuates uniform patterns
Contour direction: enhances pattern gradients
image p081fig02.35 The equilibrium activities of a shunting netwok with Gaussian on-center off-surround kernels are sensitive to the ratio-contrasts of the input patterns that they process. The terms in the denominator of the equilibrium activities accomplish this using the shunting on-center and off-surround terms.
|| Ratio-contrast detector. flat versus [Gaussian Cki, flattened Gaussian? Eki]
d[dt: xi] = -A*xi +(B - xi)*sum[k≠i: Ik]*Cki -(xi + D)*sum[k=1 to n: Ik*Eki]
Cki = C*e^(-μ*(k - i)^2), Eki = E*e^(-μ*(k - i)^2)
At equilibrium: xi = I*sum[k=1 to n: θk*Fki] / (A + I*sum[k=1 to n: θk*Gki])
Fki = B*Cki -D*Eki (weighted D.O.G)
Gki = Cki +Eki (S,O,G)
- Reflectance processing
- Contrast normalization
- Discount illuminant
image p081fig02.36 Informational noise suppression in network with Gaussian on-center and off-surround function as contour detectors that are sensitive to ratio-contrast.
|| Noise suppression and contour detection.
If B*sum[k=1 to n: Cki] <= D*sum[k=1 to n: Eki] then:
- uniform patterns are suppressed
- contrasts are selectively enhanced
- contours are detected
Ii vs i, xi vs i
Responses are selective to [REFLECTANCE, SPATIAL SCALE], eg color [feature, surface] contours.

image p106fig03.24 In response to the Synthetic Aperture image (upper corner left), a shunting on-center off-surround network "discounts the illiminant" and thereby normalizes cell activities to compute feature contours, without causing saturation (upper right corner). Multiple-scale boundaries form in response to spatially coherent activities in the feature contours (lower left corner) and create the webs, or containers, into which the feature contours fill-in the final surface representations (lower right corner).
|| Do these ideas work on hard problems? SAR!

input image	feature contours	boundary contours	filled-in surface
Synthetic Aperture Radar: sees through weather 5 orders of magnitude of power in radar return	discounting the illuminant normalizes the image: preseves RELATIVE activities without SATURATION shows individual PIXELS	boundaries complete between regions where normalized feature contrasts change	filling-in averages brightnesses within boundary compartments

image p176fig04.53 The on-center off-surround network within position and across depth helps to explain why brighter Kanizsa squares look closer.
|| inhibition vs. depth. p176c1h0.25 "... to qualitatively understand how this example of proximity-luminance covariance works. It follows directly from the boundary pruning by surface contour feedback signals (Figure 4.51) that achieves complementary consistency and initiates figure-ground perception. ...". p176c1h0.45 "... these inhibitory sigals are part of an off-surround network whose strength decreases as the depth difference increases between the surface that generates the signal and its recipient boundaries. ...". p176c1h0.8 "... Within FACADE theory, the perceived depth of a surface is controlled by the boundaries that act as its filling-in generators and barriers (Figure 3.22), since these boundaries select the depth-sselective FIDOs within whin filling-in can occur, and thereby achieve surface capture. These boundaries, in turn, are themselves strengthened after surface-to-boundary contour feedback eliminates redundant boundaries that cannot support sucessful filling-in (Figure 4.51). These surface contour feedback signals have precisely the properties that are needed to explain why brighter Kanizsa squares look closer! ..."
image p192fig05.05 ON and OFF cells in the LGN respond differently to the sides and ends of lines.
|| [ON, OFF]-center, [OFF, ON]-surround (respectively). OFF-center cells maximum response at line end (interior), ON-center cells maximum response along sides (exterior)
image p253fig06.02 After bottom-up surface inputs activate spatial attentional cells, they send top-down topographic excitatory signals back to the surface representations. This recurrent shunting on-center off-surround network contrast enhances larger attentional activities while approximately normalizing the total spatial attentional activity. A surface-shroud resonance hereby forms that selects an attentional shroud, enhances the perceived contrast of the attended surface (light blue region), and maintains spatial attention on it.
|| Surface-shroud resonance. perceptual surfaces -> competition -> spatial attention. (Carrasco, Penpeci-Talgar, and Eckstein 2000, Reynolds and Desimone 2003)
image p300fig08.12 A single flash activates a Gaussian receptive field across space whose maximum is chosen by a winner-take-all recurrent on-center off-surround network.
|| Gaussian receptive fields are sufficient! (Grossberg, Rudd 1992). Single flash. Suppose that a single flash causes a narrow peak of activity at the position where it occurs. It generates output signals through a Gaussian filter that produces a Gaussian activity profile at the next processing stage., A recurrent on-center off-surround network chooses the maximum activity and suppresses samaller activities. Winner-take-all
image p315fig08.35 The output signals from the directional grouping network obey the ART Matching Rule. They thereby select consistent motion directional signals while suppressing inconsistent ones, and do not distort that the spared cells code. The aperture problem is hereby solved by the same mechanism that dynamically stabilizes the learning of directional grouping cells.
|| How to select correct direction and preserve speed estimates? Prediction: Feedback from MSTv to MT- obeys ART Matching Rule; Top-down, modulatory on-center, off-surround network (Grossberg 1976, 1980; Carpenter, Grossberg 1987, 1991); Explains how directional grouping network can stably develop and how top-down directional attention can work. (Cavanough 1992; Goner etal 1986; Sekuer, Ball 1977; Stelmach etal 1994). Directional grouping network (MSTv) <-> Directional long-range filter (MT). Modulatory on-center selects chosen direction and preserves speed. Off-surround inhibits incompatible directions.
image p340fig09.07 Log polar remapping from the retina to cortical area V1 and beyond converts expansion, translation, and spiral flows on the retina into parallel flows, with different orientations, on the cortical map.
|| Log polar remapping of optic flow. retina -> cortex. Any combination of expansion and circular motion centered on the fovea maps to cortex as a single direction. Retinal Cartesian coordinates (x,y) map to cortical polar coordinates (r,theta). This makes it easy to compute directional receptive fields in the cortex!
image p345fig09.15 Double opponent directional receptive fields in MT are capable of detecting the motion of objects relative to each other and their backgrounds.
|| Motion opponency in MT (Born, Tootell 1992). Motion opponent (Grossberg etal), Differential motion (Royden etal), Subtractive motion cells (Neumann etal). ON center directionally selective: [excit, inhibit]ed by motion in [one, opponent] direction. OFF surround directionally selective: [excit, inhibit]ed by motion in [opponent, center] direction.
image p354fig10.01 The lamiar cortical circuit that realizes how we pay attention to an object sends signals from layer 6 of a higher cortical level to layer 6 of a lower cortical level and then back up to layer 4. This "folded feedback" circuit realizes a top-down, modulatory on-center, off-surround circuit that realizes the ART Matching Rule.
|| Top-down attention and folded feedback. Attentional signals also feed back into 6-to-4 on-center off-surround. 1-to-5-to-6 feedback path: Macaque (Lund, Booth 1975) cat (Gilbert, Wiesel 1979). V2-to-V1 feedback is on-center off-surround and affects layer 6 of V1 the most (Bullier etal 1996; Sandell, Schiller 1982). Attended stimuli enhanced, ignored stimuli suppressed. This circuit supports the predicted ART Matching Rule! [LGN, V[1,2][6->1]]
image p359fig10.06 Another, albeit indirect, pathway from LGN exists that can also excite layer 4 of V1. Why are not these two pathways redundant? The answer, ultimately, how to do with how cortex learns, as well as with how it pays attention. See the text for details.
|| Another bottom-up input to layer 4: Why?? Layer 6-to-4 on-center off-surround (Grieve, Sillito 1991, 1995; Ahmedetal 1994, 1997). LGN projects to layers 6 and 4. Layer 6 excites spiny stellates in column above it. Medium range connections onto inhibitory neurons. 6-t-4 path acts as on-center off-curround.
image p359fig10.07 The two bottom-up pathways from LGN to layer 4 of V1 can together activate layer 4 and contrast-normalize layer 4 responses.
|| Bottom-up contrast normalization (Grossberg 1968, 1973; Sperling, Sondhi 1968; Heeger 1992; Douglas etal 1995; Shapley etal 2004). Together, direct LGN-to-4 path and 6-to-4 on-center off-surround provide contrast normalization if cells obey shunting or membrane equation dynamics.
image p360fig10.08 The bottom-up on-center off-surround from LGN-to-6-to-4 has a modulatory on-center because of its role in realizing the ART Matching Rule and, with it, the ability of the cortex to dynamically stabilize its learned memories.
|| Modulation of priming by 6-to-4 on-center (Stratford etal 1996; Callaway 1998). On-center 6-to-4 excitation is inhibited down to being modulatory (priming, subthreshold). On-center 6-to-4 excitation cannot activate layer 4 on its own. Clarifies need for direct path. Prediction: plays key role in stable grouping, development and learning. ART Matching Rule!
image p362fig10.11 Feedback between layer 2/3 to the layer 6-to-4-to-2/3 feedback loop chooses the strongest grouping in cases where there is more than one. If only one grouping exists, then the circuit can function very quickly in a feedforward manner. When multiple groupings exist, the cortex "runs as fast as it can" to select the one with the most evidence to support it using the self-normalizing inhibition in the layer 6-to-4 off-surround.
|| How is the final grouping selected? Folded feedback LGN-> 6-> 4-> 2/3. 1. Layer 2/3 groupings feed back into 6-to-4 on-center off-surround: a) direct layer 2/3 -to-6 path; b) can also go via layer 5 (Blasdel etal 1985; Kisvarday etal 1989). 2. Strongest grouping enhanced by its on-center. 3. Inputs to weaker groupings suppressed by off-surround. 4. Interlaminar feedback creates functional columns. Activities of conflicting groupings are reduced by self-normalizing inhibition, slowing processing; intracortical feedback selects and contrast-enhances the winning grouping, speeding processing.
image p364fig10.13 The bottom-up adaptive filter, intracortical grouping circuit, and intercortical top-down attentional circuit all use the same competitive decision circuit between layers 6 and 4, called the attention-preattention interface, with which to select the featural patterns that will be processed.
|| Bottom-up filters and intracortical grouping feedback use the same 6-to-4 decision circuit, LGN-> Vx[6,4,2/3]. competitive decision circuit, modulatory on-center off-surround network. Top-down intercortical attention also uses the same 6-to-4 decision circuit!
image p364fig10.14 This figure emphasizes how preattentive intracortical groupings and top-down intercortical attention share the same modulatory on-center, off-surround layer 4-to-6 decision circuit.
|| Explanation: grouping and attention share the same modulatory decision circuit. Layer 6-6-4-2/3 pathway shown; also a layer 6-1-2/3 path. intercortical attention, both act via a modulatory on-center off-surround decision circuit, intracortical feedback from groupings
image p367fig10.15 Data (left column) and simulation (right column) of how attention prevents a masking stimulus from inhibiting the response to the on-center of the cell from which the recording was made.
|| Attention protects target from masking stimulus (Reynolds etal 1999; Grossberg, Raizada 2000).
image p375fig11.07 The 3D LAMINART model uses both monocular and binocular simple cells to binocularly fuse like image contrasts. The remainder of the model generates 3D boundary and surface representations of multiple kinds of experiments as well as of natural scenes.
|| 3D LAMINART model (Cao, Grossberg 2005). [left, right] eye cart [LGN, V1 blob [4->3B->2/3A], V2 thin stripe [4->2/3A], V4]. V1 blob [V1-4 monocular, V1 interior binocular] simple cells. [complex, simple, inhibitory] cells, on-center off-surround
image p376fig11.10 The 3D LAMINART model shows how the disparity filter can be integrated into the circuit that completes 3D boundary representations using bipole grouping cells. It also explains how surface contours can strengthen boundaries that succeed in generating closed filling-in domains.
|| 3D LAMINART model (Cao, Grossberg 2005). [left, right] eye cart [LGN, V1 blob [4->3B->2/3A] surface contour, V2 thin stripe (monocular surface) [4->2/3A], V2 interior [disynaptic inhibitory interneurons, bipole grouping cells, disparity filter, V4 binocular surface]. [complex, simple, inhibitory] cells, on-center off-surround
image p423fig12.20 The Spatial Pitch Network, or SPINET, model shows how a log polar spatial representation of the sound frequency spectrum can be derived from auditory signals occuring in time. The spatial representation allows the ARTSTREAM model to compute spatially distinct auditory streams.
|| SPINET model (Spatial Pitch Network) (Cohen, Grossberg, Wyse 1995). 1. input sound 2. Gamma-tone filter bank 3. Shaort-term average energy spectrum 4. MAP transfer function 5. On-center off-surround and rectification 6. Harmonic weighting 7. Harmonic summation and competition -> PITCH
image p448fig12.46 A Masking Field working memory is a multiple-scale self-similar recurrent shunting on-center off-surround network. It can learn list chunks that respond selectively to lists of item chunks of variable length that are stored in an item working memory at the previous processing stage. Chunks that code for longer lists (eg MY vs MYSELF) are larger, and give rise to stronger recurrent inhibitory neurons (red arrows).
|| How to code variable length lists? MASKING FIELDS code list chunks of variable length (Cohen, Grossberg 1986, 1987; Grossberg, Kazerounian 2011, 2016; Grossberg, Meyers 2000; Grossberg, Pearson 2008). Multiple-scale self-similar WM: Masking field, adaptive filter. Variable length coding- Masjking fields select list chunks that are sensitive to WM sequences of variable length; Selectivity- Larger cells selectively code code longer lists; Assymetric competition- Larger cells can inhibit smaller cells more than conversely MAgic Number 7! Temporal order- different list chunks respond to the same items in different orders eg LEFT vs FELT;.
image p564fig15.35 (a) A pair of recurrent shunting on-center off-surround networks for control of the fore limbs and hind limbs. (b) Varying the GO signal to these networks can trigger changes in movement gaits. See the text for details.
||
image p567fig15.38 (a) The Gated Pacemaker model for the control of circadian rythms is a recurrent shunting on-center off-surround network whose excitatory feedback signals are gated by habituative transmitters. Tonic arousal signals energize the pacemaker. Diurnal (left) and nocturnal (right) pacemakers are determined by whether phasic light signals turn the pacemaker on or off. An activity-dependent fatigue signal prevents the pacemaker from becoming overly active for too long. (b) Two simulations of circadian activity cycles during different schedules of light (L) and dark (D). See the text for details.
|| sourceOn-> on-cells (recurrent) <-(-) (-)> off-cells (recurrent) <-sourceOff. on-cells-> activity-> off-cells. off-cells-> fatigue. Diurnal: sourceOn=[light, arousal]; sourceOff=arousal;. Nocturnal: sourceOn=arousal; sourceOff=[arousal, light];.
image p586fig16.16 In the place cell learning model of (Gorchetnikov, Grossberg 2007), three populations of five cells each of entorhinal grid cells (only two are shown) with different spatial periods input to the model's dentate gyrus. The grid cells are one-dimensional and defined algorithmically. A model dentate gyrus granule cell that receives strong projections from all three grid cell scales fires (green cell) and activates a recurrent inhibitory interneuron that inhibits other granule cells. It also generates back-propagating action potentials that trigger learning in the adaptive weights of the projections from the grid cells, thereby causing learning of place cell receptive fields.
|| Grid-to-place Self-Organizing map (Gorchetnikov, Grossberg 2007). Formation of place cell fields via grid-to-place cell learning. Least common multiple: [grid (cm), place (m)] scales: [40, 50, 60 (cm); 6m], [50, 60, 70 (cm); 21m], [41, 53, 59 (cm); 1.282 km]. Our simulations: [40, 50 (cm); 2m], [44, 52 (cm); 5.72m]. Our SOM: Spiking Hodgkin-Huxley membrane equations; Nonlinear choice by contrast-enhancing recurrent on-center off-surround net;. Choice triggers back-propagating action potentials that induce STDP-modulated learning on cell dendrites.
image p627tbl17.01 Homologs between reaction-diffusion and recurrent shunting cellular network models of development.
|| byRows: (reaction-diffusion, recurrent shunting net) (activator, excitatory activity) (inhibitor, inhibitory activity) (morphogenic source density, inputs) (firing of morphogen gradient, contrast enhancement) (maintenance of morphogen gradient, short-term memory) (power or sigmoidal signal functions, power or sigmoidal signal functions) (on-center off-surround interactions via diffusion, on-center off-surround interactions via signals) (self-stabilizing distributions of morphogens if inhibitors equilibrate rapidly, short-term memory pattern if inhibitors equilibrate rapidly) (periodic pulses if inhibitors equilibrate slowly, periodic pulses if inhibitors equilibrate slowly) (regulation, adaptation).
p362fig10.11 Howell: ?comment or question?
Grossberg: Feedback between layer 2/3 to the layer 6-to-4-to-2/3 feedback loop chooses the strongest grouping in cases where there is more than one. If only one grouping exists, then the circuit can function very quickly in a feedforward manner. When multiple groupings exist, the cortex "runs as fast as it can" to select the one with the most evidence to support it using the self-normalizing inhibition in the layer 6-to-4 off-surround.
p586fig16.16 Howell: note use of term "back-propagating", albeit perhaps somewhat different from standard back-propagation in ANNs.
Grossberg: In the place cell learning model of (Gorchetnikov, Grossberg 2007), three populations of five cells each of entorhinal grid cells (only two are shown) with different spatial periods input to the model's dentate gyrus. The grid cells are one-dimensional and defined algorithmically. A model dentate gyrus granule cell that receives strong projections from all three grid cell scales fires (green cell) and activates a recurrent inhibitory interneuron that inhibits other granule cells. It also generates back-propagating action potentials that trigger learning in the adaptive weights of the projections from the grid cells, thereby causing learning of place cell receptive fields. || Grid-to-place Self-Organizing map (Gorchetnikov, Grossberg 2007). Formation of place cell fields via grid-to-place cell learning. Least common multiple: [grid (cm), place (m)] scales: [40, 50, 60 (cm); 6m], [50, 60, 70 (cm); 21m], [41, 53, 59 (cm); 1.282 km]. Our simulations: [40, 50 (cm); 2m], [44, 52 (cm); 5.72m]. Our SOM: Spiking Hodgkin-Huxley membrane equations; Nonlinear choice by contrast-enhancing recurrent on-center off-surround net;. Choice triggers back-propagating action potentials that induce STDP-modulated learning on cell dendrites.

top-down bottom-up

grepStr = top-down|bottom-up|top down|bottom up

p006sec Balancing expected vs unexpected events: top-down vs bottom-up attention
p079sec Informational noise suppression and bottom-up/top-down pattern matching
p190sec Top-down priming
p196sec Bottom-up automatic attention?
p196sec Top-down task-selective attention and memory search for new categories
p198sec Instar bottom-up category learning and outstar top-down expectation learning
p205sec Hypothesis testing using top-down learned expectations
p213sec Why learning of top-down expectations is necessary
p225sec Catastrophic forgetting without the top-down ART Matching Rule
p265sec Visual imagery: Basal ganglia volition converts top-down modulation to driving inputs
p313sec Solving the Aperture Problem: Cycling bottom-up motion grouping and top-down priming
p356sec pre-attentive automatic bottom-up processing
p356sec attentive task-selective top-down processing
p358sec Analog sensitivity to bottom-up sensory inputs
p363sec Top-down feedback from V1 to LGN
p451sec Top-down attentive matching during speech and laguage using the ART Matching Rule
p530sec Visual search: Efficient vs. inefficient, bottom-up vs. top-down
p609sec Continuous attractor with top-down uniform positive hippocampal-to-entorhinal feedback
image p016fig01.11 A sufficiently big mismatch between a bottom-up input pattern and a top-down expectation can activate the orienting system, which triggers a burst of nonspecific arousal that can reset the recognition category that read out the expectation. In this way, unexpected events can reset short-term memory and initiate a search for a category that better represents the current situation.
|| [category- top-down (TD) expectation; Bottom-up (BU) input pattern] -> Feature pattern -> BU-TD mismatch -> orienting system -> non-specific arousal -> category.
image p038fig01.25 The ART Matching Rule stabilizes real time learning using a [top-down, modulatory on-center, off-surround] network. Object attention is realized by such a network. See text for additional discussion.
|| ART Matching Rule [volition, categories, features]. [one, two] against one.
image p052fig02.02 Feature-category resonances enable us to rapidly learn how to recognize objects without experiencing catastrophic forgetting. Attentive matching between bottom-up feature pattern inputs and top-down expectations prevent catastrophic forgetting by focussing object attention upon expected patterns of features, while suppressing outlier features that might otherwise have caused catastophic forgetting if they were learned also.
|| Adaptive Resonance. Attended feature clusters reactivate bottom-up pathways. Activated categories reactivate their top-down pathways. Categories STM, Feature patterns STM. Feature-Category resonance [synchronize, amplify, prolong]s system response. Resonance triggers learning in bottom-up and top-down adaptive weights: adaptive resonance!
image p078fig02.31 How noise suppression enables matching of bottom-up and top-down input patterns.
|| Noise suppression -> pattern matching. mismatch (out of phase) suppressed, match (in phase) amplifies pattern.
image p079fig02.32 Matching amplifies the matched pattern due to automatic gain control. See terms I and J in the equation.
|| Substrate of resonance. Match (in phase) of BU and TD input patterns AMPLIFIES matched pattern due to automatic gain control by shunting terms. J = sum[i: Ji], I = sum[i: Ii], θi = (Ii + Ji)/(I + J)
xi = (B + C)*(I + J)/(A + I + J)*[θi -C/(B + C)]
Need top-down expectations to be MODULATORY.
image p087fig03.01 A macrocircuit of key visual processes (in green) and the cortical areas in which they primarily occur (in red), from the retina to the Prefrontal Cortex (PFC), including both the What and Where cortical streams. The [bottom-up, horizontal, and top-down] interactions help each of these processes to overcome computationally complementary processing deficiencies that they would experience without them, and aso to read-out top-down expectations that help to stabilize learning while they focus attention on salient objects and positions.
|| Emerging unified theory of visual intelligence. [What, Where] streams. Bottom-up and top-down interactions overcome COMPLEMENTARY processing deficiencies.
image p091fig03.04 A cross-section of the eye, and top-down view of the retina, shao how the blind spot and retinal veins can occlude the registration of light signals at their positions on the retina.
|| Eye: [optic nerve, ciliary body, iris,lens, pupil, cornea, sclera, choroid, retina]. Human retina: [fovea, blind spot, optic nerve]. see alsi cross-section of retinal layer.
image p163fig04.39 A schematic of the LAMINART model that explains key aspects of laminar visual cortical anatomy and dynamics. LGN -> V1 [6, 4, 2/3] -> V2 [6, 4, 2/3]
|| p163c1h0.6 "... The first article abount laminar computing ... proposed how the laminar cortical model could process 2D pictures using bottom-up filtering and horizontal bipole grouping interactions (Grossbeerg, Mingolla, Ross 1997). In 1999, I was able to extend the model to also include top-down circuits for expectation and attention (Grossberg 1999)(right panel). Such a synthesis of laminar bottom-up, horizontal, and top-down circuits is characteristic of the cerebral cortex (left panel). I called it LAMINART because it began to show how properties of Adaptive Resonance Theory, or ART, notably the ART prediction about how top-down expectations and attention work, and are realized by identical cortical cells and circuits. You can immediately see from the schematic laminar circuit diagram ... (right panel) that circuits in V2 seem to repeat circuits in V1, albeit with a larger spatial scale, despite the fact that V1 and V2 carry out different functions, How this anatomical similarity can coexist with functional diversity will be clarified in subsequent sections and chapters. It enable dfifferent kinds of biological intelligence to communicate seamlessly while carrying out their different psychological functions. ..."
image p192fig05.06 Bottom-up and top-down circuits between the LGN and cortical area V1. The top-down circuits obey the ART Matching Rule for matching with bottom-up input patterns and focussing attention on expected critical features.
|| Model V1-LGN circuits, version [1, 2]. retina -> LGN relay cells -> interneurons -> cortex [simple, endstopped] cells -> cortex complex cells
image p193fig05.08 The patterns of LGN activation and inhibition on the sides and ends of a line without the top-down feedback (A) and with it (C). The top-down distribution of excitation (+) and inhibition (-) are shown in (B).
||
image p199fig05.11 Instar learning enables a bottom-up adaptive filter to become selectively tuned to particular feature patterns. Such pattern learning needs adaptive weights that can either increase or decrease to match the featural activations that they filter.
|| Instar learning STM->LTM: need both increases and decreases in strength for the LTM pattern to learn the STM pattern
image p200fig05.13 Instar and outstar learning are often used to learn the adaptive weights in the bottom-up filters and top-down expectations that occur in ART. The ART Matching Rule for object attention enables top-down expectations to select, amplify, and synchronize expected patterns of critical features, while suppressing unexpected features.
|| Expectations focus attention: feature pattern (STM), Bottom-Up adaptive filter (LTM), Category (STM), competition, Top-Down expectation (LTM); ART Matching Rule: STM before top-down matching, STM after top-down matching (attention!)
image p211fig05.20 The PN and N200 event-related potentials are computationally complementary events that are computed within the attentional and orienting systems.
|| PN and N200 are complementary waves. PN [top-down, conditionable, specific] match; N200 [bottom-up, unconditionable, nonspecific] mismatch
image p214fig05.24 Learning of a top-down expectation must occur during bottom-up learning in the adaptive filter in order to be able to match the previously associated feature pattern with the one that is currently active.
|| Learning top-down expectations. When the code (green right triangle GRT) for X1 was learned at F2, GRT learned to read-out X1 at F1. [Bottom-Up, Top-Down] learning
image p214fig05.25 The sequence of events whereby a novel input pattern can activate a category which, in turn, reads out its learned top-down expectation to be matched against the input pattern. Error correction thus requires the use of a Match Detector that has properties of the Processing Negativity ERP.
|| How is an error corrected. During bottom-up learning, top-down learning must also occur so that the pattern that is read out top-down can be compared with the pattern that is activated by bottom-up inputs. Match detector: Processing Negativity ERP. 1. top-down, 2. conditionable, 3. specific, 4. match
image p214fig05.26 When a big enough mismatch occurs, the orienting system is activated and sends a burst of nonspecific arousal to the category level. This Mismatch Detector has properties of the N200 ERP.
|| Mismatch triggers nonspecific arousal. Mismatch at F1 eleicits a nonspecific event at F2. Call this event nonspecific arousal. N200 ERP Naatanen etal: 1. bottom-up, 2. unconditionable, 3. nonspecific, 4. mismatch
image p215fig05.28 How a mismatch between bottom-up and top-down input patterns can trigger activation of the orienting system A and, with it, a burst of nonspecific arousal to the category level.
|| Mismatch -> inhibition -> arousal -> reset. BU input orienting arousal, BU+TD mismatch arousal and reset. ART Matching Rule: TD mismatch can suppress a part of F1 STM pattern, F2 is reset if degree of match < vigilance
image p220fig05.29 Vigilance is a gain parameter on inputs to the orienting system that regulates whether net excitation from bottom-up inputs or inhibition from activated categories will dominate the orienting system. If excitation wins, then a memory search for a better matching will occur. If inhibition wins, then the orienting system will remain quiet, thereby enabling resonance and learning to occur.
|| Vigilance control [resonate and learn, reset and search]. ρ is a sensitivity or gain parameter
image p221fig05.30 When a predictive disconfirmation occurs, vigilance increases enough to drive a search for a more predictive category. If vigilance increases just enough to exceed the analog match between features that survive top-down matching and the entire bottom-up input pattern, then minimax learning occurs. In this case, the minimum amount of category generalization is given up to correct the predictive error.
|| Match tracking realizes minimax learning principle. Given a predictive error, vigilance increases just enough to trigger search and thus acrifices the minimum generalization to correct the error ... and enables expert knowledge to be incrementally learned. predictive error -> vigilance increase just enough -> minimax learning
image p221fig05.31 A system like Fuzzy ARTMAP can learn to associate learned categories in one ART network with learned categories in a second ART network. Because both bottom-up and top-down interactions occur in both networks, a bottom-up input pattern to the first ART network can learn to generate a top-down output pattern from the second ART network.
|| Fuzzy ARTMAP. Match tracking realizes minimax learning principle: vigilence increases to just above the match ratio of prototype / exemplar, thereby triggering search
image p226fig05.35 I had shown in 1976 how a competitive learning or self-organizing map model could undergo catastrophic forgetting if the input environment was sufficiently dense and nonstationary, as illustrated by Figure 5.18. Later work with Gail Carpenter showed how, if the ART Matching Rule was shut off, repeating just four input patterns in the correct order could also casue catastrophic forgetting by causing superset recoding, as illustrated in Figure 5.36.
|| Code instability input sequences. D C A; B A; B C = ; |D|<|B|<|C|; where |E| is the number of features in the set E. Any set of input vectors that satisfy the above conditions will lead to unstable coding if they are periodically presented in the order ABCAD and the top-down ART Matching Rule is shut off.
image p246fig05.48 Microcircuits of the LAMINART model that I developed with Rajeev Raizada. See the text for details of how they integrate bottom-up adaptive filtering, horizontal bipole grouping, and top-down attentional matching that satisfied the ART Matching Rule.
||
image p252fig06.01 A surface-shroud resonance begins to form when the surface representations of objects bid for spatial attention. In addition to these topographic excitatory inputs, there is long-range inhibition of the spatial attention cells that determines which inputs will attract spatial attention.
|| Bottom-up spatial attention competition. [more, less] luminous perceptual surfaces -> competition -> spatial attention
image p253fig06.02 After bottom-up surface inputs activate spatial attentional cells, they send top-down topographic excitatory signals back to the surface representations. This recurrent shunting on-center off-surround network contrast enhances larger attentional activities while approximately normalizing the total spatial attentional activity. A surface-shroud resonance hereby forms that selects an attentional shroud, enhances the perceived contrast of the attended surface (light blue region), and maintains spatial attention on it.
|| Surface-shroud resonance. perceptual surfaces -> competition -> spatial attention. (Carrasco, Penpeci-Talgar, and Eckstein 2000, Reynolds and Desimone 2003)
image p258fig06.07 A top-down spotlight of attention can also be converted into a shroud. This process begins when the spotlight triggers surface filling-in within a region. Figure 6.8 shows how it is completed.
|| Reconciling spotlights and shrouds: top-down attentional spotlight becomes a shroud. spotlight of attention, surface filling-in
image p286fig07.04 Illusory contours persist longer than real contours because real contours have more inducers whose rebound at contour offset can cause faster boundary reset. Illusory contours also take longer to form than real contours, which explains the increasing portion of the curve.
|| Persistence data and simulations (Meyer, Ming 1988; Reynolds 1981). Increasing portion of curve is due to formation time of the illusory contour. Longer persistence is due to fewer bottom-up inducers of an illusory contour that has the same length as a real contour: only illuminance-derived edges generate reset signals. When bottom-up inducers are inhibited by OFF cell rebounds, their offset gradually propagates to the center of the illusory contour.
image p286fig07.05 This figure shows the propagation through time of illusory contour offset from the rebounded cells that got direct inputs to the center of the contour.
|| Persistence data and simulations. Illusory contours persist longer than real contours (Meyer, Ming 1988; Reynolds 1981). When bottom-up inducers are inhibited by OFF cell rebounds, their offset gradually propagates to the center of the illusory contour.
image p315fig08.35 The output signals from the directional grouping network obey the ART Matching Rule. They thereby select consistent motion directional signals while suppressing inconsistent ones, and do not distort that the spared cells code. The aperture problem is hereby solved by the same mechanism that dynamically stabilizes the learning of directional grouping cells.
|| How to select correct direction and preserve speed estimates? Prediction: Feedback from MSTv to MT- obeys ART Matching Rule; Top-down, modulatory on-center, off-surround network (Grossberg 1976, 1980; Carpenter, Grossberg 1987, 1991); Explains how directional grouping network can stably develop and how top-down directional attention can work. (Cavanough 1992; Goner etal 1986; Sekuer, Ball 1977; Stelmach etal 1994). Directional grouping network (MSTv) <-> Directional long-range filter (MT). Modulatory on-center selects chosen direction and preserves speed. Off-surround inhibits incompatible directions.
image p330fig08.52 Direction fields of the object frame (left column) and of the two dot "parts" (right column) show the correct motion directions after the peak shift top-down expectation acts.
|| Simulation of motion vector decomposition. [Larger scale (nearer depth), Small scale (farther depth)] vs [Down, Up]
image p331fig08.54 The simulated part directions of the rotating dot through time after the translational motion of the frame does its work via the top-down peak shift mechanism.
|| Cycloid. Motion directions of a single dot moving slowly along a cycloid curve through time.
image p354fig10.01 The lamiar cortical circuit that realizes how we pay attention to an object sends signals from layer 6 of a higher cortical level to layer 6 of a lower cortical level and then back up to layer 4. This "folded feedback" circuit realizes a top-down, modulatory on-center, off-surround circuit that realizes the ART Matching Rule.
|| Top-down attention and folded feedback. Attentional signals also feed back into 6-to-4 on-center off-surround. 1-to-5-to-6 feedback path: Macaque (Lund, Booth 1975) cat (Gilbert, Wiesel 1979). V2-to-V1 feedback is on-center off-surround and affects layer 6 of V1 the most (Bullier etal 1996; Sandell, Schiller 1982). Attended stimuli enhanced, ignored stimuli suppressed. This circuit supports the predicted ART Matching Rule! [LGN, V[1,2][6->1]]
image p356fig10.03 Laminar computing achieves at least three basic properties of visual processing that have analogs in all biologically intelligent behaviors. These properties may be found in all cortical circuits in specialized form.
|| What does Laminar Computing achieve? 1. Self-stabilizing development and learning; 2. Seamless fusion of a) pre-attentive automatic bottom-up processing, b) attentive task-selective top-down processing; 3. Analog coherence: Solution of Binding Problem for perceptual grouping without loss of analog sensitivity. Even the earliest visual cortical stages carry out active adaptive information processing: [learn, group, attention]ing
image p359fig10.05 Activation of V1 is initiated, in part, by direct excitatory signals from the LGN to layer 4 of V1.
|| How are layer 2/3 bipole cells activated? Direct bottom-up activation of layer 4. LGN -> V1 layer 4. Strong bottom-up LGN input to layer 4 (Stratford etal 1996; Chung, Ferster 1998). Many details omitted.
image p359fig10.06 Another, albeit indirect, pathway from LGN exists that can also excite layer 4 of V1. Why are not these two pathways redundant? The answer, ultimately, how to do with how cortex learns, as well as with how it pays attention. See the text for details.
|| Another bottom-up input to layer 4: Why?? Layer 6-to-4 on-center off-surround (Grieve, Sillito 1991, 1995; Ahmedetal 1994, 1997). LGN projects to layers 6 and 4. Layer 6 excites spiny stellates in column above it. Medium range connections onto inhibitory neurons. 6-t-4 path acts as on-center off-curround.
image p359fig10.07 The two bottom-up pathways from LGN to layer 4 of V1 can together activate layer 4 and contrast-normalize layer 4 responses.
|| Bottom-up contrast normalization (Grossberg 1968, 1973; Sperling, Sondhi 1968; Heeger 1992; Douglas etal 1995; Shapley etal 2004). Together, direct LGN-to-4 path and 6-to-4 on-center off-surround provide contrast normalization if cells obey shunting or membrane equation dynamics.
image p360fig10.08 The bottom-up on-center off-surround from LGN-to-6-to-4 has a modulatory on-center because of its role in realizing the ART Matching Rule and, with it, the ability of the cortex to dynamically stabilize its learned memories.
|| Modulation of priming by 6-to-4 on-center (Stratford etal 1996; Callaway 1998). On-center 6-to-4 excitation is inhibited down to being modulatory (priming, subthreshold). On-center 6-to-4 excitation cannot activate layer 4 on its own. Clarifies need for direct path. Prediction: plays key role in stable grouping, development and learning. ART Matching Rule!
image p364fig10.13 The bottom-up adaptive filter, intracortical grouping circuit, and intercortical top-down attentional circuit all use the same competitive decision circuit between layers 6 and 4, called the attention-preattention interface, with which to select the featural patterns that will be processed.
|| Bottom-up filters and intracortical grouping feedback use the same 6-to-4 decision circuit, LGN-> Vx[6,4,2/3]. competitive decision circuit, modulatory on-center off-surround network. Top-down intercortical attention also uses the same 6-to-4 decision circuit!
image p364fig10.14 This figure emphasizes how preattentive intracortical groupings and top-down intercortical attention share the same modulatory on-center, off-surround layer 4-to-6 decision circuit.
|| Explanation: grouping and attention share the same modulatory decision circuit. Layer 6-6-4-2/3 pathway shown; also a layer 6-1-2/3 path. intercortical attention, both act via a modulatory on-center off-surround decision circuit, intracortical feedback from groupings
image p420fig12.18 The ARTSTREAM model explains and simulates the auditory continuity illusion as an example of a spectral-pitch resonance. Interactions of ART Matching Rule and asymmetric competition mechanisms in cortical strip maps explain how the tone selects the consistent frequency from the noise in its own stream while separating the rest of the noise into another stream.
|| ARTSTREAM model (Grossberg 1999; Grossberg, Govindarajan, Wyse, Cohen 2004). SPINET. Frequency and pitch strips. Bottom Up (BU) harmonic sieve. Top Down (TD) harmonic ART matching. Exclusive allocation. Learn pitch categories based on early harmonic processing. A stream is a Spectral-Pitch Resonance!
image p441fig12.38 The LTM Invariance Principle is realized if the relative sizes of the inputs to the list chunk level stay the same as more items are stored in working memory. This property, in turn, follows from shunting previously stored working memory activities when a ne4w item occurs.
|| LTM Invariance principle. Choose STM activities so that newly stored STM activities may alter the size of old STM activities without recoding their LTM patterns. In particular: New events do not change the relative activities of past event sequences, but may reduce their absolute activites. Why? Bottom-up adaptive filtering uses dot products: T(j) = sum[i=1 to n: x(i)*z(i,j) = total input to v(j). The relative sizes of inputs to coding nodes v(j) are preserved. x(i) -> w*x(i), 0 < w <= 1, leaves all past ratios T(j)/T(k) unchanged.
image p449fig12.47 This figure illustrates the self-similarity in a Masking Field of both its recurrent inhibitory connections (red arrows) and its top-down excitatory priming signals (green arrows) to the item chunk working memory.
|| Both recurrent inhibition and top-down excitatory priming are self-similar in a masking field. MYSELF <-> [MY, MYSELF]
image p453fig12.50 The ARTWORD perception cycle shows how sequences of items activate possible list chunks, which compete among each other and begin to send their top-down expectations back to the item working memory. An item-list resonance develops through time as a result.
|| ARTWORD perception cycle. (a) bottom-up activation (b) list chunk competition (c) item-list resonance (d) chunk reset due to habituative collapse.
image p483fig13.02 The object-value categories in the orbitofrontal cortex require converging specific inputs from the sensory cortex and nonspecific incentive motivational inputs from the amygdala in order to fire. When the orbitofrontal cortex fires, it can deliver top-down ART Matching Rule priming signals to the sensory cortical area by which it was activated, thereby helping to choose the active recognition categories there that have the most emotional support, while suppressing others, leading to attentional blocking of irrelevant cues.
|| Cognitive-Emotional-Motor (CogEM) model. Drive-> amygdala incentive motivational learning-> orbitofrontal cortex- need converging cue and incentive iputs to fire <-> sensory cortex- conditioned renforcer learning-> amygdala. CS-> sensory cortex. Motivated attention closes the cognitive-emotional feedback loop, focuses on relevant cues, and causes blocking of irrelevant cues.
image p484fig13.04 The top-down feedback from the orbitofrontal cortex closes a feedback loop that supports a cognitive-emotional resonance. If this resonance can be sustained long enough, it enables us to have feelings at the same time that we experience the categories that caused them.
|| Cognitive-Emotional resonance. Basis of "core consciousness" and "the feeling of what happens". (Damasio 1999) derives heuristic version of CogEM model from his clinical data. Drive-> amygdala-> prefrontal cortex-> sensory cortex, resonance around the latter 3. How is this resonance maintained long enough to become conscious?
image p523fig14.03 (a) The MOTIVATOR neural model generalizes CogEM by also including the basal ganglia. It can hereby explain and simulate complementary functions of the amygdala and basal ganglia (SNc) during conditioning and learned performance. The basal ganglia generate Now Print signals in response to unexpected rewards. These signals modulate learning of new associations in many brain regions. The amygdala supports motivated attention to trigger actions that are expected to occur in response to conditioned or unconditioned stimuli. Object Categories represent visual or gustatory inputs in anterior inferotemporal (ITA) and rhinal (RHIN) cortices, respectively. Value Categories represent the value of anticipated outcomes on the basis of hunger and satiety inputs, in amygdala (AMYG) and lateral hypothalamus (LH). Object-Value Categories resolve the value of competing perceptual stimuli in medial (MORB) and lateral (ORB) orbitofrontal cortex. The Reward Expectation Filter detects the omission or delivery of rewards using a circuit that spans ventral striatum (VS), ventral pallidum (VP), striosomal delay (SD) cells in the ventral striatum, the pedunculopontine nucleus (PPTN) and midbrain dopaminergic neurons of the substantia nigra pars compacta/ventral tegmental area (SNc/VTA). The circuit that processes CS-related visual information (ITA, AMTG, ORB) operates in parallel with a circuit that processes US-related visual and gustatory information (RHIN, AMYG, MORB). (b) Reciprocal adaptive connections between hypothalamus and amygdala enable amygdala cells to become learned value categories. The bottom region represents hypothalmic cells, which receive converging taste and metabolite inputs whereby they become taste-drive cells. Bottom-up signals from activity patterns across these cells activate competing value category, or US Value Representations, in the amygdala. A winning value category learns to respond selectively to specific combinations of taste-drive activity patterns and sends adaptive top-down priming signals back to the taste-drive cells that activated it. CS-activated conditioned reinforcer signals are also associatively linked to value categories. Adpative connections end in (approximately) hemidiscs. See the text for details.
||
image p600fig16.36 The entorhinal-hipppocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories. See the text for details.
|| Entorhinal-hippocampal interactions as an ART system. Hippocampal place cells as spatial categories. Angular head velocity-> head direction cells-> stripe cells- small scale 1D periodic code (ECIII) SOM-> grid cells- small scale 2D periodic code (ECII) SOM-> place cells- larger scale spatial map (DG/CA3)-> place cells (CA1)-> conjunctive-coding cells (EC V/VI)-> top-down feedback back to stripe cells- small scale 1D periodic code (ECIII). stripe cells- small scale 1D periodic code (ECIII)-> place cells (CA1).
image p613fig16.44 The main target position vector (TPV), difference vector (DV), and volitional GO computations in SOVEREIGN that bring together reactive and planned signals to control decision-making and action. See the text for details.
|| Reactive visual TPV (RVT), NETs (NETs), S-MV mismatch (SMVM), NETmv (NETmv), reactive visual TPV storage (RVTS), reactive DV1 (RD1), NET (NET), motivated what and where decisions (MWWD), Planned DV1 (PD1), tonic (Tonic), top-down readout mismatch (TDRM), Parvo gate (tonic) (PG), Orienting GOp offset (OGpO). RVT-> [NETs, RVTS], NETs-> [SMVM, NET], SMVM-> NET, NETmv-> SMVM, RVTS-> [NETs, RD1], NET-> [RD1, PD1, TDRM], MWWD-> PD1, PD1-> Tonic-> TDRMPG-> NETs, OGpO-> [NETmv, PD1].
Grossberg 2021 p229c2h0.60 SMART computer simulations demonstrate that a good enough match of a top-down expectation with a bottom-up feature pattern generates an attentive resonance during which the spikes of active cells synchronize in the gamma frequency range of 20-70 Hz (Figure 5.40). Many labs have reported a link between attention and gamma oscillations in the brain, including two articles published in 2001, one from the laboratory of Robert Desimone when he was at the the National Institute of Mental Health in Bethseda (Fries, Reynolds, Rorie, Desimone 2001), and the other from the laboratory of Wolf Singer in Frankfurt (Engel, Fries, Singer 2001). You'll note that Pascal Fries participated in both studies, and is an acknowledged leader in neurobiological studies of gamma oscillations; eg (Fries 2009). .."
Grossberg 2021 p081-83 sub-section: How does one evolve a computational brain?
- Behavioral Success drives::>> Behavioral Data -> Art of Modelling -> Design Principles -> Mathematical Models -> Method of Minimal Anatomies (Occam's Razor) -> Behavioral Predictions -> explain Neural Data -> novel Brain Predictions <<::links behavior-mind-brain
- "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything.
- >characterize the boundary between the known and unknown: the shape of this boundary helps to direct attention to new design principles
(keys : behavior-mind-brain link)
Grossberg 2021 p081c2h0.66 sub-section: How does one evolve a computational brain?
The above discussion illustrates that no single step of theoretical derivation can derive a whole brain. One needs a method for deriving a brain in stages, or cycles, much as evolution has incrementally discovered ever more complex brains over many thousands of years. The following theoretical method has been successfully applied many times since I first used it in 1957. It embodies a kind of conceptual evolutionary process for deriving a brain.

Because "brain evolution needs to achieve behavioural success", we need to start with data that embodiey indices of behavioral success. That is why, as illustrated in Figure 2.37 Modelling method and cycle, one starts with Behavioral Data from scores or hundreds of psychological experiments. These data are analyszed as the result of an individual adapting autonomously in real time to a changing world. This is the Arty of Modeling. It requires that one be able to infer from static data curves the dynamical processes that control individual behaviors occuring in real time. One of the hardest things that I teach to my students to do is "how to think in real time" to be able to carry out this speculative leap.

Properly carried out, this analysis leads to the discovery of new Design Principles that are embodied by these behavioral processes. The Design Principles highlight the functional meaning of the data, and clarify how individual behaviors occurring in real time give rise to these static data curves.

These principles are then converted into the simplest Mathematical Model using a method of minimal anatomies, which is a form of Occam's Razor, or principle of parsimony. Such a mathematical model embodies the psychological principles using the simplest possible differential equations. By "simplest" I mean that, if any part of the derived model is removed, then a significant fraction of the targeted data could no longer be explained. One then analyzes the model mathematically and simulates it on the computer, showing along the way how variations on the minimal anatomy can realize the design principles in different individuals or species.

This analysis has always provided functional explanations and Behavioral Predictions for much larger behavioral data bases than those used to discover the Design Principles. The most remarkable fact is, however, that the behaviorally derived model always looks like part of a brain, thereby explaining a body of challenging Neural Data and making novel Brain Predictions.

The derivation hereby links mind to brain via psychological organizational principles and their mechanistic realization as a mathematically defined neural network. This startling fact is what I first experienced as a college Freshman taking Introductory Psychology, and it changed my life forever.

I conclude from having had this experience scores of times since 1957 that brains look the way they do because they embody a natural computational realization for controlling autonomous adaptation in real-time to a changing world. Moreover, the Behavior -> Principles -> Model -> Neural derivation predicts new functional roles for both known and unknown brain mechanisms by linking the brain data to how it helps to ensure behavioral success. As I noted above, the power of this method is illustrated by the fact that scores of these predictions about brain and behavior have been supported by experimental data 5-30 years after they were first published.

Having made the link from behavior to brain, one can then "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything. At each stage of development, the model can cope with certain environmental challenges but not others. An important part of the mathematical and computational analysis is to characterize the boundary between the known and unknown; that is which challenges the model can cope with and which it cannot. The shape of this boundary between the known and unknown helps to direct the theorist's attention to new design principles that have been omitted from previous analysis.

The next step is to show how these new design principles can be incorporated into the evolved model in a self-consistent way, without undermining its previous mechanisms, thereby leading to a progressively more realistic model, one that can explain and predict ever more behavioral and neural data. In this way, the model undergoes a type of evolutionary development, as it becomes able to cope behaviorally with environmental constraints of ever increasing subtlety and complexity. The Method of Minimal Anatomies may hereby be viewed as way to functionally understand how increasingly demanding combinations of environmental pressures were incorporated into brains during the evolutionary process.

If such an Embedding Principle cannot be carried out - that is, if the model cannot be unlumped or refined in a self-consistent way - then the previous model was, put simply, wrong, and one needs to figure out which parts must be discarded. Such a model is, as it were, an evolutionary dead end. Fortunately, this has not happened to me since I began my work in 1957 because the theoretical method is so conservative. No theoretical addition is made unless it is supported by multiple experiments that cannot be explained in its absence. Where multiple mechanistic instantiations of some Design Principles were possible, they were all developed in models to better underestand their explanatory implications. Not all of these instantiations could survive the pressure of the evolutionary method, but some always could. As a happy result, all earlier models have been capable of incremental refinement and expansion.

The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved.

Howell: bold added for emphasis.
(keys : Principles-Principia, behavior-mind-brain link, brain evolution, cycle of model evolution)
see also quotes: Charles William Lucas "Universal Force" and others (not retyped yet).

p190 Howell: [neural microcircuits, modal architectures] used in ART -

bottom-up filters	top-down expectations	purpose
instar learning	outstar learning	p200fig05.13 Expectations focus attention: [in, out]star often used to learn the adaptive weights. top-down expectations to select, amplify, and synchronize expected patterns of critical features, while suppressing unexpected features
LGN Lateral Geniculate Nucleus	V1 cortical area	p192fig05.06 focus attention on expected critical features
EC-III entorhinal stripe cells	CA1 hippocampal place cells	p600fig16.36 entorhinal-hippocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories
auditory orienting arousal	auditory category	p215fig05.28 How a mismatch between bottom-up and top-down patterns can trigger activation of the orienting system A and, with it, a burst of nonspecific arousal to the category level. ART Matching Rule: TD mismatch can suppress a part of F1 STM pattern, F2 is reset if degree of match < vigilance
auditory stream with/without [silence, noise] gaps	perceived sound continues?	p419fig12.17 The auditory continuity illusion: Backwards in time - How does a future sound let past sound continue through noise? Resonance!
visual perception, learning and recognition of visual object categories	motion perception, spatial representation and target tracking	p520fig14.02 pART predictive Adaptive Resonance Theory. Many adaptive synapses are bidirectional, thereby supporting synchronous resonant dynamics among multiple cortical regions. The output signals from the basal ganglia that regulate reinforcement learning and gating of multiple cortical areas are not shown. red - cognitive-emotional dynamics green - working memory dynamics black - see [bottom-up, top-down] lists
EEG with mismatch, arousal, and STM reset events	expected [P120, N200, P300] event-related potentials (ERPs)	p211fig05.21 Sequences of P120, N200, and P300 event-related potentials (ERPs) occur during oddball learning EEG experiments under conditions that ART predicted should occur during sequences of mismatch, arousal, and STM reset events, respectively.
Cognitive	Emotional	p541fig15.02 nSTART neurotrophic Spectrally Timed Adaptive Resonance Theory: Hippocampus can sustain a Cognitive-Emotional resonance: that can support "the feeling of what happens" and knowing what event caused that feeling. Hippocampus enables adaptively timed learning that can bridge a trace conditioning gap, or other temporal gap between Conditioned Stimuluus (CS) and Unconditioned Stimulus (US).

backgound colours in the table signify :

white	general microcircuit : a possible component of ART architecture
lime green	sensory perception [attention, expectation, learn]. Table includes [see, hear, !!must add touch example!!], no Grossberg [smell, taste] yet?
light blue	post-perceptual cognition?
pink	"the feeling of what happens" and knowing what event caused that feeling

cooperative-competitive

grepStr = cooperative|competitive

p009sec Cooperative-competitive dynamics: stable economic markets and the invisible hand
p011sec Cooperative and competitive dynamics: democracy, totalitarianism, and socialism
p012sec Cooperative-competitive-learning dynamics: ART in the brain and technology
As stated in [Grossberg 2021 p13c1h1.0] : "... This range of applications is possible because ART models embody general-purpose properties that are needed to solve the stability-plasticity dilemma in many different types of environments. In all these applications, insights about cooperative-competitive dynamics also play a critical role. ..."
p075sec Towards a univeral developmental code using mass action cooperative-competitive dynamics
p149sec How does long-range cooperative grouping occur?
p152sec Cooperative-competitive feedback selects the final grouping
p201sec Joining competitive choice to INSTAR learning: Competitive learning
p202sec Competitive decisions: Contrast enhancement and normalization during category learning
p204sec Competitive learning and self-organizing maps: On the road to adaptive rsonance
p204sec Nice properties of competitive learning in sparse environments
p609sec Competitive choices in STM
image p009fig01.06 Primacy gradient of activity stored in working memory within a recurrent shunting on-center off-surround network. Rehersal is controlled by a nonspecific rehersal wave and self-inhibitory feedback of the item that is currently being rehearsed. Rehearsal is controlled by a nonspecific rehearsal wave and self-inhibitory feedback of the item that is currently being rehearsed. Green = excitatory, red = inhibitory
|| inputs? -> item and order WM storage -> competitive selection-> rehearsal wave -> outputs
image p077fig02.27 A schematic of the on-center off-surround network that occurs in the mudpuppy retina, including three main cell types: receptors, horizontal cells, and bipolar cells.
|| Mechanism: cooperative-competitive dynamics.
On-center off-surround (Kuffler 1953) cat retina
Subtractive lateral inhibition (Hartline, Ratcliff 1956/7+) limulus retina.
R receptor -> H horizontal -> B bipolar (Werblin, Dowling, etal 1969+) mudpuppy retina.
image p100fig03.15 A fuzzy band of possible initial grouping orientations allows grouping to get started. Cooperative-competitive feedback via a hierarchical resolution of uncertainty chooses a sharp final grouping that has the most evidence to support it.
|| before choice: transient; after choice: equilibrium
image p108fig03.28 The watercolor illusion of Baingio Pinna 1987 can be explained using spatial competition betweeen like-oriented boundary signals. This occurs at what I have called the First Competitive Stage. This is one stage in the brain's computation of hypercomplex cells, which are also called endstopped complex cells. Why the blue regions seem to bulge in depth may be explained using multple-scale, depth-selective boundary webs. See ther text for details.
|| Baigio Pinna. Watercolor illusion 1987. Filled-in regions bulge in depth. Multiple-scale, depth-selective boundary web!
image p146fig04.25 Networks of simple, complex, and hypercomplex cells can create end cuts as an example of hierarchical resolution of uncertainty. See the text for details.
|| How are end cuts created? (Grossberg 1984) Two stages of short-range competition. 1st stage: Simple cells -> complex cells -> hypercomplex - endstopped complex. First competitive stage- across position, same orientation; Second competitive stage- same position, across orientation. -> cooperation.
image p148fig04.26 End cuts are formed during neon color spreading in the same way that they are formed at line ends.
|| End cut during neon color spreading.
FIRST competitive stage SECOND competitive stage
within orientation across orientation
across position within position
to generate end cuts.
image p149fig04.27 Bipole cells can form boundaries that interpolate end cuts, and use their cooperative-competitive interactions to choose the boundary groupings that have the most support from them.
|| Bipole cells: boundary completion. long-range cooperation & short-range inhibition: complete winning boundary groupings and suppress weaker boundaries.
image p161fig04.37 Kanizsa squares that form either collinearly to their inducers (left panel) or perpendicular to them (right panel) confirm predictions of the BCS boundary completion model.
|| Analog-sensitive boundary completion. contour strength vs Kanizsa square image. Increases with "support ratio" (Shipley, Kellman 1992). Inverted-U (Lesher, Mingoloa 1993; cf Soriano, Spillmann, Bach 1994)(shifted gratings). p370h0.6 BCS = Boundary Contour System, FCS = Feature Contour System. p161c1h0.85 "... As predicted by the BCS, they found an Inverted-U in contour strength as a function of line density. ... This effect may be explained by the action of the short-range competition that occurs before the stage of long-range cooperative grouping by bipole cells (Figure 4.32). It is thus another example of the balance between cooperative and competitive mechanisms. ..."
image p198fig05.10 A competitive learning circuit learns to transform distributed feature patterns into selective responses of recognition categories.
|| Competitive learning and Self-Organized Maps (SOMs). input patterns -> feature level (F1) -> adaptive filter (T=ZS) ->
image p205fig05.18 How catastrophic forgetting can occur in a competitive learning or self-organizing map model due to basic properties of competition and associative learning.
|| Learning from pattern sequences, practicing a sequence of spatial patterns can recode all of them! When is learning stable? Input patterns cannot be too dense relative to the number of categories; Either: not to many distributed inputs relative to the number of categories, or not too many input clusters
image p226fig05.35 I had shown in 1976 how a competitive learning or self-organizing map model could undergo catastrophic forgetting if the input environment was sufficiently dense and nonstationary, as illustrated by Figure 5.18. Later work with Gail Carpenter showed how, if the ART Matching Rule was shut off, repeating just four input patterns in the correct order could also casue catastrophic forgetting by causing superset recoding, as illustrated in Figure 5.36.
|| Code instability input sequences. D C A; B A; B C = ; |D|<|B|<|C|; where |E| is the number of features in the set E. Any set of input vectors that satisfy the above conditions will lead to unstable coding if they are periodically presented in the order ABCAD and the top-down ART Matching Rule is shut off.
image p287fig07.07 Persistence increases with distance between a target and a masking stimulus due to weakening of the spatial competition in the first competitive stage of hypercomplex cells.
|| Persistence data and simulations. Persistence increases with distance between a target and a masking stimulus (Farrell, Pavel, Sperling 1990). There is less spatial competition from the masker to the target when they are more distant, hence the target is more persistent.
image p364fig10.13 The bottom-up adaptive filter, intracortical grouping circuit, and intercortical top-down attentional circuit all use the same competitive decision circuit between layers 6 and 4, called the attention-preattention interface, with which to select the featural patterns that will be processed.
|| Bottom-up filters and intracortical grouping feedback use the same 6-to-4 decision circuit, LGN-> Vx[6,4,2/3]. competitive decision circuit, modulatory on-center off-surround network. Top-down intercortical attention also uses the same 6-to-4 decision circuit!
image p437fig12.33 Item and Order working memory models explain free recall data, as well as many other psychological and neurobiological data, by simulating how temporal series of events are stored as evolving spatial patterns of activity at content-addressable item categories. The categories with the largest activities are rehearsed first, and self-inhibit their activity as they do so in order to prevent tem from being rehearsed perseveratively. The laws whereby the items are stored in working memory obey basic design principles concerning list categories, or chunks, of sequences of stored items can be stably remembered.
|| Working memory models: item and order, or competitive queuing (Grossberg 1978; Houghton 1990; Page, Norris 1998). Event sequence in time stored as an evolving spatial pattern of activity. Primacy gradient of working memory activation stores correct temporal order at content-addressable cells. Maximally activated cell populations is performed next when a rehearsal wave is turned on. Output signal from chosen cell population inhibits its own activity to prevent perseveration: inhibition of return. Iterate until entire sequence is performed.
image p488fig13.12 (left column) How incentive motivational feedback amplifies activity of a sensory cortical cell population. (right column) A sensory cortical cell population whose activity is amplified by incentive motivational feedback can suppress the activities of less activated populations via self-normalizing recurrent competitive interactions.
|| Motivational feedback and blocking. (left) sensory input CS, STM activity without motivational feedback, STM activity with motivational feedback. (right) STM suppressed by competition, STM amplified by (+) feedback.
image p510fig13.39 Shunting competition and informational noise suppression in affective gated dipoles, plus back-propagating action potentials for teaching signals, enable the net normalized adaptive weights to be learned. They never saturate!
|| Learn net dipole output pattern. Opponent "decision" controls learning. Cf. competitive learning. Learning signal, opponent extinction.
Grossberg 2021 p067c1h0.60 Teuvo Kohonen at Helsinki University also made a transition from linear algebra concepts, such as the Moore-Penrose pseudo-inverse (eg Albert 1972), to more biologically motivated models that include nonlinear interactions. In the latter category, Kohonen used a simplified version of a Self-Organizing Map, or SOM, model in various applications (eg Kohonen 1984). ... The SOM had earlier been introduced and developed in the early 1970s by myself working in Cambridge and Boston, and Christof von der Malsburg, who was then at the Max-Planc Institute for Biophysical Chemistry in Gottingen(Grossberg 1972c, 1976a, 1976b; Malsburg 1973). I had even earlier introduced and mathematically analyszed the learning laws and competitive networks that go into the SOM model, starting in the 1960s (eg Grossberg 1968c, 1969c, 1971b, 1973, 1974).
Grossberg 2021 p204c2h0.30 Models that combine instar learning and competition are often called competitive learning or self-organizing map models. Such models were introduced and computationally characteried by Christoph von der Malsburg and myself between 1972-1978 (Grossberg 1972c, 1976a, 1978a; von der Malsburg 1973; Willshaw, Malsburg 1976). They were subsequently applied and further developed by many authors, notably Teuvo Kohonen, whose 1984 book on the subject, and its subsequent editions, helped to popularize them (Kohonen 1984).

complementary computing

grepStr = complementary

p001 Chapter 1 Overview - From Complementary Computing and Adaptive Resonance to conscious awareness
p250 Chapter 6 Conscious seeing and invariant recognition - Complementary cortical streams coordinate attention for seeing and recognition
p289 Chapter 8 How we see and recognize object motion - Visual form and motion perception obey complementary laws
p337 Chapter 9 Target tracking, navigation, and decision-making - Visual tracking and navigation obey complementary laws
p000XVIIIsec New computational paradigms: Laminar Computing and Complementary Computing From Complementary Computing and Adaptive Resonance to conscious awareness
p006sec Balance, complementary computing, and the stability-plasticity dilemma
p029sec Two revolutionary paradigms: [complementary, laminar] computing
p032sec Complementary computing vs independent modules
p033sec Hierarchical resolution of uncertainty and complementary consistency
p096sec Complementary computation of boundaries and surfaces
p097sec Complementary processing streams in visual cortex
p097sec Multiple-scale symmetry-breaking generates complementary streams during development
p173sec Surface contours realize complementary consistency and begin figure-ground separation
p208sec Complementary attentional and orienting systems
p210sec Feature-category resonances and complementary PN and N200 event-related potentials
p213sec Complementary information is computed at distributed and symbolic levels
p222sec Vigilence control depends upon complementary computing Complementary cortical streams coordinate attention for seeing and attention
p265sec From complementary consistency to figure-ground separation: Surface contours Visual form and motion perception obey complementary laws
p290sec Complementary computing of orientation and direction: Long-range directional filter Visual tracking and navigation obey complementary laws
p337sec Target tracking and optic flow navigation use complementary cortical streams
p404sec Ventral sound-to-menaing vs. dorsal sound-to-action: Complementary invariants

image p029tbl01.01 Some pairs of complementary processing streams.
||

visual boundary: interblob stream V1-V2-V4	visual surface: blob stream V1-V2-V4
visual boundary: interblob stream V1-V2-V4	visual motion: magno stream V1-MT-MST
WHAT stream	WHERE stream
perception & recognition: interferotemporal & prefrontal areas	space & action: parietal & prefrontal areas
object tracking: MT interbands & MSTv	optic flow navigation: MT+ bands & MSTd
motor target position: motor & parietal cortex	volitional speed: basal ganglia

image p030tbl01.02 The What and Where cortical processing streams obey complementary laws. These laws enable the What stream to rapidly and stably learn invariant object categories without experiencing catastrophic forgetting, while the Where stream learns labile spatial and action representations to control actions that are aimed towards these objects.
||

WHAT	WHERE
spatially-invariant object learning and recognition	spatially-variant reaching and movement
fast learning without catastrophic forgetting	continually update sensory-motor maps and gains
IT InterferoTemporal Cortex	PPC Posterior Parietal Cortex

	What	Where
matching	excitatory	inhibitory
learning	match	mismatch

image p087fig03.01 A macrocircuit of key visual processes (in green) and the cortical areas in which they primarily occur (in red), from the retina to the Prefrontal Cortex (PFC), including both the What and Where cortical streams. The [bottom-up, horizontal, and top-down] interactions help each of these processes to overcome computationally complementary processing deficiencies that they would experience without them, and aso to read-out top-down expectations that help to stabilize learning while they focus attention on salient objects and positions.
|| Emerging unified theory of visual intelligence. [What, Where] streams. Bottom-up and top-down interactions overcome COMPLEMENTARY processing deficiencies.
image p094fig03.07 The processes of boundary completion and surface filling-in are computationally complementary.
||
Boundary completion Surface filling-in
outward inward
oriented unoriented
insensitive to direction of contrast sensitive to direction-of-contrast
image p174fig04.51 The same feedback circuit that ensures complementary consistency between boundaries and surfaces also, automatically, initiates figure-ground separation! See the text for details.
|| before feedback: [V1 -> V2 pale stripe -> V2 thin stripe, "attention pointers" (Cavanagh etal 2010)]; after feedback: [V1 + V2 thin stripe] -> V2 pale stripe via contrast sensitive [exhitation, inhibition] for depths [1, 2] -> object recognition
image p176fig04.53 The on-center off-surround network within position and across depth helps to explain why brighter Kanizsa squares look closer.
|| inhibition vs. depth. p176c1h0.25 "... to qualitatively understand how this example of proximity-luminance covariance works. It follows directly from the boundary pruning by surface contour feedback signals (Figure 4.51) that achieves complementary consistency and initiates figure-ground perception. ...". p176c1h0.45 "... these inhibitory sigals are part of an off-surround network whose strength decreases as the depth difference increases between the surface that generates the signal and its recipient boundaries. ...". p176c1h0.8 "... Within FACADE theory, the perceived depth of a surface is controlled by the boundaries that act as its filling-in generators and barriers (Figure 3.22), since these boundaries select the depth-sselective FIDOs within whin filling-in can occur, and thereby achieve surface capture. These boundaries, in turn, are themselves strengthened after surface-to-boundary contour feedback eliminates redundant boundaries that cannot support sucessful filling-in (Figure 4.51). These surface contour feedback signals have precisely the properties that are needed to explain why brighter Kanizsa squares look closer! ..."
image p211fig05.20 The PN and N200 event-related potentials are computationally complementary events that are computed within the attentional and orienting systems.
|| PN and N200 are complementary waves. PN [top-down, conditionable, specific] match; N200 [bottom-up, unconditionable, nonspecific] mismatch
image p267fig06.14 Feedback from object surfaces to object boundaries uses surface contours. This feedback assures complementary consistency and enables figure-ground separation. A corollary discharge of the surface contours can be used to compite salient object feature positions.
|| Perceptual consistency and figure-ground separation.
image p314fig08.34 The VISTARS model for visually-based spatial navigation. It uses the Motion BCS as a front end and feeds it output signals into two computationally complementary cortical processing streams for computing optic flow and target tracking information.
|| VISTARS navigation model (Browning, Grossberg, Mingolia 2009). Use FORMOTION model as front end for higher level navigational circuits: input natural image sequences -> estimate heading (MT+)-MSTd -> additive processing -> estimate object position (MT-)-MSTv direction and speed subtractive processing -> Complementary Computing. [optic flow navigation, object tracking]
image p523fig14.03 (a) The MOTIVATOR neural model generalizes CogEM by also including the basal ganglia. It can hereby explain and simulate complementary functions of the amygdala and basal ganglia (SNc) during conditioning and learned performance. The basal ganglia generate Now Print signals in response to unexpected rewards. These signals modulate learning of new associations in many brain regions. The amygdala supports motivated attention to trigger actions that are expected to occur in response to conditioned or unconditioned stimuli. Object Categories represent visual or gustatory inputs in anterior inferotemporal (ITA) and rhinal (RHIN) cortices, respectively. Value Categories represent the value of anticipated outcomes on the basis of hunger and satiety inputs, in amygdala (AMYG) and lateral hypothalamus (LH). Object-Value Categories resolve the value of competing perceptual stimuli in medial (MORB) and lateral (ORB) orbitofrontal cortex. The Reward Expectation Filter detects the omission or delivery of rewards using a circuit that spans ventral striatum (VS), ventral pallidum (VP), striosomal delay (SD) cells in the ventral striatum, the pedunculopontine nucleus (PPTN) and midbrain dopaminergic neurons of the substantia nigra pars compacta/ventral tegmental area (SNc/VTA). The circuit that processes CS-related visual information (ITA, AMTG, ORB) operates in parallel with a circuit that processes US-related visual and gustatory information (RHIN, AMYG, MORB). (b) Reciprocal adaptive connections between hypothalamus and amygdala enable amygdala cells to become learned value categories. The bottom region represents hypothalmic cells, which receive converging taste and metabolite inputs whereby they become taste-drive cells. Bottom-up signals from activity patterns across these cells activate competing value category, or US Value Representations, in the amygdala. A winning value category learns to respond selectively to specific combinations of taste-drive activity patterns and sends adaptive top-down priming signals back to the taste-drive cells that activated it. CS-activated conditioned reinforcer signals are also associatively linked to value categories. Adpative connections end in (approximately) hemidiscs. See the text for details.
||
image p548fig15.16 Homologous recognition learning and reinforcement learning macrocicuits enable adaptively timed conditioning in the reinforcement learning circuit to increase inhibition of the orienting system at times when a mismatch in the recognition system would have reduced inhibition of it.
|| Homolog between ART and CogEM model, complementary systems. [Recognition, Reinforcement] learning vs [Attentional, Orienting] system. Reinforcement: timing, drive representation.
Grossberg 2021 p040c2h0.80 sub-section: Why did consciousness evolve? In particular, the book will show how and why multiple processing stages are needed before the brain can construct a complete and stable enough representation of the information in the world with which to control effective behaviours. This happens because the sensory signals that our brains process are often noisy and ambiguous, as are the representations that our brains form in response to them at the earliest processing stages. Complementary computing and hierarchical resolution of uncertainty overcome these problems until perceptual representations that are sufficiently complete, context-sensitive, and stable can be formed. The brain regions where these representations are completed are different for seeing, hearing, feeling, and knowing. But then how do our brains select these representations? My proposed answer is: A resonant state is generated that selectively "lights up" these representations and thereby renders them conscious. These conscious representations can then be used to trigger effective behaviours.

Consciousness hereby enables our brains to prevent the noisy and ambiguous information that is computed at earlier processing stages from triggering actions that could lead to disastrous consequences. Conscious states thus provide an extra degree of freedom whereby the brain ensures that its interactions with the environment, whether internal or external, are effective as possible, given the information at hand. These conclusions will be supported by theoretical analysis throughout the book that enable explanations of many psychological and neurobiological data about normal and clinical behaviours that have no other mechanistic explanations at the present time.

laminar computing

grepStr = laminar

p353 Chapter 10 Laminar computing by cerebral cortex - Towards a unified theory of biologucal and artificial intelligence
p000XIVsec From Laminar Computing to neuromorphic chips
p000XVIIIsec New computational paradigms: Laminar Computing and Complementary Computing
p029sec Two revolutionary paradigms: [complementary, laminar] computing
p031sec Laminar neocortical circuits to represent higher-order biological intelligence
p162sec Analogue coherence and the laminar cortical architecture of visual cortex
p163sec Laminar models of vision, speech, and cognition: A universal design for intelligence?
p229sec SMART: A laminar cortical hierarchy of spiking neurons
p245sec Self-normalizing laminar cortical circuits balance excitation and inhibition
Chapter 10 - Laminar computing by cerebral cortex
p353sec Why does the entire cerebral cortex use variations of a canonical laminar circuit?
p356sec Three basic properties of Laminar Computing
p356sec Laminar Computing unifies oposing design constraints
p358sec Intracortical but interlaminar feedback also carries out the ART Matching Rule
p358sec Laminar mechanisms of preattentive perceptual grouping
p363sec Laminar mechanisms of attention, development, and learning
p369sec From non-laminar models of 2D vision to laminar cortical models of 3D vision
p443sec LIST PARSE: A laminar model of variable-length sequences
p455sec Phonemic restoration in a laminar cortical model of speech perception
Chapter 10 - Laminar computing by cerebral cortex
image p030fig01.20 A schematic cross-section of a slice of laminar neocortex whose cells are organized in a characteristic way in six layers, which themselves may be organized into distinct sublaminae. The computational paradigm of Laminar Computing attempts to show how different parts of neocortex can represent and control very different kinds of behavior - including vision, speech, can cognition - using specializations of the same canonical laminar cortical design.
|| Projection fibres: Cortico[spinal, bulbar, pontine, striate, reticulat, etc]; Thalamocortical fibres; Diffuse cortical afferent fibres: [nonspecific thalamocortical, Cholinergic, Monoaminergic]; Corticocortical efferents; Projection [cell, fibre]; Corticocortical efferent terminals.
image p141fig04.19 A laminar cortical circuit for computing binocular disparities in layer 3B of V1 at binocular simple cells. These cells add positionally disparate inputes from like polarized monocular simple cells (layer 4 of V1). Binocular simple cells at each position that is sensitive to opposite polarities then add their outputs at complex cells in layer 2/3. Chapter 10 will explain how these laminar circuits work in greater detail.
|| Laminar cortical circuit for complex cells. [left, right] eye.
V1 layer description
2/3A complex cells
3B binocular simple cells
4 monocular simple cells
image p163fig04.39 A schematic of the LAMINART model that explains key aspects of laminar visual cortical anatomy and dynamics. LGN -> V1 [6, 4, 2/3] -> V2 [6, 4, 2/3]
|| p163c1h0.6 "... The first article abount laminar computing ... proposed how the laminar cortical model could process 2D pictures using bottom-up filtering and horizontal bipole grouping interactions (Grossbeerg, Mingolla, Ross 1997). In 1999, I was able to extend the model to also include top-down circuits for expectation and attention (Grossberg 1999)(right panel). Such a synthesis of laminar bottom-up, horizontal, and top-down circuits is characteristic of the cerebral cortex (left panel). I called it LAMINART because it began to show how properties of Adaptive Resonance Theory, or ART, notably the ART prediction about how top-down expectations and attention work, and are realized by identical cortical cells and circuits. You can immediately see from the schematic laminar circuit diagram ... (right panel) that circuits in V2 seem to repeat circuits in V1, albeit with a larger spatial scale, despite the fact that V1 and V2 carry out different functions, How this anatomical similarity can coexist with functional diversity will be clarified in subsequent sections and chapters. It enable dfifferent kinds of biological intelligence to communicate seamlessly while carrying out their different psychological functions. ..."
image p174fig04.52 An example of how the 3D LAMINART model can transform the two monocular images of the random dot stereogream in the top row into the three depth-separated surfaces representations in the bottom row.
|| Stereogram surface percepts: surface lightnesses are segregated in depth (Fang and Grossberg 2009). [left, right] inputs, [far, fixation, near] planes. Contrast algorithms that just compute disparity matches and let computer code build the surface eg (Marr, Poggio, etal 1974).
image p182fig04.58 LAMINART model processing stage that are sufficient to explain many percepts of transparency, including those summarized in Figure 4.57.
|| [left, right] eye, [LGN, V1 [6, 4, 3B, 2/3 A], V2 [4, 2/3]], [mo, bi]nocular cart [simple, complex] cells, [excita, inhibi]tory cart [connection, cell]s.
image p230fig05.38 The Synchronous Matching ART, or SMART, model includes spiking neurons in a laminar cortical hierarchy. I developed SMART with my PhD student Massimiliano Versace. By unlumping LAMINART to include spiking neurons, finer details of neurodynamics, such as the existence of faster gamma oscillations during good enough matches, and slower beta oscillations during bad enough mismatches, could be shown as emergent properties of network interactions.
|| Second order thalamus -> specific thalamic nucleus -> Thalamic reticulate nucleus -> neocortical laminar circuit [6ll, 6l, 5, 2/3, 1] -> Higher order cortex. Similar for First order thalamus -> First order cortex, with interconnection to Second order, nonspecific thalamic nucleus
image p231fig05.39 The SMART hypothesis testing and learning cycle predicts that vigilance increases when a mismatch in subcortical regions like the nonspecific thanlamus activates the nucleus basalis of Meynert which, in turn, broadcasts a burst of the neurotransmitter acetylcholine, or ACh, to deeper cortical layers. Due to the way in which LAMINART proposes that cortical matching and mismatching occurs, this ACh burst can increase vigilance and thereby trigger a memory search. See the text for details.
|| [BU input, [, non]specific thalamic nucleus, thalamic reticulate nucleus, neocortical laminar circuit] cart [Arousal, Reset, Search, Vigilance]
image p232fig05.41 (a)-(c). The sequence of interlaminar events that SMART predicts during a mismatch reset. (d) Some of the compatible neurophysiological data.
|| Mismatch causes layer 5 dendritic spike that trigger reset. (a) Arousal causes increase in nonspecific thalamic nuclei firing rate and layer 5 dendritic and later somatic spikes (Karkum and Zhu 2002, Williams and Stuart 1999) (b) Layer 5 spikes reach layer 4 via layer 6i and inhibitory neurons (Lund and Boothe 1975, Gilbert and Wiesel 1979) (c) habituative neurotransmitters in layer 6i shift the balance of active cells in layer 4 (Grossberg 1972, 1976) (d) Dendritic stimulation fires layer 5 (Larkum and Zhu 2002) stimulation apical dendrites of nonspecific thalamus
image p246fig05.48 Microcircuits of the LAMINART model that I developed with Rajeev Raizada. See the text for details of how they integrate bottom-up adaptive filtering, horizontal bipole grouping, and top-down attentional matching that satisfied the ART Matching Rule.
||
image p248fig05.49 This circuit of the LAMINART model helps to explain properties of Up and Down states during slow wave sleep, and how disturbances in ACh dynamics can disrupt them.
||
Chapter 10 - Laminar computing by cerebral cortex
image p356fig10.03 Laminar computing achieves at least three basic properties of visual processing that have analogs in all biologically intelligent behaviors. These properties may be found in all cortical circuits in specialized form.
|| What does Laminar Computing achieve? 1. Self-stabilizing development and learning; 2. Seamless fusion of a) pre-attentive automatic bottom-up processing, b) attentive task-selective top-down processing; 3. Analog coherence: Solution of Binding Problem for perceptual grouping without loss of analog sensitivity. Even the earliest visual cortical stages carry out active adaptive information processing: [learn, group, attention]ing
image p357fig10.04 Laminar Computing achieves its properties by computing in a new way that sythesizes the best properties of feedforward and feedback interactions, analog and digital computations, and preattentive and attentive learning. The property of analog coherence enables coherent groupings and decisions to form without losing sensitivity to the amount of evidence that supports them.
|| Laminar Computing: a new way to compute. 1. Feedforward and feedback: a) Fast feedforward processing when data are unambiguous (eg Thorpe etal), b) slower feedback chooses among ambiguous alternatives [self-normalizing property, real-time probabiligy theory], c) A self-organizing system that trades certainty against speed: Goes beyond Bayesian models! 2. Analog and Digital: Analog Coherence combines the stability of digital with the sensitivity of analog. 3. Preattentive and Attentive Learning: Reconciles the differences of (eg) Helmholtz and Kanizsa, "A preattentive grouping is its own 'attentional' prime"
image p362fig10.11 Feedback between layer 2/3 to the layer 6-to-4-to-2/3 feedback loop chooses the strongest grouping in cases where there is more than one. If only one grouping exists, then the circuit can function very quickly in a feedforward manner. When multiple groupings exist, the cortex "runs as fast as it can" to select the one with the most evidence to support it using the self-normalizing inhibition in the layer 6-to-4 off-surround.
|| How is the final grouping selected? Folded feedback LGN-> 6-> 4-> 2/3. 1. Layer 2/3 groupings feed back into 6-to-4 on-center off-surround: a) direct layer 2/3 -to-6 path; b) can also go via layer 5 (Blasdel etal 1985; Kisvarday etal 1989). 2. Strongest grouping enhanced by its on-center. 3. Inputs to weaker groupings suppressed by off-surround. 4. Interlaminar feedback creates functional columns. Activities of conflicting groupings are reduced by self-normalizing inhibition, slowing processing; intracortical feedback selects and contrast-enhances the winning grouping, speeding processing.
image p363fig10.12 The same laminar circuit design repeats in V1 and V2, albeit with specializations that include longer horizontal grouping axoms and figure-ground separation interactions.
|| V2 repeats V1 circuitry at larger spatial scale, LGN-> V1[6,4,2/3]-> V2[6,4,2/3]. V2 layer 2/3 horizontal axons longer-range than in V1 (Amir etal 1993). Therefore, longer-range groupings can form in V2 (Von der Heydt etal 1984)
image p375fig11.07 The 3D LAMINART model uses both monocular and binocular simple cells to binocularly fuse like image contrasts. The remainder of the model generates 3D boundary and surface representations of multiple kinds of experiments as well as of natural scenes.
|| 3D LAMINART model (Cao, Grossberg 2005). [left, right] eye cart [LGN, V1 blob [4->3B->2/3A], V2 thin stripe [4->2/3A], V4]. V1 blob [V1-4 monocular, V1 interior binocular] simple cells. [complex, simple, inhibitory] cells, on-center off-surround
image p376fig11.10 The 3D LAMINART model shows how the disparity filter can be integrated into the circuit that completes 3D boundary representations using bipole grouping cells. It also explains how surface contours can strengthen boundaries that succeed in generating closed filling-in domains.
|| 3D LAMINART model (Cao, Grossberg 2005). [left, right] eye cart [LGN, V1 blob [4->3B->2/3A] surface contour, V2 thin stripe (monocular surface) [4->2/3A], V2 interior [disynaptic inhibitory interneurons, bipole grouping cells, disparity filter, V4 binocular surface]. [complex, simple, inhibitory] cells, on-center off-surround
image p378fig11.12 How monocular and binocular information are combined in V1 and V2 in the 3D LAMINART model.
|| Model utilizes monocular information. [left, right] eye cart V1-[4 monocular simple, 3B binocular simple, complex2/3A [mo,bi]nocular] cells, V2-4 binocular complex cells. black = monocular cells, blue = binocular cells. In V2, monocular inputs add to binocular inputs along the line of sight and contribute to depth perception.
image p379fig11.13 How the 3D LAMINART model explains DaVinci stereopsis. All the stages of boundary and surface formation are color coded to clarify their explanation. Although each mechanism is very simple, when all of them act together, the correct depthful surface representation is generated. See the text for details.
|| DaVinci stereopsis (Nakayama, Shimojo 1990). An emergent property of the previous simple mechanisms working together. [V1 binocular boundaries -> V2 initial boundaries -> V2 final boundaries -> V4 surface] pair [Binocular match: boundaries of thick bar -> Add monocular boundaries along lines-of-sight -> Line-of-sight inhibition kills weaker vertical boundaries -> 3D surface percept not just a disparity match!] pair [Binocular match: right edge of thin and thick bars -> Strongest boundaries: binocular and monocular boundaries add -> Vertical boundaries from monocular left edge of thin bar survive -> Filling-in contained by connected boundaries]. cart [very near, near, fixation plane, far, very far]
image p380fig11.14 The model explanation of DaVinci stereopsis when the input stimuli have opposite contrast polarities.
|| Polarity-reversed Da Vinci stereopsis (Nakayama, Shimojo 1990). Same explanation! (... as Figure 11.13 ...) [V1 binocular boundaries -> V2 initial boundaries -> V2 final boundaries -> V4 surface] cart [very near, near, fixation plane, far, very far]
image p395fig11.34 A comparison of the properties of other rivalry models with those of the 3D LAMINART model (surrounded by red border). Significantly, only 3D LAMINART explains both stable vision and rivalry (green border).
|| Comparison of rivalry models
image p401fig11.41 The 3D LAMINART model proposes how angle cells and disparity-gradient interact through learning to generate 3D representations of slanted objects.
|| 3D LAMINART model. [LGN, V1, V2, V4] Four key additions: 1. Angle cells - tuned to various angles; 2. Disparity-gradient cells - tuned to disparity gradients in the image; 3. weights from [angle to disparity-gradient] cells - learned while viewing 3D image; Colinear grouping between [angle to disparity-gradient] cells - disambiguates ambiguous groupings.
image p402fig11.44 3D scenic reconstruction of the image in Figure 11.43 by the 3D LAMINART model.
|| Disparity [5, 6, 8, 10, 11, 14]: images of objects in common depth planes
image p436fig12.30 The conscious ARTWORD, or cARTWORD, laminar cortical speech model simulates how future context can disambiguate noisy past speech sounds in such a way that the completed percept is consciously heard to proceed from past to future as a feature-item-list resonant wave propagates through time.
|| cARTWORD: Laminar cortical model macrocircuit (Grossberg, Kazerounian 2011) Simulates PHONEMIC RESTORATION: Cognitive Working Memory (processed item sequences) - [Excitatory-> inhibitory-> habituative-> adaptive filter-> adaptive filter-> adaptive filter with depletable synapse-> Acoustic [item, feature]
image p444fig12.42 The LIST PARSE laminar cortical model of working memory and list chunking that I published with Lance Pearson in 2008 simulated the Averbeck etal data in Figure 12.41, as in the left column of the figure. It also simulated cognitive data about working memory storage by human subjects. See the text for details.
|| LIST PARSE: Laminar cortical model of working memory and list chunking (Grossberg, Pearson 2008). Simulates data about: [immediate, delayed, continuous] distractor free recall; immediate serial recall; and variable-speed sequential perfomance of motor acts. [velocity, acceleration] vs time (ms) from recall cue.
image p445fig12.43 The LIST PARSE laminar cortical Cognitive Working Memory circuit, that is proposed to occur in ventrolateral prefrontal cortex, is homologous to the LAMINART circuit circuit that models aspects of how visual cortex sees. The Motor Working Memory, VITE Trajectory Generator, and Variable-Rate Volitional Control circuits model how other brain regions, including dorsolateral prefrontal cortex, motor cortex, cerebellum, and basal ganglia, interact with the Cognitive Working Memory to control working memory storage and variable-rate performance of item sequences.
|| List parse circuit diagram. Connectivity convention. sequence chunks [<- BU filter, TD expectation ->] working memory. Working memory and sequence chunking circuit is homologous to visual LAMINART circuit!
image p498fig13.22 (left column, top row) Adaptive filtering and conditioned arousal are both needed to regulate what cues can learn to activate particular space-time patterns. These developments lead inexorably to basic cognitive abilities, as embodied in the 3D LAMINART models for 3D vision and figure-ground perception (Chapter 11) and the 3D ARTSCAN SEARCH model for invariant object learning, recognition, and 3D search (Chapter 6). (right column, top row) Conditioned arousal enables only emotionally important cues to activate a motivationally relevant space-time pattern. (bottom row) Conditioned arousal and drive representations arise naturally from the unlumping of avalanche circuits to make them selective to motivationally important cues. The MOTIVATOR model is a natural outcome of this unlumping process (this chapter).
|| (top) Adaptive filtering and Conditioned arousal. Towards Cognition: need to filter inputs to the command cell. Towards Emotion: important signals turn arousal ON and OFF. (bottom) Conditioned arousal and Drive representations. Competition between conditioned arousal sources at drive representations, eg amygdala.
p402fig11.44 Howell: amazing results from the 3D LAMINART model, Disparity [5, 6, 8, 10, 11, 14]: images of objects in common depth planes. But could simple parallax processing produce much of the same?
Grossberg: 3D scenic reconstruction of the image in Figure 11.43 by the 3D LAMINART model. || Disparity [5, 6, 8, 10, 11, 14]: images of objects in common depth planes

computing with cellular patterns

grepStr = cellular pattern

p070sec What is a cell: Computing with cellular patterns
p625sec A universal development code for biology: Computing with cellular patterns

family of ART.....base, linguistic

ART - Adaptive Resonance Theory

grepStr = ART

p012sec Cooperative-competitive-learning dynamics: ART in the brain and technology
p038sec How does object attention work? ART matching rule
p041sec The road to ART: Helmholtz, James, and Gregory
Credibility from non-[bio, psycho]logical applications of Grossberg's ART
As stated in [Grossberg 2021 p13c1h1.0] : "... This range of applications is possible because ART models embody general-purpose properties that are needed to solve the stability-plasticity dilemma in many different types of environments. In all these applications, insights about cooperative-competitive dynamics also play a critical role. ..."
add content of subSection "Multiple applications of ART to large-scale problems in engineering and technology"
p190sec Computational properties of the ART Matching Rule
p190sec ART matching: Expectation and attention during brightness perception
p193sec Neurobiological data for ART matching by corticogeniculate feedback
p194sec Neurobiological data for ART matching in visual, auditory, and somatosensory cortex
p195sec Mathematical form of the ART Matching Rule
p205sec How does ART stabilize learning?
p206sec ART cycle of hypothesis testing and category learning
p208sec ART links synchronous oscillations to attention and learning
p212sec A thought experiment that leads to ART
p222sec Multiple applications of ART to large-scale problems in engineering and technology
p225sec Catastrophic forgetting without the top-down ART Matching Rule
p227sec ART direct access solves the local minimum problem
p228sec Learning of fuzzy IF-THEN rules by a self-organizing ART production system
p228sec ART provides autonomous soluutions for Explanable AI
p240sec Converting algebraic exemplar models into dynamical ART prototype models
p241sec Explaining human categorization data with ART: Learning rules-plus-exceptions
p246sec Self-normalizing inhibition during attentional priming with the ART Matching Rule
p249sec Many kinds of psychological and neurobiological data have been explained by ART
p315sec ART again: Choosing an object's motion direction and speed
p315sec ART again: Dynamically stabilizing learned directional cells also solves the aperture problem
p319sec ART Matching Rule explains induced motion
p350sec Solving the aperture problem for heading in natural scenes using the ART Matching Rule
p358sec Intracortical but interlaminar feedback also carries out the ART Matching Rule
p365sec ART Matching Rule in multiple cortical modalities
p451sec Top-down attentive matching during speech and laguage using the ART Matching Rule
p474sec Explaining chunk data from the tachistoscopic condition using ART
p474sec Explaining data from the reaction time condition using ART: List item error trade-off
p579sec Spatial navigation uses a self-stabilizing ART spatial category learning system
p600sec An ART spatial category learning system: The hippocampus IS a cognitive map!
image p038fig01.25 The ART Matching Rule stabilizes real time learning using a [top-down, modulatory on-center, off-surround] network. Object attention is realized by such a network. See text for additional discussion.
|| ART Matching Rule [volition, categories, features]. [one, two] against one.
image p163fig04.39 A schematic of the LAMINART model that explains key aspects of laminar visual cortical anatomy and dynamics. LGN -> V1 [6, 4, 2/3] -> V2 [6, 4, 2/3]
|| p163c1h0.6 "... The first article abount laminar computing ... proposed how the laminar cortical model could process 2D pictures using bottom-up filtering and horizontal bipole grouping interactions (Grossbeerg, Mingolla, Ross 1997). In 1999, I was able to extend the model to also include top-down circuits for expectation and attention (Grossberg 1999)(right panel). Such a synthesis of laminar bottom-up, horizontal, and top-down circuits is characteristic of the cerebral cortex (left panel). I called it LAMINART because it began to show how properties of Adaptive Resonance Theory, or ART, notably the ART prediction about how top-down expectations and attention work, and are realized by identical cortical cells and circuits. You can immediately see from the schematic laminar circuit diagram ... (right panel) that circuits in V2 seem to repeat circuits in V1, albeit with a larger spatial scale, despite the fact that V1 and V2 carry out different functions, How this anatomical similarity can coexist with functional diversity will be clarified in subsequent sections and chapters. It enable dfifferent kinds of biological intelligence to communicate seamlessly while carrying out their different psychological functions. ..."
image p192fig05.06 Bottom-up and top-down circuits between the LGN and cortical area V1. The top-down circuits obey the ART Matching Rule for matching with bottom-up input patterns and focussing attention on expected critical features.
|| Model V1-LGN circuits, version [1, 2]. retina -> LGN relay cells -> interneurons -> cortex [simple, endstopped] cells -> cortex complex cells
image p200fig05.13 Instar and outstar learning are often used to learn the adaptive weights in the bottom-up filters and top-down expectations that occur in ART. The ART Matching Rule for object attention enables top-down expectations to select, amplify, and synchronize expected patterns of critical features, while suppressing unexpected features.
|| Expectations focus attention: feature pattern (STM), Bottom-Up adaptive filter (LTM), Category (STM), competition, Top-Down expectation (LTM); ART Matching Rule: STM before top-down matching, STM after top-down matching (attention!)
image p207fig05.19 The ART hypothesis testing and learning cycle. See the text for details about how the attentional system and orienting system interact in order to incorporate learning of novel categories into the corpus of already learned categories without causing catastophic forgetting.
||
image p211fig05.21 Sequences of P120, N200, and P300 event-related potentials occur during oddball learning EEG experiments under conditions that ART predicted should occur during sequences of mismatch, arousal, and STM reset events, respectively.
|| ERP support for mismatch-mediated reset: event-related potentials: human scalp potentials. ART predicted correlated sequences of P120-N200-P300 Event Related Potentials during oddball learning. P120 mismatch; N200 arousal/novelty; P300 STM reset. Confirmed in (Banquet and Grossberg 1987)
image p215fig05.28 How a mismatch between bottom-up and top-down input patterns can trigger activation of the orienting system A and, with it, a burst of nonspecific arousal to the category level.
|| Mismatch -> inhibition -> arousal -> reset. BU input orienting arousal, BU+TD mismatch arousal and reset. ART Matching Rule: TD mismatch can suppress a part of F1 STM pattern, F2 is reset if degree of match < vigilance
image p221fig05.31 A system like Fuzzy ARTMAP can learn to associate learned categories in one ART network with learned categories in a second ART network. Because both bottom-up and top-down interactions occur in both networks, a bottom-up input pattern to the first ART network can learn to generate a top-down output pattern from the second ART network.
|| Fuzzy ARTMAP. Match tracking realizes minimax learning principle: vigilence increases to just above the match ratio of prototype / exemplar, thereby triggering search
image p226fig05.35 I had shown in 1976 how a competitive learning or self-organizing map model could undergo catastrophic forgetting if the input environment was sufficiently dense and nonstationary, as illustrated by Figure 5.18. Later work with Gail Carpenter showed how, if the ART Matching Rule was shut off, repeating just four input patterns in the correct order could also casue catastrophic forgetting by causing superset recoding, as illustrated in Figure 5.36.
|| Code instability input sequences. D C A; B A; B C = ; |D|<|B|<|C|; where |E| is the number of features in the set E. Any set of input vectors that satisfy the above conditions will lead to unstable coding if they are periodically presented in the order ABCAD and the top-down ART Matching Rule is shut off.
image p226fig05.36 Column (a) shows catastrophic forgetting when the ART Matching Rule is not operative. It is due to superset recoding. Column (b) shows how category learning quickly stabilizes when the ART Martching Rule is restored.
|| Stabel and unstable learning, superset recoding
image p228fig05.37 A macrocircuit of the neurotrophic Spectrally Timed ART, or nSTART, model. I developed nSTART with my PhD student Daniel Franklin. It proposes how adaptively timed learning in the hippocampus, bolstered by Brain Derived Neurotrophic Factor, or BDNF, helps to ensure normal memory consolidation.
|| habituative gates, CS, US, Thalamus (sensory cortex, category learning, conditioned reinforcer learning, adaptively time learning and BDNF), Amygdala (incentive motivation learning), Hippocampus (BDNF), Prefrontal Cortex (attention), Pontine nuclei, Cerebellum (adaptively timed motor learning)
image p230fig05.38 The Synchronous Matching ART, or SMART, model includes spiking neurons in a laminar cortical hierarchy. I developed SMART with my PhD student Massimiliano Versace. By unlumping LAMINART to include spiking neurons, finer details of neurodynamics, such as the existence of faster gamma oscillations during good enough matches, and slower beta oscillations during bad enough mismatches, could be shown as emergent properties of network interactions.
|| Second order thalamus -> specific thalamic nucleus -> Thalamic reticulate nucleus -> neocortical laminar circuit [6ll, 6l, 5, 2/3, 1] -> Higher order cortex. Similar for First order thalamus -> First order cortex, with interconnection to Second order, nonspecific thalamic nucleus
image p240fig05.44 When an algebraic exemplar model is realized using only local computations, it starts looking like an ART prototype model.
|| How does the model know which exemplars are in category A? BU-TD learning. How does a NOVEL test item access category A?
image p241fig05.45 The 5-4 category structure is one example of how an ART network learns the same kinds of categories as human learners. See the text for details.
|| 5-4 Category structure. A1-A5: closer to the (1 1 1 1) prototype; B1-B4: closer to the (0 0 0 0) prototype
image p246fig05.48 Microcircuits of the LAMINART model that I developed with Rajeev Raizada. See the text for details of how they integrate bottom-up adaptive filtering, horizontal bipole grouping, and top-down attentional matching that satisfied the ART Matching Rule.
||
image p254fig06.03 These interactions of the ARTSCAN Search model enable it to learn to recognize and name invariant object categories. Interactions between spatial attention in the Where cortical stream, via surface-shroud resonances, and object attention in the What cortical stream, that obeys the ART Matching Rule, coordinate these learning, recognition, and naming processes.
|| Retinal image -> On & OFF cell contrast normalization (retina/LGN) -> polarity [, in]sensitive contrast enhancement (V1) -> object [boundary (V2), surface (V2/V4)] -> surface contour (V2); What stream categories: volition control (BG). object boundary (V2) <-> view (ITp) <-> view integrator (ITp) <-> object (ITa) <-> [object-value (ORB), value (Amyg)] <-> name (PFC)
image p315fig08.35 The output signals from the directional grouping network obey the ART Matching Rule. They thereby select consistent motion directional signals while suppressing inconsistent ones, and do not distort that the spared cells code. The aperture problem is hereby solved by the same mechanism that dynamically stabilizes the learning of directional grouping cells.
|| How to select correct direction and preserve speed estimates? Prediction: Feedback from MSTv to MT- obeys ART Matching Rule; Top-down, modulatory on-center, off-surround network (Grossberg 1976, 1980; Carpenter, Grossberg 1987, 1991); Explains how directional grouping network can stably develop and how top-down directional attention can work. (Cavanough 1992; Goner etal 1986; Sekuer, Ball 1977; Stelmach etal 1994). Directional grouping network (MSTv) <-> Directional long-range filter (MT). Modulatory on-center selects chosen direction and preserves speed. Off-surround inhibits incompatible directions.
image p316fig08.36 How the directional grouping network, notably properties of the ART Matching Rule, enables a small set of amplified feature tracking signals at the ends of a line to select consistent directions in the line interior, while suppressing inconsistent directions.
|| Motion capture by directional grouping feedback. Directional grouping network (MSTv) <-> Directional long-range filter (MT). It takes longer to capture ambiguous motion signals in the line interior as the length of the line increases cf (Castet etal 1993)
image p354fig10.01 The lamiar cortical circuit that realizes how we pay attention to an object sends signals from layer 6 of a higher cortical level to layer 6 of a lower cortical level and then back up to layer 4. This "folded feedback" circuit realizes a top-down, modulatory on-center, off-surround circuit that realizes the ART Matching Rule.
|| Top-down attention and folded feedback. Attentional signals also feed back into 6-to-4 on-center off-surround. 1-to-5-to-6 feedback path: Macaque (Lund, Booth 1975) cat (Gilbert, Wiesel 1979). V2-to-V1 feedback is on-center off-surround and affects layer 6 of V1 the most (Bullier etal 1996; Sandell, Schiller 1982). Attended stimuli enhanced, ignored stimuli suppressed. This circuit supports the predicted ART Matching Rule! [LGN, V[1,2][6->1]]
image p360fig10.08 The bottom-up on-center off-surround from LGN-to-6-to-4 has a modulatory on-center because of its role in realizing the ART Matching Rule and, with it, the ability of the cortex to dynamically stabilize its learned memories.
|| Modulation of priming by 6-to-4 on-center (Stratford etal 1996; Callaway 1998). On-center 6-to-4 excitation is inhibited down to being modulatory (priming, subthreshold). On-center 6-to-4 excitation cannot activate layer 4 on its own. Clarifies need for direct path. Prediction: plays key role in stable grouping, development and learning. ART Matching Rule!
image p419fig12.17 The auditory continuity illusion illustrates the ART Matching Rule at the level of auditory streaming. Its "backwards in time" effect of future context on past conscious perception is a signature of resonance.
|| Auditory continuity illusion. input, percept. Backwards in time - How does a future sound let past sound continue through noise? Resonance! - It takes a while to kick in. After it starts, a future tone can maintain it much more quickly. Why does this not happen if there is no noise? - ART Matching Rule! TD harmonic filter is maodulatory without BU input. It cannot create something out of nothing.
image p420fig12.18 The ARTSTREAM model explains and simulates the auditory continuity illusion as an example of a spectral-pitch resonance. Interactions of ART Matching Rule and asymmetric competition mechanisms in cortical strip maps explain how the tone selects the consistent frequency from the noise in its own stream while separating the rest of the noise into another stream.
|| ARTSTREAM model (Grossberg 1999; Grossberg, Govindarajan, Wyse, Cohen 2004). SPINET. Frequency and pitch strips. Bottom Up (BU) harmonic sieve. Top Down (TD) harmonic ART matching. Exclusive allocation. Learn pitch categories based on early harmonic processing. A stream is a Spectral-Pitch Resonance!
image p473fig12.69 Error rate and mean reaction time (RT) data from the lexical decision experiments of (Schvanevelt, McDonald 1981). ART Matching Rule properties explain these data in (Grossberg, Stone 1986).
|| (left) Error rate vs type of prime [R, N, U], [non,] word. (right) Mean RT (msec) vs type of prime [R, N, U], [non,] word.
image p483fig13.02 The object-value categories in the orbitofrontal cortex require converging specific inputs from the sensory cortex and nonspecific incentive motivational inputs from the amygdala in order to fire. When the orbitofrontal cortex fires, it can deliver top-down ART Matching Rule priming signals to the sensory cortical area by which it was activated, thereby helping to choose the active recognition categories there that have the most emotional support, while suppressing others, leading to attentional blocking of irrelevant cues.
|| Cognitive-Emotional-Motor (CogEM) model. Drive-> amygdala incentive motivational learning-> orbitofrontal cortex- need converging cue and incentive iputs to fire <-> sensory cortex- conditioned renforcer learning-> amygdala. CS-> sensory cortex. Motivated attention closes the cognitive-emotional feedback loop, focuses on relevant cues, and causes blocking of irrelevant cues.
image p548fig15.16 Homologous recognition learning and reinforcement learning macrocicuits enable adaptively timed conditioning in the reinforcement learning circuit to increase inhibition of the orienting system at times when a mismatch in the recognition system would have reduced inhibition of it.
|| Homolog between ART and CogEM model, complementary systems. [Recognition, Reinforcement] learning vs [Attentional, Orienting] system. Reinforcement: timing, drive representation.
image p600fig16.36 The entorhinal-hipppocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories. See the text for details.
|| Entorhinal-hippocampal interactions as an ART system. Hippocampal place cells as spatial categories. Angular head velocity-> head direction cells-> stripe cells- small scale 1D periodic code (ECIII) SOM-> grid cells- small scale 2D periodic code (ECII) SOM-> place cells- larger scale spatial map (DG/CA3)-> place cells (CA1)-> conjunctive-coding cells (EC V/VI)-> top-down feedback back to stripe cells- small scale 1D periodic code (ECIII). stripe cells- small scale 1D periodic code (ECIII)-> place cells (CA1).
image p613fig16.43 The main visual form and motion processing stream mechanisms of SOVEREIGN, many of them described at length in previous chapters.
|| Render 3-D scene (R3DS), figure-ground separation (FGS), log-polar transform (LPT), Gaussian coarse-coding (GCC), Invariant visual target map (IVTM), What Fuzzy ART (WhatFuzz), body spatial coordinates (BSC), where reactive visual TPV storage (WRVTS), Directional transient cell network (DTCN), Motion direction hemifild map (MDHM), Hemifiled left/right scoring (HLRS), reactive visual control signal (RVCS), Parvo/Magno/Erg competition (PMEC), Approach and Orient GOp (AOGp), GOm (GOm). R3DS [parvo-> FGS, magno-> DTCN], FGS-> [LPT, WRVTS], LPT-> GCC-> IVTM-> WhatFuzz, BSC-> [RVTS, PMEC], PMEC-> [gateRVTS-> RVTS, gateRVCS-> RVCS], DTCN-> MDHM-> HLRS, HLRS-> [PMEC, RVCS], AOGp-> gateRVTS, GOm-> gateRVCS.
Grossberg 2021 p045c2h0.85 ART's predicted links between attention and synchronous oscillations have been experimentally supported by experiments reported by (edited list) :
- (Timothy Bushman, Earl Miller 2007)
- (Andreas Engel, Pascal Fries, Wolf Singer 2001)
- (Georgia Gregoriou, Stephen Gotts, Huilhui Zhou, Robert Desimone 2009)
- (Georgia Gregoriou, Robert Desimone, Anfrew Rossi, Leslie Ungerleider 2014)
- (Daniel Pollen 1999)
Grossberg 2021 p046c1h0.05 Of particular interest are data supporting ART's predicted link between synchronous oscillations and consciousness that neuroscientists such as ;
- (Victor Lamme 2006)
- (Rudolfo Llinas, Ribrary, Contreras, Pedroarena 1998)
- (Wolf Singer 1998)
- and their colleagues
have reported.
Grossberg 2021 p229c1h0.50 (Scott Brincat, Earl Miller 2015) reported neurophysiological data that support the ART distinction between category learning within the attentional system, that includes prefrontal cortex (PFC), and the orienting system, that includes the hippocampus (HPC). ... These results contribute to a long history of neurobiological experiments that have implicated the hippocampus in mismatch processing, notably the processing of novel events, including experiments and theoretical concepts about habituation of novelty in the hippocampus as learning proceeds. ... The ART hypothesis testing and category learning cycle hereby clarifies and unifies an important series of psychophysiological experiments that go back at least 50 years.
Grossberg 2021 p048c1h0.25 In 2012 and 2015, Tononi further develops postulates for his Integrated Information Theory (IIT) for physical systems that include consciousness (Tononi 2012, 2015). These postulates are intrinsic existence, compositionality, information, integration, and exclusion, These postulates summarize some basic facts about consciousness, but do not explain them. p048c1h0.85 Both Dehaene and Tononi used the word "information" as a critical component of their hypothesis. But what is "information"? The scientific concept of "information" in the mathematical sense of Information Theory was defined and characterized in 1948 by the great American mathematician, electrical engineer, and cryptographer Claude Shannon (Shannon 1948). ... In order to even discuss what "information" is requires that a set of states exist whose information can be computed, and that fixed probabilities exist for transitions between these states. In contrast, the brain is a self-organizing system that continuously creates new states through development and learning, and whose probability structure is continually changing along with them. Without a theory that explains how their transition probabilities may change through time in response to changing environmental statistics, the classical concept of information is useless. How such states arise is a key explanatory target of ART, and is one reason why ART can offer a classification of the resonances that are proposed to embody specific conscious experiences.
Grossberg 2021 p204c2h0.30 In their 1990 article, Crick and Koch described two forms of consciousness : "a very fast form, linked to iconic memory...; and a slower one [wherein] an attentional mechanism transiently binds together all those neurons whose activity relates to the relevant features of a single visual object]". This conclusion was consistent with available results about ART at that time, but did not offer a linking hypothesis between brain dynamics and the perceptual, cognitive, and cognitive-emotional representations whose resonances support different conscious qualia. A great deal of additional experimental evidence for neural correlates of consciousness has been reported since 1990, but has typically led to theoretical conclusions that fail to make the crucial linking hypothesis between specific conscious pyschological qualia (eg Baars 2005; Dehaene 2014; Dennett 1991; Edelman, Tononi 2000; Koch etal 2016; Tononi 2004, 2015). ... Such claims are consistent with the analysis in this book, but they do not describe the underlying organizational principles, neural mechanisms, or brain representations that embody subjective conscious aspects of experience.
p184 Howell: grepStr 'conscious' - "... I claim that a common set of brain mechnisms controls all of these processes. Adaptive Resonance Theory, or ART, has been incrementally developed to explain what these mechanisms are, and how they work and interact, since I introduced it in 1976 [Grossberg, 1976a, 1976b] and it was incrementally developed in many articles to the present, notably with the help and leadership of Gail Carpenter, as I will elaborate on below. There are many aspects of these processes that are worth considering. For example, we need to understand between... ..." [Grossberg 2021 p184]

p190 Howell: [neural microcircuits, modal architectures] used in ART -

bottom-up filters	top-down expectations	purpose
instar learning	outstar learning	p200fig05.13 Expectations focus attention: [in, out]star often used to learn the adaptive weights. top-down expectations to select, amplify, and synchronize expected patterns of critical features, while suppressing unexpected features
LGN Lateral Geniculate Nucleus	V1 cortical area	p192fig05.06 focus attention on expected critical features
EC-III entorhinal stripe cells	CA1 hippocampal place cells	p600fig16.36 entorhinal-hippocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories
auditory orienting arousal	auditory category	p215fig05.28 How a mismatch between bottom-up and top-down patterns can trigger activation of the orienting system A and, with it, a burst of nonspecific arousal to the category level. ART Matching Rule: TD mismatch can suppress a part of F1 STM pattern, F2 is reset if degree of match < vigilance
auditory stream with/without [silence, noise] gaps	perceived sound continues?	p419fig12.17 The auditory continuity illusion: Backwards in time - How does a future sound let past sound continue through noise? Resonance!
visual perception, learning and recognition of visual object categories	motion perception, spatial representation and target tracking	p520fig14.02 pART predictive Adaptive Resonance Theory. Many adaptive synapses are bidirectional, thereby supporting synchronous resonant dynamics among multiple cortical regions. The output signals from the basal ganglia that regulate reinforcement learning and gating of multiple cortical areas are not shown. red - cognitive-emotional dynamics green - working memory dynamics black - see [bottom-up, top-down] lists
EEG with mismatch, arousal, and STM reset events	expected [P120, N200, P300] event-related potentials (ERPs)	p211fig05.21 Sequences of P120, N200, and P300 event-related potentials (ERPs) occur during oddball learning EEG experiments under conditions that ART predicted should occur during sequences of mismatch, arousal, and STM reset events, respectively.
Cognitive	Emotional	p541fig15.02 nSTART neurotrophic Spectrally Timed Adaptive Resonance Theory: Hippocampus can sustain a Cognitive-Emotional resonance: that can support "the feeling of what happens" and knowing what event caused that feeling. Hippocampus enables adaptively timed learning that can bridge a trace conditioning gap, or other temporal gap between Conditioned Stimuluus (CS) and Unconditioned Stimulus (US).

backgound colours in the table signify :

white	general microcircuit : a possible component of ART architecture
lime green	sensory perception [attention, expectation, learn]. Table includes [see, hear, !!must add touch example!!], no Grossberg [smell, taste] yet?
light blue	post-perceptual cognition?
pink	"the feeling of what happens" and knowing what event caused that feeling

p240fig05.44 Howell: grepStr 'local computations' what about TrNN basic unit as one of three questions for webSite? Hebbian comments elsewhere in book... :
Grossberg: When an algebraic exemplar model is realized using only local computations, it starts looking like an ART prototype model. || How does the model know which exemplars are in category A? BU-TD learning. How does a NOVEL test item access category A?
p419fig12.17 Howell: important indicator of resonance, which is a requirement for consciousness (but not all resonance is conscious)
Howell: grepStr 'auditory continuity' - DeLiang Wang "Auditory scene analysis"
Grossberg: The auditory continuity illusion illustrates the ART Matching Rule at the level of auditory streaming. Its "backwards in time" effect of future context on past conscious perception is a signature of resonance. ||
p??? Howell: grepStr 'Explainable AI' - Grossberg explains how ART easily leads to IF-THEN rules... somewhere in book

LAMINART vison, speech, cognition

grepStr = LAMINART

image p163fig04.39 A schematic of the LAMINART model that explains key aspects of laminar visual cortical anatomy and dynamics. LGN -> V1 [6, 4, 2/3] -> V2 [6, 4, 2/3]
|| p163c1h0.6 "... The first article abount laminar computing ... proposed how the laminar cortical model could process 2D pictures using bottom-up filtering and horizontal bipole grouping interactions (Grossbeerg, Mingolla, Ross 1997). In 1999, I was able to extend the model to also include top-down circuits for expectation and attention (Grossberg 1999)(right panel). Such a synthesis of laminar bottom-up, horizontal, and top-down circuits is characteristic of the cerebral cortex (left panel). I called it LAMINART because it began to show how properties of Adaptive Resonance Theory, or ART, notably the ART prediction about how top-down expectations and attention work, and are realized by identical cortical cells and circuits. You can immediately see from the schematic laminar circuit diagram ... (right panel) that circuits in V2 seem to repeat circuits in V1, albeit with a larger spatial scale, despite the fact that V1 and V2 carry out different functions, How this anatomical similarity can coexist with functional diversity will be clarified in subsequent sections and chapters. It enable dfifferent kinds of biological intelligence to communicate seamlessly while carrying out their different psychological functions. ..."
image p174fig04.52 An example of how the 3D LAMINART model can transform the two monocular images of the random dot stereogream in the top row into the three depth-separated surfaces representations in the bottom row.
|| Stereogram surface percepts: surface lightnesses are segregated in depth (Fang and Grossberg 2009). [left, right] inputs, [far, fixation, near] planes. Contrast algorithms that just compute disparity matches and let computer code build the surface eg (Marr, Poggio, etal 1974).
image p182fig04.58 LAMINART model processing stage that are sufficient to explain many percepts of transparency, including those summarized in Figure 4.57.
|| [left, right] eye, [LGN, V1 [6, 4, 3B, 2/3 A], V2 [4, 2/3]], [mo, bi]nocular cart [simple, complex] cells, [excita, inhibi]tory cart [connection, cell]s.
image p230fig05.38 The Synchronous Matching ART, or SMART, model includes spiking neurons in a laminar cortical hierarchy. I developed SMART with my PhD student Massimiliano Versace. By unlumping LAMINART to include spiking neurons, finer details of neurodynamics, such as the existence of faster gamma oscillations during good enough matches, and slower beta oscillations during bad enough mismatches, could be shown as emergent properties of network interactions.
|| Second order thalamus -> specific thalamic nucleus -> Thalamic reticulate nucleus -> neocortical laminar circuit [6ll, 6l, 5, 2/3, 1] -> Higher order cortex. Similar for First order thalamus -> First order cortex, with interconnection to Second order, nonspecific thalamic nucleus
image p231fig05.39 The SMART hypothesis testing and learning cycle predicts that vigilance increases when a mismatch in subcortical regions like the nonspecific thanlamus activates the nucleus basalis of Meynert which, in turn, broadcasts a burst of the neurotransmitter acetylcholine, or ACh, to deeper cortical layers. Due to the way in which LAMINART proposes that cortical matching and mismatching occurs, this ACh burst can increase vigilance and thereby trigger a memory search. See the text for details.
|| [BU input, [, non]specific thalamic nucleus, thalamic reticulate nucleus, neocortical laminar circuit] cart [Arousal, Reset, Search, Vigilance]
image p246fig05.48 Microcircuits of the LAMINART model that I developed with Rajeev Raizada. See the text for details of how they integrate bottom-up adaptive filtering, horizontal bipole grouping, and top-down attentional matching that satisfied the ART Matching Rule.
||
image p248fig05.49 This circuit of the LAMINART model helps to explain properties of Up and Down states during slow wave sleep, and how disturbances in ACh dynamics can disrupt them.
||
image p375fig11.07 The 3D LAMINART model uses both monocular and binocular simple cells to binocularly fuse like image contrasts. The remainder of the model generates 3D boundary and surface representations of multiple kinds of experiments as well as of natural scenes.
|| 3D LAMINART model (Cao, Grossberg 2005). [left, right] eye cart [LGN, V1 blob [4->3B->2/3A], V2 thin stripe [4->2/3A], V4]. V1 blob [V1-4 monocular, V1 interior binocular] simple cells. [complex, simple, inhibitory] cells, on-center off-surround
image p376fig11.10 The 3D LAMINART model shows how the disparity filter can be integrated into the circuit that completes 3D boundary representations using bipole grouping cells. It also explains how surface contours can strengthen boundaries that succeed in generating closed filling-in domains.
|| 3D LAMINART model (Cao, Grossberg 2005). [left, right] eye cart [LGN, V1 blob [4->3B->2/3A] surface contour, V2 thin stripe (monocular surface) [4->2/3A], V2 interior [disynaptic inhibitory interneurons, bipole grouping cells, disparity filter, V4 binocular surface]. [complex, simple, inhibitory] cells, on-center off-surround
image p378fig11.12 How monocular and binocular information are combined in V1 and V2 in the 3D LAMINART model.
|| Model utilizes monocular information. [left, right] eye cart V1-[4 monocular simple, 3B binocular simple, complex2/3A [mo,bi]nocular] cells, V2-4 binocular complex cells. black = monocular cells, blue = binocular cells. In V2, monocular inputs add to binocular inputs along the line of sight and contribute to depth perception.
image p379fig11.13 How the 3D LAMINART model explains DaVinci stereopsis. All the stages of boundary and surface formation are color coded to clarify their explanation. Although each mechanism is very simple, when all of them act together, the correct depthful surface representation is generated. See the text for details.
|| DaVinci stereopsis (Nakayama, Shimojo 1990). An emergent property of the previous simple mechanisms working together. [V1 binocular boundaries -> V2 initial boundaries -> V2 final boundaries -> V4 surface] pair [Binocular match: boundaries of thick bar -> Add monocular boundaries along lines-of-sight -> Line-of-sight inhibition kills weaker vertical boundaries -> 3D surface percept not just a disparity match!] pair [Binocular match: right edge of thin and thick bars -> Strongest boundaries: binocular and monocular boundaries add -> Vertical boundaries from monocular left edge of thin bar survive -> Filling-in contained by connected boundaries]. cart [very near, near, fixation plane, far, very far]
image p380fig11.14 The model explanation of DaVinci stereopsis when the input stimuli have opposite contrast polarities.
|| Polarity-reversed Da Vinci stereopsis (Nakayama, Shimojo 1990). Same explanation! (... as Figure 11.13 ...) [V1 binocular boundaries -> V2 initial boundaries -> V2 final boundaries -> V4 surface] cart [very near, near, fixation plane, far, very far]
image p395fig11.34 A comparison of the properties of other rivalry models with those of the 3D LAMINART model (surrounded by red border). Significantly, only 3D LAMINART explains both stable vision and rivalry (green border).
|| Comparison of rivalry models
image p401fig11.41 The 3D LAMINART model proposes how angle cells and disparity-gradient interact through learning to generate 3D representations of slanted objects.
|| 3D LAMINART model. [LGN, V1, V2, V4] Four key additions: 1. Angle cells - tuned to various angles; 2. Disparity-gradient cells - tuned to disparity gradients in the image; 3. weights from [angle to disparity-gradient] cells - learned while viewing 3D image; Colinear grouping between [angle to disparity-gradient] cells - disambiguates ambiguous groupings.
image p402fig11.44 3D scenic reconstruction of the image in Figure 11.43 by the 3D LAMINART model.
|| Disparity [5, 6, 8, 10, 11, 14]: images of objects in common depth planes
image p445fig12.43 The LIST PARSE laminar cortical Cognitive Working Memory circuit, that is proposed to occur in ventrolateral prefrontal cortex, is homologous to the LAMINART circuit circuit that models aspects of how visual cortex sees. The Motor Working Memory, VITE Trajectory Generator, and Variable-Rate Volitional Control circuits model how other brain regions, including dorsolateral prefrontal cortex, motor cortex, cerebellum, and basal ganglia, interact with the Cognitive Working Memory to control working memory storage and variable-rate performance of item sequences.
|| List parse circuit diagram. Connectivity convention. sequence chunks [<- BU filter, TD expectation ->] working memory. Working memory and sequence chunking circuit is homologous to visual LAMINART circuit!
image p498fig13.22 (left column, top row) Adaptive filtering and conditioned arousal are both needed to regulate what cues can learn to activate particular space-time patterns. These developments lead inexorably to basic cognitive abilities, as embodied in the 3D LAMINART models for 3D vision and figure-ground perception (Chapter 11) and the 3D ARTSCAN SEARCH model for invariant object learning, recognition, and 3D search (Chapter 6). (right column, top row) Conditioned arousal enables only emotionally important cues to activate a motivationally relevant space-time pattern. (bottom row) Conditioned arousal and drive representations arise naturally from the unlumping of avalanche circuits to make them selective to motivationally important cues. The MOTIVATOR model is a natural outcome of this unlumping process (this chapter).
|| (top) Adaptive filtering and Conditioned arousal. Towards Cognition: need to filter inputs to the command cell. Towards Emotion: important signals turn arousal ON and OFF. (bottom) Conditioned arousal and Drive representations. Competition between conditioned arousal sources at drive representations, eg amygdala.
p402fig11.44 Howell: amazing results from the 3D LAMINART model, Disparity [5, 6, 8, 10, 11, 14]: images of objects in common depth planes. But could simple parallax processing produce much of the same?
Grossberg: 3D scenic reconstruction of the image in Figure 11.43 by the 3D LAMINART model. || Disparity [5, 6, 8, 10, 11, 14]: images of objects in common depth planes

[, n]START learning & memory consolidation

grepStr = START|nSTART

p549sec START: Spectrally Timed Adaptive Resonance Theory
p552sec nSTART: neurotrophic Spectrally Timed Adaptive Resonance Theory
image p228fig05.37 A macrocircuit of the neurotrophic Spectrally Timed ART, or nSTART, model. I developed nSTART with my PhD student Daniel Franklin. It proposes how adaptively timed learning in the hippocampus, bolstered by Brain Derived Neurotrophic Factor, or BDNF, helps to ensure normal memory consolidation.
|| habituative gates, CS, US, Thalamus (sensory cortex, category learning, conditioned reinforcer learning, adaptively time learning and BDNF), Amygdala (incentive motivation learning), Hippocampus (BDNF), Prefrontal Cortex (attention), Pontine nuclei, Cerebellum (adaptively timed motor learning)
image p541fig15.02 The neurotrophic Spectrally Timed Adaptive Resonance Theory, or nSTART, model of (Franklin, Grossberg 2017) includes hippocampus to enable adaptively timed learning that can bridge a trace conditioning gap, or other temporal gap between CS and US.
|| Hippocampus can sustain a Cognitive-Emotional resonance: that can support "the feeling of what happens" and knowing what event caused that feeling. [CS, US] -> Sensory Cortex (SC) <- motivational attention <-> category learning -> Prefrontal Cortex (PFC). SC conditioned reinforcement learning-> Amygdala (cannot bridge the temporal gap) incentive motivational learning-> PFC. SC adaptively timer learning and BDNF-> Hippocampus (can bridge the temporal gap) BDNF-> PFC. PFC adaptively timed motor learning-> cerebellum.

p190 Howell: [neural microcircuits, modal architectures] used in ART -

bottom-up filters	top-down expectations	purpose
instar learning	outstar learning	p200fig05.13 Expectations focus attention: [in, out]star often used to learn the adaptive weights. top-down expectations to select, amplify, and synchronize expected patterns of critical features, while suppressing unexpected features
LGN Lateral Geniculate Nucleus	V1 cortical area	p192fig05.06 focus attention on expected critical features
EC-III entorhinal stripe cells	CA1 hippocampal place cells	p600fig16.36 entorhinal-hippocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories
auditory orienting arousal	auditory category	p215fig05.28 How a mismatch between bottom-up and top-down patterns can trigger activation of the orienting system A and, with it, a burst of nonspecific arousal to the category level. ART Matching Rule: TD mismatch can suppress a part of F1 STM pattern, F2 is reset if degree of match < vigilance
auditory stream with/without [silence, noise] gaps	perceived sound continues?	p419fig12.17 The auditory continuity illusion: Backwards in time - How does a future sound let past sound continue through noise? Resonance!
visual perception, learning and recognition of visual object categories	motion perception, spatial representation and target tracking	p520fig14.02 pART predictive Adaptive Resonance Theory. Many adaptive synapses are bidirectional, thereby supporting synchronous resonant dynamics among multiple cortical regions. The output signals from the basal ganglia that regulate reinforcement learning and gating of multiple cortical areas are not shown. red - cognitive-emotional dynamics green - working memory dynamics black - see [bottom-up, top-down] lists
EEG with mismatch, arousal, and STM reset events	expected [P120, N200, P300] event-related potentials (ERPs)	p211fig05.21 Sequences of P120, N200, and P300 event-related potentials (ERPs) occur during oddball learning EEG experiments under conditions that ART predicted should occur during sequences of mismatch, arousal, and STM reset events, respectively.
Cognitive	Emotional	p541fig15.02 nSTART neurotrophic Spectrally Timed Adaptive Resonance Theory: Hippocampus can sustain a Cognitive-Emotional resonance: that can support "the feeling of what happens" and knowing what event caused that feeling. Hippocampus enables adaptively timed learning that can bridge a trace conditioning gap, or other temporal gap between Conditioned Stimuluus (CS) and Unconditioned Stimulus (US).

backgound colours in the table signify :

white	general microcircuit : a possible component of ART architecture
lime green	sensory perception [attention, expectation, learn]. Table includes [see, hear, !!must add touch example!!], no Grossberg [smell, taste] yet?
light blue	post-perceptual cognition?
pink	"the feeling of what happens" and knowing what event caused that feeling

ARTMAP learn maps of natural terrain

grepStr = ARTMAP

p242sec Self-supervised ARTMAP: Learning on our own after leaving school
image p221fig05.31 A system like Fuzzy ARTMAP can learn to associate learned categories in one ART network with learned categories in a second ART network. Because both bottom-up and top-down interactions occur in both networks, a bottom-up input pattern to the first ART network can learn to generate a top-down output pattern from the second ART network.
|| Fuzzy ARTMAP. Match tracking realizes minimax learning principle: vigilence increases to just above the match ratio of prototype / exemplar, thereby triggering search
image p225fig05.33 Some early ARTMAP benchmark studies. These successes led to the use of ARTMAP, and many variants that we and other groups have developed, in many large-scale applications in engineering and technology that has not abated even today.
|| see Early ARTMAP benchmark studies
image p225fig05.34 ARTMAP was successfully used to learn maps of natural terrains with many advantages over those of mapping projects that used AI expert systems. The advantages are so great that many mapping projects started to use this technology.
|| AI expert system - 1 year: field identification of natural regions; derivation of ad hoc rules for each region by expert geographers; correct 80,000 of 250,000 site labels; 230m (site-level) scale. ARTMAP system - 1 day: rapid, automatic, no natural regions or rules; confidence map; 30m (pixel-level) scale can see roads; equal accuracy at test sites
image p242fig05.46 Computer simulations of how two variants of Distributed ARTMAP incrementally learn the 5-4 category structure. See the text for details.
|| Distributed ARTMAP with [self-supervised learning, post-training LTM noise]

[, c]ARTWORD word perception cycle

grepStr = ARTWORD|cARTWORD

image p436fig12.30 The conscious ARTWORD, or cARTWORD, laminar cortical speech model simulates how future context can disambiguate noisy past speech sounds in such a way that the completed percept is consciously heard to proceed from past to future as a feature-item-list resonant wave propagates through time.
|| cARTWORD: Laminar cortical model macrocircuit (Grossberg, Kazerounian 2011) Simulates PHONEMIC RESTORATION: Cognitive Working Memory (processed item sequences) - [Excitatory-> inhibitory-> habituative-> adaptive filter-> adaptive filter-> adaptive filter with depletable synapse-> Acoustic [item, feature]
image p453fig12.49 The ARTWORD model that I published in 2000 with my PhD student Christopher Myers simulates data such as the (Repp etal 1978) data in Figure 12.68. See the text for details.
|| ARTWORD model (Grossberg, Myers 2000). Input phonetic features-> Phonemic item working memory-> Masking Field unitized lists-> Automatic gain control-> Phonemic item working memory. [habituative gate, adaptive filter]s.
image p453fig12.50 The ARTWORD perception cycle shows how sequences of items activate possible list chunks, which compete among each other and begin to send their top-down expectations back to the item working memory. An item-list resonance develops through time as a result.
|| ARTWORD perception cycle. (a) bottom-up activation (b) list chunk competition (c) item-list resonance (d) chunk reset due to habituative collapse.
image p455fig12.52 Simulation of cARTWORD dynamics in response to the complete list /1/-/2/-/3/. The relevant responses are surrounded by a red box.
|| Presentation of a normal sequence: input /1/-/2/-/3/. |c(i,1)-5| vs time (msec). List chunks select most predictive code. Order stored in WM layers. Resonant activity of /1/-/2/-/3/ in item and feature layers corresponds to conscious speech percept.
image p456fig12.53 Simulation of cARTWORD dynamics in response to the partial list /1/-silence-/3/ with /2/ replaced by silence. Only the representations of these items can be seen in the red box.
|| Presentation with silence duration: input /1/-silence-/3/. |c(i,1)-5| vs time (msec). List chunks select most predictive code. Order stored in WM layers. Gap in resonant activity of /1/-silence-/3/ in item and feature layers corresponds to perceived silence.
image p456fig12.54 Item /2/ is restored in the correct list position in response to the list /1/-noise-/3/.
|| Presentation with noise: input /1/-noise-/3/. |c(i,1)-5| vs time (msec). List chunks select the most predictive code. Order restored in WM layers. Resonant activity of /1/-/2/-/3/ in item and feature layers corresponds to restoration of item /2/ replaced by noise in input.
image p457fig12.55 Item /4/ is restored in the correct list position in response to the list /1/-noise-/5/. This and the previous figure show how future context can disambiguate past noisy sequences that are otherwise identical.
|| Presentation with noise: input /1/-noise-/5/. |c(i,1)-5| vs time (msec). List chunks select the most predictive code. Order restored in WM layers. Resonant activity of /1/-/4/-/3/ in item and feature layers corresponds to restoration of item /4/ replaced by noise in input. conscious ARTWORD, see cARTWORD

LIST PARSE [linguistic, spatial, motor] working memory

grepStr = LIST PARSE

p443sec LIST PARSE: A laminar model of variable-length sequences
p477sec Back to LIST PARSE: Volitionally-controlled variable-rate sequential performance
image p444fig12.42 The LIST PARSE laminar cortical model of working memory and list chunking that I published with Lance Pearson in 2008 simulated the Averbeck etal data in Figure 12.41, as in the left column of the figure. It also simulated cognitive data about working memory storage by human subjects. See the text for details.
|| LIST PARSE: Laminar cortical model of working memory and list chunking (Grossberg, Pearson 2008). Simulates data about: [immediate, delayed, continuous] distractor free recall; immediate serial recall; and variable-speed sequential perfomance of motor acts. [velocity, acceleration] vs time (ms) from recall cue.
image p445fig12.43 The LIST PARSE laminar cortical Cognitive Working Memory circuit, that is proposed to occur in ventrolateral prefrontal cortex, is homologous to the LAMINART circuit circuit that models aspects of how visual cortex sees. The Motor Working Memory, VITE Trajectory Generator, and Variable-Rate Volitional Control circuits model how other brain regions, including dorsolateral prefrontal cortex, motor cortex, cerebellum, and basal ganglia, interact with the Cognitive Working Memory to control working memory storage and variable-rate performance of item sequences.
|| List parse circuit diagram. Connectivity convention. sequence chunks [<- BU filter, TD expectation ->] working memory. Working memory and sequence chunking circuit is homologous to visual LAMINART circuit!
image p446fig12.44 (left column, top row) LIST PARSE can model linguistic data from human subjects. In this figure, model parameters are fixed to enable a close fit to data about error-type distributions in immediate free recall experiments, notably transposition errors. (right column, top row) Simulation and data showing bowing of the serial position curve, including an extended primacy gradient. (left column, bottom row) The simulation curve overlays data about list length effects, notably the increasing recall difficulty of longer lists during immediate serial recall (ISR). (right column, bottom row) Simulation (bottom image) and data (top image) of the limited temporal extent for recall.
|| (1. TL) Error-type distributions in immediate serial recall (Hanson etal 1996). % occurence vs serial position. Graph convention: Data- dashed lines; Simulations- solid lines. Six letter visual ISR. Order errors- transpositions of neighboring items are the most common. Model explanation: Noisy activation levels change relative oorder in primacy gradient. Similar activation of neighboring items most susceptible to noise. Model paramenters fitted on these data. (2. TR) Bowing of serial position curve (Cowan etal 1999). % correct vs serial position. Auditory ISR with various list lengths (graphs shifted rightward): For [, sub-]span lists- extended primacy, with one (or two) item recency; Auditory presentation- enhanced performance for last items. LIST PARSE: End effects- first and last items half as many members; Echoic memory- last presented item retained in separate store. (3. BL) List length effects, circles (Crannell, Parrish 1968), squares (Baddeley, Hitch 1975), solid line- simulation. % list correct vs list length. Variable list length ISR: longer lists are more difficult to recall. LIST PARSE: More items- closer activation levels and lower absolute activity level with enough inputs; Noise is more likely to produce order errors, Activity levels more likely to drop below threshold;. (4. BR) Limited temporal extent for recall (Murdoch 1961). % recalled vs retention interval (s). ISR task with distractor-filled retention intervals (to prevent rehersal): Increasing retention interval - decreases probabilty of recalling list correctly; Load dependence- longer list more affected by delays; Performance plateau- subjects reach apparent asymptote. LIST PARSE: Increase convergence of activities with time; loss of order information;.
image p447fig12.45 (left column) LIST PARSE simulations of the proportion of order errors as a function of serial position for 6 item lists with (a) an extended pause of 7 time units between the third and fourth items, and (b) pauses of 5 time units (solid curve) and 10 time units (dashed curve) between all items. (right column) Simulations (solid curves) and data (dashed curves) illustrating close model fits in various immediate free recall tasks.
|| (Left) Temporal grouping and presentation variability. Temporal grouping: Inserting an extended pause leads to inter-group bowing; Significantly different times of integration and activity levels across pause, fewer interchanges. (Right) Immediate free recall, and [delayed, continuous] distractor-free recall. Overt rehersal IFR task with super-span (ie 20 item) lists: Extended recency- even more extended with shorter ISIs; Increased probability of recall with diminished time from last rehearsal; Early items in list rehearsed most;. LIST PARSE (unique) for long lists: Incoming items from recency gradient; Rehearsal (re-presentation) based upon level of activity.

family of ART ....visual, auditory

[, d, p]ARTSCAN attentional shroud, binocular rivalry

grepStr = ARTSCAN|dARTSCAN|pARTSCAN

p515sec From survival circuits to ARTSCAN Search, pART, and the Where's Waldo problem
image p254fig06.03 These interactions of the ARTSCAN Search model enable it to learn to recognize and name invariant object categories. Interactions between spatial attention in the Where cortical stream, via surface-shroud resonances, and object attention in the What cortical stream, that obeys the ART Matching Rule, coordinate these learning, recognition, and naming processes.
|| Retinal image -> On & OFF cell contrast normalization (retina/LGN) -> polarity [, in]sensitive contrast enhancement (V1) -> object [boundary (V2), surface (V2/V4)] -> surface contour (V2); What stream categories: volition control (BG). object boundary (V2) <-> view (ITp) <-> view integrator (ITp) <-> object (ITa) <-> [object-value (ORB), value (Amyg)] <-> name (PFC)
image p255fig06.04 The ARTSCAN Search model can also search for a desired target object in a scene, thereby clarifying how our brains solve the Where's Waldo problem.
|| similar ilustration as Figure 06.03, with some changes to arrows
image p259fig06.08 The distributed ARTSCAN, or dARTSCAN, model includes spatial attention in both PPC and PFC, and both fast-acting attention, triggered by transient cells in Where cortical areas such as MT, and slower-acting surface-shroud resonances in What cortical areas such as V4 and PPC. See the text for details.
|| dARTSCN spatial attention hierarchy, Fast (Where stream) Slow (What stream) (Foley, Grossberg, and Mingolia 2012). [transient cells (MT) ->, object surfaces (V4) <->] [object shrouds (PPC) <-> spatial shrouds (PPC/PFC)]
image p271fig06.17 Persistent activity in IT cells is just what is needed to enable view-invariant object category learning by ARTSCAN to be generalized to [view, position, size]-invariant category learning by positional ARTSCAN, or pARTSCAN. See the text for details.
|| Persistent activity in IT. Physiological data show that persistent activity exist in IT (Fuester and Jervey 1981, Miyashita and Chang 1988, Tomita etal 1999). Adapted from (Tomita etal 1999 Nature)
image p272fig06.18 The pARTSCAN model can learn [view, position, size]-invariant categories by adding view category integrator cells that have the properties of persistent neurons in IT. These integrator cells get reset with the invariant object category, not the view category.
|| pARTSCAN: positionally-invariant object learning. (Cao, Grossberg, Markowitz 2011). IT cells with persistent activities are modeled by view category integrators in ITp. View-specific category cells are RESET as the eyes move within the object. View category integrator cells are NOT RESET when the view-specific category is reset. They are RESET along with invariant object category cells when a spatial attention shift occurs.
image p273fig06.20 pARTSCAN can simulate the IT cell recoding that Li and DiCarlo reported in their swapping experiments because the swapping procedure happens without causing a parietal reset burst to occur. Thus the originally activated invariant category remains activated and can get associated with the swapped object features.
|| Simulation of Li and DiCarlo swapping data. data (Li and DiCarlo 2008), model (Cao, Grossberg, Markowitz 2011). normalized response vs. exposure (swaps and/or hours)
image p274fig06.21 pARTSCAN can also simulate the trade-off in IT cell responses between position invariance and selectivity that was reported by Zoccolan etal 2007. This trade-off limits the amount of position invariance that can be learned by a cortical area like V1 that is constrained by the cortical magnification factor.
|| Trade-off in IT cell response properties. Inferotemporal cortex cells with greater position invariance respond less selectively to natural objects. invariance-tolerance, selectivity-sparseness. data (Zoccolan etal 2007) model (Grossberg, Markowitzm, Cao 2011). position tolerance (PT, degrees) vs sparseness (S)
image p274fig06.22 pARTSCAN can simulate how IT cortex processes image morphs, when it learns with high vigilance. See the text for details.
|| Akrami etal simulation: a case of high vigilance. tested on morphs between image pairs
image p498fig13.22 (left column, top row) Adaptive filtering and conditioned arousal are both needed to regulate what cues can learn to activate particular space-time patterns. These developments lead inexorably to basic cognitive abilities, as embodied in the 3D LAMINART models for 3D vision and figure-ground perception (Chapter 11) and the 3D ARTSCAN SEARCH model for invariant object learning, recognition, and 3D search (Chapter 6). (right column, top row) Conditioned arousal enables only emotionally important cues to activate a motivationally relevant space-time pattern. (bottom row) Conditioned arousal and drive representations arise naturally from the unlumping of avalanche circuits to make them selective to motivationally important cues. The MOTIVATOR model is a natural outcome of this unlumping process (this chapter).
|| (top) Adaptive filtering and Conditioned arousal. Towards Cognition: need to filter inputs to the command cell. Towards Emotion: important signals turn arousal ON and OFF. (bottom) Conditioned arousal and Drive representations. Competition between conditioned arousal sources at drive representations, eg amygdala.

ARTSCENE classification of scenic properties

grepStr = ARTSCENE

image p531fig14.06 Classification of scenic properties as texture categories by the ARTSCENE model. See the text for details.
|| Image-> Feature extraction (texture principal component rankings)-> Learning feature-to-scene mapping (texture category principal component rankings)<- scene class. Large-to-small attentional shrouds as principle component higher.
image p531fig14.07 Voting in the ARTSCENE model achieves even better prediction of scene type. See the text for details.
|| Image-> Feature extraction (texture principal component rankings)-> Learning feature-to-scene mapping (texture category principal component rankings)-> evidence accumulation (sum)-> scene class winner-take-all inference. Large-to-small attentional shrouds as principle component higher.
image p532fig14.08 Macrocircuit of the ARTSCENE Search neural model for learning to search for desired objects by using the sequences of already experienced objects and their locations to predict what and where the desired object is. V1 = First visual area or primary visual cortex; V2 = Second visual area; V4 = Fourth visual area; PPC = Posterior Parietal Cortex; ITp = posterior InferoTemporal cortex; ITa = anterior InferoTemporal cortex; MTL = Medial Temporal Lobe; PHC = ParaHippoCampal cortex; PRC = PeriRhinal Cortex; PFC = PreFrontal Cortex; DLPFC = DorsoLateral PreFrontal Cortex; VPFC = Ventral PFC; SC = Superior Colliculus.
||
image p533fig14.09 Search data and ARTSCENE Search simulations of them in each pair of images from (A) to (F). See the text for details.
|| 6*[data vs simulation], [Response time (ms) versus epoch].

SMART synchronous matching ART, mismatch triggering

grepStr = SMART|synchronous match|mismatch trigger

p229sec SMART: A laminar cortical hierarchy of spiking neurons
image p214fig05.26 When a big enough mismatch occurs, the orienting system is activated and sends a burst of nonspecific arousal to the category level. This Mismatch Detector has properties of the N200 ERP.
|| Mismatch triggers nonspecific arousal. Mismatch at F1 eleicits a nonspecific event at F2. Call this event nonspecific arousal. N200 ERP Naatanen etal: 1. bottom-up, 2. unconditionable, 3. nonspecific, 4. mismatch
image p230fig05.38 The Synchronous Matching ART, or SMART, model includes spiking neurons in a laminar cortical hierarchy. I developed SMART with my PhD student Massimiliano Versace. By unlumping LAMINART to include spiking neurons, finer details of neurodynamics, such as the existence of faster gamma oscillations during good enough matches, and slower beta oscillations during bad enough mismatches, could be shown as emergent properties of network interactions.
|| Second order thalamus -> specific thalamic nucleus -> Thalamic reticulate nucleus -> neocortical laminar circuit [6ll, 6l, 5, 2/3, 1] -> Higher order cortex. Similar for First order thalamus -> First order cortex, with interconnection to Second order, nonspecific thalamic nucleus
image p231fig05.39 The SMART hypothesis testing and learning cycle predicts that vigilance increases when a mismatch in subcortical regions like the nonspecific thanlamus activates the nucleus basalis of Meynert which, in turn, broadcasts a burst of the neurotransmitter acetylcholine, or ACh, to deeper cortical layers. Due to the way in which LAMINART proposes that cortical matching and mismatching occurs, this ACh burst can increase vigilance and thereby trigger a memory search. See the text for details.
|| [BU input, [, non]specific thalamic nucleus, thalamic reticulate nucleus, neocortical laminar circuit] cart [Arousal, Reset, Search, Vigilance]
image p232fig05.40 Computer simulation of how the SMART mode generates (a) gamma oscillations if a good enough match occurs, or (c) beta oscillations if a bad enough match occurs. See the text for details.
|| Brain oscillations during match/mismatch, data, simulation. (a) TD corticothalamic feedback increases synchrony (Sillito etal 1994) (b) Match increases γ oscillations (c) Mismatch increases θ,β oscillations
image p232fig05.41 (a)-(c). The sequence of interlaminar events that SMART predicts during a mismatch reset. (d) Some of the compatible neurophysiological data.
|| Mismatch causes layer 5 dendritic spike that trigger reset. (a) Arousal causes increase in nonspecific thalamic nuclei firing rate and layer 5 dendritic and later somatic spikes (Karkum and Zhu 2002, Williams and Stuart 1999) (b) Layer 5 spikes reach layer 4 via layer 6i and inhibitory neurons (Lund and Boothe 1975, Gilbert and Wiesel 1979) (c) habituative neurotransmitters in layer 6i shift the balance of active cells in layer 4 (Grossberg 1972, 1976) (d) Dendritic stimulation fires layer 5 (Larkum and Zhu 2002) stimulation apical dendrites of nonspecific thalamus
Grossberg 2021 p229c2h0.60 SMART computer simulations demonstrate that a good enough match of a top-down expectation with a bottom-up feature pattern generates an attentive resonance during which the spikes of active cells synchronize in the gamma frequency range of 20-70 Hz (Figure 5.40). Many labs have reported a link between attention and gamma oscillations in the brain, including two articles published in 2001, one from the laboratory of Robert Desimone when he was at the the National Institute of Mental Health in Bethseda (Fries, Reynolds, Rorie, Desimone 2001), and the other from the laboratory of Wolf Singer in Frankfurt (Engel, Fries, Singer 2001). You'll note that Pascal Fries participated in both studies, and is an acknowledged leader in neurobiological studies of gamma oscillations; eg (Fries 2009). .."

ARTPHONE [gain control, working] memory

grepStr = ARTPHONE

p468sec ARTPHONE: Rate-sensitive gain control creates rate-invariant working memories
image p468fig12.65 Linguistic properties of the PHONET model and some of the data that it simulates. The upper left image summarizes the asymmetric transient-to-sustained gain control that helps to create invariant intraword ratios during variable-rate speech. The lower left image summarizes the rate-dependent gain control of the ARTPHONE model that creates rate-invariant working memory representations in response to sequences of variable-rate speech. The right image summarizes the kind of paradoxical VC-CV category boundary data of (Repp 1980) that ARTPHONE simulates. See the text for details.
|| (left upper) [transient, sustained] [working memory, filter, category]. (left lower) phone inputs-> [input rate estimate, features], Features w <- habituative transmitter gates -> categories-> rate invariant phonetic output, input rate estimate-> gain control-> [features, categories] rate-dependent integration of categories and features. (right) % 2-stop vs VC-CV silent interval (msec): [ib-ga, ib-ba, iga, iba].

ARTSTREAM auditory streaming, SPINET sound spectra

grepStr = ARTSTREAM|SPINET

p420sec SPINET and ARTSTREAM: Resonant dynamics doing auditory streaming
p422sec From SPINET processing of sound spectra to ARTSTREAM creation of multiple streams
image p420fig12.18 The ARTSTREAM model explains and simulates the auditory continuity illusion as an example of a spectral-pitch resonance. Interactions of ART Matching Rule and asymmetric competition mechanisms in cortical strip maps explain how the tone selects the consistent frequency from the noise in its own stream while separating the rest of the noise into another stream.
|| ARTSTREAM model (Grossberg 1999; Grossberg, Govindarajan, Wyse, Cohen 2004). SPINET. Frequency and pitch strips. Bottom Up (BU) harmonic sieve. Top Down (TD) harmonic ART matching. Exclusive allocation. Learn pitch categories based on early harmonic processing. A stream is a Spectral-Pitch Resonance!
image p422fig12.19 The ARTSTREAM model includes mechanisms for deriving streams both from pitch and from source direction. See the text for details.
|| [left, right] cart Peripheral processing = [input signal-> outer & middle ear preemphasis-> basilar membrane gammatone filterbank-> energy measure]. Spectral stream layer-> spectral summation layer-> delays-> [f-, tau] plane-> pitch stream layer-> pitch summation layer.
image p423fig12.20 The Spatial Pitch Network, or SPINET, model shows how a log polar spatial representation of the sound frequency spectrum can be derived from auditory signals occuring in time. The spatial representation allows the ARTSTREAM model to compute spatially distinct auditory streams.
|| SPINET model (Spatial Pitch Network) (Cohen, Grossberg, Wyse 1995). 1. input sound 2. Gamma-tone filter bank 3. Shaort-term average energy spectrum 4. MAP transfer function 5. On-center off-surround and rectification 6. Harmonic weighting 7. Harmonic summation and competition -> PITCH
image p424fig12.21 One of the many types of data about pitch processing that are simulated by the SPINET model. See the text for details.
|| Pitch shifts with component shifts (Patterson, Wightman 1976; Schouten 1962). Pitch vs lowest harmonic number.
image p425fig12.23 ARTSTREAM simulations of the auditory continuity illusion and other streaming properties (left column, top row). When two tones are separated by silence (Input), a percept of silence also separates them in a spectral-pitch resonance. (left column, bottom row). When two tones are separated by broadband noise, the percept of tone continues through the noise in one stream (stream 1) while the remainder of the noise occurs in a different stream (stream 2). (right column) Some of the other streaming properties that have been simulated by the ARTSTREAM model.
|| Auditory continuity does not occur without noise. Auditory continuity in noise. Other simulated streaming data.
image p431fig12.27 The strip maps that occur in ARTSTREAM and NormNet are variants of a cortical design that aalso creates ocular dominance columns in the visual cortex.
|| Adult organization of V1 (Grinvald etal http://www.weizmann.ac.il/brain/images/cubes.html). (1) Occular dominance columns (OCDs): Alternating strips of cortex respond preferentially to visual inputs of each eye (R/L corresponds to Right and Left eye inputs in the figure); Orientation columns: A smooth pattern of changing orientation preference within each ODC. Organized in a pinwheel like fashion.

conciousness

What is consciousness?

grepStr = conscious

[Chapter, section]s of Stephen Grossberg 2021 "Conscious Mind, Resonant Brain"

Conscious mind, resonant brain: Table of Contents

Conscious mind, resonant brain: sub-section list I retyped content DIRECTLY from [Grossberg 2021 "Conscious Mind, Resonant brain"], which sounds insane as normal document tools do this automatically, and this is probably available on Grossberg's webPages or elsewhere. Still, it was important for me to do this to foce myself to go step-by-step through the book, rather than lazily skimming through it which is mostly "in one eye and out the mind?" for me. Also, it was extremely important for me to see all of the gaps in my own awareness of Grossberg's work and thinking, in spote of having bought previous books, and reading numberous papers of Grossberg and his colleagues. I was shocked at how much I had missed, which is perhaps not surprising given all of the other areas of reserch in neural networks. Mostly, I was missing the overall scope of his work, and the higher-level concepts.

p370 Chapter 11	means (Grossberg 2021) page 370, Chapter 11
p002sec Illusion and reality	means (Grossberg 2021) page 2, section Illusion and reality
p013fig01.09	means (Grossberg 2021) page 13, Figure 1.09 (1.9 as in book)
p030tbl01.02	means (Grossberg 2021) page 30, Table 1.02 (1.2 as in book)
p111c2h0.5	means (Grossberg 2021) page 111, column 2, height from top as fraction of page height
\|\| text...	Are notes in addition to [figure, table] captions, mostly comprised of text within the image, but also including quotes of text in the book. Rarely, it includes comments by Howell preceded by "Howell". The latter are distinct from "readers notes" (see, for example : reader Howell notes).
p044 Howell: grepStr 'conscious'	means a comment by reader Howell, extracted using the grep string shown, referring to page 44 in (Grossberg 2021)

Conscious mind, resonant brain: Table of Contents

p001 Chapter 1 Overview - From Complementary Computing and Adaptive Resonance to conscious awareness From vigilant conscious awareness to autism, amnesia, and Alzheimer's disease
p250 Chapter 6 Conscious seeing and invariant recognition - Complementary cortical streams coordinate attention for seeing and recognition
p539 Chapter 15 Adaptively timed learning - How timed motivation regulates conscious learning and memory consolidation
Conscious mind, resonant brain: sub-section list
Many topics in the sub-section titles below do not address consciousness, but almost all contribute to the theory of conciousness that emerges.
p000XIsec Why so many books about consciousness?
p000XIIsec The varieties of brain resonances: All conscious states are resonant states From Complementary Computing and Adaptive Resonance to conscious awareness
p001sec Seeing, recognizing, and consciousness
p005sec Why do not all occluding objects look transparent? conscious seeing vs recognition
p028sec What and Where cortical processing streams and consciousness
p034sec Is the conscious present in the past?
p038sec All conscious states are resonant states
p040sec Conscious seeing, hearing, feeling, and knowing: linking conciousness to action
p040sec Why did consciousness evolve?
p042sec All conscious states are resonant states, but some resonant states are not conscious
p043sec Towards solving the 'Hard Problem' of consciousness: a philosophical third rail?
p044sec A linking hypothesis between resonant brain dynamics and the conscious mind including: Some other approaches to understanding consciousness (Grossberg 2021 p47c1h0.5)
p134sec Why is neon color-spreading an important probe of how we consciously see?
p152sec Consciousness again! From vigilant conscious awareness to autism, amnesia, and Alzheimer's disease
p184sec become conscious of these events
p184sec learn language quickly and consciously hear completed speech sounds in noise
p189sec The predictive brain: Intention, attention, and consciousness
Chapter 6 - Conscious seeing and invariant recognition
p258sec Unconscious processing of neglected object identity
p264sec Conscious visual qualia are computed in retinotopic coordinates
p276sec Two types of perceptual stability cooperate during active conscious vision
p325sec How do these results help to explain conscious peerception?
p419sec Stream-shroud resonances for conscious hearing of auditory qualia
p427sec Stream-shroud resonances for conscious perception of auditory objects in streams
p451sec Phonemic restoration: How the future can influence what is consciously heard in the past
p452sec Conscious speech is a resonant wave: Coherently grouping the units of speech and language
p514sec The feeling of what happens, core consciousness, dual competitionm and survival circuits
p516sec Conscious vs. non-conscious emotions How timed motivation regulates conscious learning and memory consolidation
p540sec Conditioing and consciousness: Trace conditioning, hippocampus, and "time cells"
p545sec Is eposodic learning and memory necessary to consciously experience trace conditioning?
p618sec Resonant conscious experiences of perceiving, feeling, and knowing
p619sec Why we become conscious: Hierarchical resolution of complementarity and uncertainty

p619sec Consciousness and the brain-environment loops that drive brain self-organization

[Figure, table]s of Stephen Grossberg 2021 "Conscious Mind, Resonant Brain"

p370 Chapter 11	means (Grossberg 2021) page 370, Chapter 11
p002sec Illusion and reality	means (Grossberg 2021) page 2, section Illusion and reality
p013fig01.09	means (Grossberg 2021) page 13, Figure 1.09 (1.9 as in book)
p030tbl01.02	means (Grossberg 2021) page 30, Table 1.02 (1.2 as in book)
p111c2h0.5	means (Grossberg 2021) page 111, column 2, height from top as fraction of page height
\|\| text...	Are notes in addition to [figure, table] captions, mostly comprised of text within the image, but also including quotes of text in the book. Rarely, it includes comments by Howell preceded by "Howell". The latter are distinct from "readers notes" (see, for example : reader Howell notes).
p044 Howell: grepStr 'conscious'	means a comment by reader Howell, extracted using the grep string shown, referring to page 44 in (Grossberg 2021)

Chapter 6 - Conscious seeing and invariant recognition
image p039tbl01.03 The link between consciousness and movement
||
VISUAL seeing, knowing, and reaching
AUDITORY hearing, knowing, and speaking
EMOTIONAL feeling, knowing, and acting

image p042tbl01.04 The six main kinds of resonances which support different kinds of conscious awareness that will be explained and discussed in this book.
||

type of resonance	type of consciousness
surface-shroud	see visual object or scene
feature-category	recognize visual object or scene
stream-shroud	hear auditory object or stream
spectral-pitch-and-timbre	recognize auditory object or stream
item-list	recognize speech and language
cognitive-emotional	feel emotion and know its source

Chapter 6 - Conscious seeing and invariant recognition

image p270fig06.16 The same target position signal that can command the next saccade also updates a gain field that predictively maintains the attentional shroud in head-centered coordinates, even before the eye movement is complete. This process keeps the shroud invariant under eye movements, so that it can continue to inhibit reset of an emerging invariant category as t is associated with multiple object views, even while the conscious surface representation shifts with each eye movement in retinotopic coordinates. This pdating process is often called predictive re mapping.
|| Predictive remapping of eye movements! From V3A to LIP. [spatial attention, object attention, figure-ground separation, eye movement remapping, visual search]. (Beauvillaib etal 2005, Carlson-Radvansky 1999, Cavanaugh etal 2001, Fecteau & Munoz 2003, Henderson & Hollingworth 2003, Irwin 1991)
image p278fig06.27 A surface-shroud resonance through the Where stream enables us to consciously see an object while a feature-category resonance into the What stream enables us to recognize it. Both kinds of resonances can synchronize via visual cortex so that we can know what an object is when we see it.
|| What kinds of resonances support knowing vs seeing? What stream [knowing, feature-prototype resonance], Where stream [seeing, surface-shroud resonance]
image p278fig06.28 If the feature-category resonances cannot form, say due to a lesion in IT, then a surface-shroud resonance can still support conscious seeing of an attended object, and looking at or reaching for it, even if the individual doing so knows nothing about the object, as occurs during visual agnosia. The surface-shroud resonance supports both spatial attention and releases commands that embody the intention to move towards the attended object.
|| What kinds of resonances support knowing vs seeing? visual agnosia: reaching without knowing Patient DF (Goodale etal 1991). Attention and intention both parietal cortical functions (Anderson, Essick, Siegel 1985; Gnadt, Andersen 1988; Synder, Batista, Andersen 1997, 1998)
image p355fig10.02 Distinguishing processes of seeing vs knowing has been difficult because they interact so strongly.
|| Seeing vs. Knowing. Seeing and knowing [operate at different levels of the brain, use specialized circuits], but they [interact via feedback, use similar cortical designs, feedback is needed for conscious perception]. Cerebral Cortex: Seeing [V1-V4, MS-MST], Knowing [IT, PFC].
image p369fig10.19 Data from (Watanabe etal 2001) showing perceptual learning of the coherent motion direction, despite the lack of extra-foveal attention and awareness of the moving stimuli.
|| Unconscious perceptual learning of motion direction, % correct for two tests, compared to chance level results.
image p396fig11.35 Three properties of bipole boundary grouping in V2 can explain how boundaries oscillate in response to rivalry-inducing stimuli. Because all boundaries are invisible, however, these properties are not sufficient to generate a conscious percept of rivalrous surfaces.
|| 3 V2 boundary properties cause binocular rivalry. 1. Bipole grouping, 2. Orientational competition, 3. Actovity-dependent habituation
image p419fig12.17 The auditory continuity illusion illustrates the ART Matching Rule at the level of auditory streaming. Its "backwards in time" effect of future context on past conscious perception is a signature of resonance.
|| Auditory continuity illusion. input, percept. Backwards in time - How does a future sound let past sound continue through noise? Resonance! - It takes a while to kick in. After it starts, a future tone can maintain it much more quickly. Why does this not happen if there is no noise? - ART Matching Rule! TD harmonic filter is maodulatory without BU input. It cannot create something out of nothing.
image p436fig12.30 The conscious ARTWORD, or cARTWORD, laminar cortical speech model simulates how future context can disambiguate noisy past speech sounds in such a way that the completed percept is consciously heard to proceed from past to future as a feature-item-list resonant wave propagates through time.
|| cARTWORD: Laminar cortical model macrocircuit (Grossberg, Kazerounian 2011) Simulates PHONEMIC RESTORATION: Cognitive Working Memory (processed item sequences) - [Excitatory-> inhibitory-> habituative-> adaptive filter-> adaptive filter-> adaptive filter with depletable synapse-> Acoustic [item, feature]
image p455fig12.52 Simulation of cARTWORD dynamics in response to the complete list /1/-/2/-/3/. The relevant responses are surrounded by a red box.
|| Presentation of a normal sequence: input /1/-/2/-/3/. |c(i,1)-5| vs time (msec). List chunks select most predictive code. Order stored in WM layers. Resonant activity of /1/-/2/-/3/ in item and feature layers corresponds to conscious speech percept.
image p484fig13.04 The top-down feedback from the orbitofrontal cortex closes a feedback loop that supports a cognitive-emotional resonance. If this resonance can be sustained long enough, it enables us to have feelings at the same time that we experience the categories that caused them.
|| Cognitive-Emotional resonance. Basis of "core consciousness" and "the feeling of what happens". (Damasio 1999) derives heuristic version of CogEM model from his clinical data. Drive-> amygdala-> prefrontal cortex-> sensory cortex, resonance around the latter 3. How is this resonance maintained long enough to become conscious? p619idx conscious, resolution of complementarity and uncertainty
conscious ARTWORD, see cARTWORD
p479idx conscious audition, singing
p516idx conscious emotions
p618idx conscious minds, experiences of
p044idx conscious minds, hypothesis linking resonant brain dynamics and
p152idx consciousness, xi
p047idx consciousness, approaches to understanding
p619idx consciousness, brain organization
p040idx consciousness, evolution of
p041idx consciousness, extra degree of freedom
pxiiidx consciousness, hard problem of
p039idx consciousness, link between movement and
p042idx consciousness, type of, table
p028idx consciousness, What and Where cortical processing streams
p088idx consciousness, without qualia
p048idx consciousness Explained, (Dennett), also p135
p325idx conscious perception, motion explaining
p041idx conscious resonance, extra degree of freedom
p005idx conscious seeing vs recognition
p452idx conscious speech, grouping speech and language
pxiiidx conscious states, resonant states, also pp 38-9,42-3
p372idx conscious visibility
p004idx unconscious inference, also pp 41,154,335,355
p069idx unconscious inference, Helmholtz's theory
Grossberg's comments for some well-known consciousness theories This webPage was initially created to provide background for Grossberg's OR[anticipated, predicted, unified] the [experimental result, model]s on consciousness by other often-cited experts. It may be expanded for other themes. Keep in mind that onl a tiny fraction of quotes are provided, reflecting a very few other researchers that I pursued in (Grossberg 2021).
Grossberg 2021 p046c1h0.05 Of particular interest are data supporting ART's predicted link between synchronous oscillations and consciousness that neuroscientists such as ;
- (Victor Lamme 2006)
- (Rudolfo Llinas, Ribrary, Contreras, Pedroarena 1998)
- (Wolf Singer 1998)
- and their colleagues
have reported.
What is consciousness?
Grossberg 2021 page xii I will explain that "all conscious states are resonant states". The importance of this assertion motivated the title of this book. I will describe the resonances that seem to underlie our conscious experiences of seeing, hearing, feeling, and knowing. These explanations will include where in the brain these resonances take place, how they occur there, and why evolution may have been driven to discover conscious mental states in the first place. I will also clarify how these resonances interact when we simultaneously see, hear, feel, and know something, all at once, about a person or na event in our world. This description is part of a burgeoning classification of resonances. I will also explain why not all resonant states are conscious, and why not all brain dynamics are resonant. These results contribute to solving what has been called the Hard Problem of Consciousness.
Grossberg 2021 p040c2h0.80 sub-section: Why did consciousness evolve? In particular, the book will show how and why multiple processing stages are needed before the brain can construct a complete and stable enough representation of the information in the world with which to control effective behaviours. This happens because the sensory signals that our brains process are often noisy and ambiguous, as are the representations that our brains form in response to them at the earliest processing stages. Complementary computing and hierarchical resolution of uncertainty overcome these problems until perceptual representations that are sufficiently complete, context-sensitive, and stable can be formed. The brain regions where these representations are completed are different for seeing, hearing, feeling, and knowing. But then how do our brains select these representations? My proposed answer is: A resonant state is generated that selectively "lights up" these representations and thereby renders them conscious. These conscious representations can then be used to trigger effective behaviours.

Consciousness hereby enables our brains to prevent the noisy and ambiguous information that is computed at earlier processing stages from triggering actions that could lead to disastrous consequences. Conscious states thus provide an extra degree of freedom whereby the brain ensures that its interactions with the environment, whether internal or external, are effective as possible, given the information at hand. These conclusions will be supported by theoretical analysis throughout the book that enable explanations of many psychological and neurobiological data about normal and clinical behaviours that have no other mechanistic explanations at the present time.
Grossberg's comments for some well-known consciousness theories
Grossberg 2021 p047c2h0.20 the neural global workspace model that was published in 2014 by Stanislas Dehaene (Dehaene 2014) builds upon the global workspace of Bernard Baars (Baars 2005) and claims that "consciousness is global information broadcasting within the cortex [to achieve] massive sharing of pertinent information throughout the brain" (p13). Dehaene also makes a number of other useful observations, including that "the time that our conscious vision spends entertaining an interpretation is directly related to its likelihood, given the sensory information received" (p97) and that "the conditioning paradigm suggests that consciousness has a specific evolutionary role: learning over time, rather than simply living in the instant. The system formed by the prefrontal cortex and its interconnected areas, including the hippocampus, may serve the essential role of bridging temporal gaps" (p103). Such claims are consistent with the analysis in this book, but they do not describe the underlying organizational principles, neural mechanisms, or brain representations that embody subjective conscious aspects of experience.
Grossberg 2021 p048c1h0.25 In 2012 and 2015, Tononi further develops postulates for his Integrated Information Theory (IIT) for physical systems that include consciousness (Tononi 2012, 2015). These postulates are intrinsic existence, compositionality, information, integration, and exclusion, These postulates summarize some basic facts about consciousness, but do not explain them. p048c1h0.85 Both Dehaene and Tononi used the word "information" as a critical component of their hypothesis. But what is "information"? The scientific concept of "information" in the mathematical sense of Information Theory was defined and characterized in 1948 by the great American mathematician, electrical engineer, and cryptographer Claude Shannon (Shannon 1948). ... In order to even discuss what "information" is requires that a set of states exist whose information can be computed, and that fixed probabilities exist for transitions between these states. In contrast, the brain is a self-organizing system that continuously creates new states through development and learning, and whose probability structure is continually changing along with them. Without a theory that explains how their transition probabilities may change through time in response to changing environmental statistics, the classical concept of information is useless. How such states arise is a key explanatory target of ART, and is one reason why ART can offer a classification of the resonances that are proposed to embody specific conscious experiences.
Grossberg 2021 p048c2h0.90 Perhaps the theory of the highly influential Portugese-American neuroscientist and author, Antonio Damasio, comes closest to theoretically linking brain to mind in his beautifully written 1999 book with the title The Feeling of What Happens: Body and Emotion in the making of consciousness (Damasio 1999). ... Damasio used the cliical data that he elegantly summarized in his book to guide him to what is, in effect, a heuristic derivation of the Cognitive-Emotional-Motor, or CogEM, model that I had first published in 1991. ... Unlike CogEM and its refinements, Damasio's theory provides no mechanistic account, and could therefore provide no data simulations or predictions based on the model's emergent properties. Nor could he situate his heuristic concepts within a larger theory of how brain resonances and conciousness may be linked.
Grossberg 2021 p204c2h0.30 In their 1990 article, Crick and Koch described two forms of consciousness : "a very fast form, linked to iconic memory...; and a slower one [wherein] an attentional mechanism transiently binds together all those neurons whose activity relates to the relevant features of a single visual object]". This conclusion was consistent with available results about ART at that time, but did not offer a linking hypothesis between brain dynamics and the perceptual, cognitive, and cognitive-emotional representations whose resonances support different conscious qualia. A great deal of additional experimental evidence for neural correlates of consciousness has been reported since 1990, but has typically led to theoretical conclusions that fail to make the crucial linking hypothesis between specific conscious pyschological qualia (eg Baars 2005; Dehaene 2014; Dennett 1991; Edelman, Tononi 2000; Koch etal 2016; Tononi 2004, 2015). ... Such claims are consistent with the analysis in this book, but they do not describe the underlying organizational principles, neural mechanisms, or brain representations that embody subjective conscious aspects of experience.
p040 Howell 24Jul2023, Grossberg 2021 p040c2h0.80 : so I must sometimes be cutting off my thinking at a sub-conscious level, which explains my "disastrous consequences"...
p040 Howell 24Jul2023, Grossberg 2021 p040c2h0.80 : It appears to me that the same [microcircuit, modal architecture] in the brain may be conscious in one situation (for example when producing results for simple decision-making), and non-conscious in another (when the outputs must be considered in the context of more complex, multiple [modes, environmental scenarios]?).
p044 Howell: grepStr 'conscious' - A linking hypothesis between resonant brain dynamics and the conscious mind
p184 Howell: grepStr 'conscious' - including: Some other approaches to understanding consciousness (Grossberg 2021 p47c1h0.5) :
p184 Howell: grepStr 'conscious' - rapidly learn to categorize and recognize so many objects in the world
p184 Howell: grepStr 'conscious' - remember this information as well as we do over a period years
p184 Howell: grepStr 'conscious' - learn to expect and anticipate events that may occur in familiar situations
p184 Howell: grepStr 'conscious' - pay attention to events that are of particular interest to us
p184 Howell: grepStr 'conscious' - become conscious of these events
p184 Howell: grepStr 'conscious' - balance between expected and unexpected events, and orient to unexpected events
p184 Howell: grepStr 'conscious' - engage in fantasy activities such as visual imagery, internalized speech, and planning
p184 Howell: grepStr 'conscious' - learn language quickly and consciously hear completed speech sounds in noise
p184 Howell: grepStr 'conscious' - hallucinate during mental disorders
p184 Howell: grepStr 'conscious' - "... I claim that a common set of brain mechnisms controls all of these processes. Adaptive Resonance Theory, or ART, has been incrementally developed to explain what these mechanisms are, and how they work and interact, since I introduced it in 1976 [Grossberg, 1976a, 1976b] and it was incrementally developed in many articles to the present, notably with the help and leadership of Gail Carpenter, as I will elaborate on below. There are many aspects of these processes that are worth considering. For example, we need to understand between... ..." [Grossberg 2021 p184]
p419fig12.17 Howell: important indicator of resonance, which is a requirement for consciousness (but not all resonance is conscious)
Howell: grepStr 'auditory continuity' - DeLiang Wang "Auditory scene analysis"
Grossberg: The auditory continuity illusion illustrates the ART Matching Rule at the level of auditory streaming. Its "backwards in time" effect of future context on past conscious perception is a signature of resonance. ||
Howell: Do you have to understand the subconscious processes in order to understand conscious processes? 1. Yourself - No. That's kind of a definition of consciousness or its effect. We do not understand the details of the subconscious, and don't know how it works, and that's fine. [Philosopy, classical science] are based on simple [rational, logical, scientific] reasoning, and they work OK. 2. Conscious systems construction: YES, you do have to undeerstand sub-conscious processes, and in order to do that you have to tie into them.
Howell: 11Jun2023 Does one need to understand sub-conscious [architecture, process]s in order to understand consciousness? For most people, NO. Consciousness is all you need, for example almost all concepts of consiousness to date, from philosophy to [Bernard Baars, Dehaene global workspace model] and almost all other concepts. For those building substantive models of consciousness, YES. [Philosophy, Poetry, Yapping and arm-waving] aren't enough, and don't lead to [profound, workable] understanding. Still, they may be inspiring, there are [needles in the haystack, gems in the rough], and a few may lead somewhere.

conscious vs non-conscious

grepStr = non-conscious|nonconscious|unconscious

p516sec Conscious vs. non-conscious emotions p004idx unconscious inference, also pp 41,154,335,355
p069idx unconscious inference, Helmholtz's theory
p040 Howell 24Jul2023, Grossberg 2021 p040c2h0.80 : It appears to me that the same [microcircuit, modal architecture] in the brain may be conscious in one situation (for example when producing results for simple decision-making), and non-conscious in another (when the outputs must be considered in the context of more complex, multiple [modes, environmental scenarios]?).

behavior-mind-brain link

grepStr = behavior-mind-brain link

Grossberg 2021 p081-83 sub-section: How does one evolve a computational brain?
- Behavioral Success drives::>> Behavioral Data -> Art of Modelling -> Design Principles -> Mathematical Models -> Method of Minimal Anatomies (Occam's Razor) -> Behavioral Predictions -> explain Neural Data -> novel Brain Predictions <<::links behavior-mind-brain
- "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything.
- >characterize the boundary between the known and unknown: the shape of this boundary helps to direct attention to new design principles
(keys : behavior-mind-brain link)
Grossberg 2021 p081c2h0.66 sub-section: How does one evolve a computational brain?
The above discussion illustrates that no single step of theoretical derivation can derive a whole brain. One needs a method for deriving a brain in stages, or cycles, much as evolution has incrementally discovered ever more complex brains over many thousands of years. The following theoretical method has been successfully applied many times since I first used it in 1957. It embodies a kind of conceptual evolutionary process for deriving a brain.

Because "brain evolution needs to achieve behavioural success", we need to start with data that embodiey indices of behavioral success. That is why, as illustrated in Figure 2.37 Modelling method and cycle, one starts with Behavioral Data from scores or hundreds of psychological experiments. These data are analyszed as the result of an individual adapting autonomously in real time to a changing world. This is the Arty of Modeling. It requires that one be able to infer from static data curves the dynamical processes that control individual behaviors occuring in real time. One of the hardest things that I teach to my students to do is "how to think in real time" to be able to carry out this speculative leap.

Properly carried out, this analysis leads to the discovery of new Design Principles that are embodied by these behavioral processes. The Design Principles highlight the functional meaning of the data, and clarify how individual behaviors occurring in real time give rise to these static data curves.

These principles are then converted into the simplest Mathematical Model using a method of minimal anatomies, which is a form of Occam's Razor, or principle of parsimony. Such a mathematical model embodies the psychological principles using the simplest possible differential equations. By "simplest" I mean that, if any part of the derived model is removed, then a significant fraction of the targeted data could no longer be explained. One then analyzes the model mathematically and simulates it on the computer, showing along the way how variations on the minimal anatomy can realize the design principles in different individuals or species.

This analysis has always provided functional explanations and Behavioral Predictions for much larger behavioral data bases than those used to discover the Design Principles. The most remarkable fact is, however, that the behaviorally derived model always looks like part of a brain, thereby explaining a body of challenging Neural Data and making novel Brain Predictions.

The derivation hereby links mind to brain via psychological organizational principles and their mechanistic realization as a mathematically defined neural network. This startling fact is what I first experienced as a college Freshman taking Introductory Psychology, and it changed my life forever.

I conclude from having had this experience scores of times since 1957 that brains look the way they do because they embody a natural computational realization for controlling autonomous adaptation in real-time to a changing world. Moreover, the Behavior -> Principles -> Model -> Neural derivation predicts new functional roles for both known and unknown brain mechanisms by linking the brain data to how it helps to ensure behavioral success. As I noted above, the power of this method is illustrated by the fact that scores of these predictions about brain and behavior have been supported by experimental data 5-30 years after they were first published.

Having made the link from behavior to brain, one can then "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything. At each stage of development, the model can cope with certain environmental challenges but not others. An important part of the mathematical and computational analysis is to characterize the boundary between the known and unknown; that is which challenges the model can cope with and which it cannot. The shape of this boundary between the known and unknown helps to direct the theorist's attention to new design principles that have been omitted from previous analysis.

The next step is to show how these new design principles can be incorporated into the evolved model in a self-consistent way, without undermining its previous mechanisms, thereby leading to a progressively more realistic model, one that can explain and predict ever more behavioral and neural data. In this way, the model undergoes a type of evolutionary development, as it becomes able to cope behaviorally with environmental constraints of ever increasing subtlety and complexity. The Method of Minimal Anatomies may hereby be viewed as way to functionally understand how increasingly demanding combinations of environmental pressures were incorporated into brains during the evolutionary process.

If such an Embedding Principle cannot be carried out - that is, if the model cannot be unlumped or refined in a self-consistent way - then the previous model was, put simply, wrong, and one needs to figure out which parts must be discarded. Such a model is, as it were, an evolutionary dead end. Fortunately, this has not happened to me since I began my work in 1957 because the theoretical method is so conservative. No theoretical addition is made unless it is supported by multiple experiments that cannot be explained in its absence. Where multiple mechanistic instantiations of some Design Principles were possible, they were all developed in models to better underestand their explanatory implications. Not all of these instantiations could survive the pressure of the evolutionary method, but some always could. As a happy result, all earlier models have been capable of incremental refinement and expansion.

The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved.

Howell: bold added for emphasis.
(keys : Principles-Principia, behavior-mind-brain link, brain evolution, cycle of model evolution)
see also quotes: Charles William Lucas "Universal Force" and others (not retyped yet).

classical mind-brain problem

grepStr = mind-body -classical- problem

Grossberg 2021 p081c2h0.66 The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved
(keys : mind-body -classical- problem)

Grossberg: other consciousness theories

p480sec Cognitive-emotional dynamics: Beyond Chomsky and Skinner
image p484fig13.04 The top-down feedback from the orbitofrontal cortex closes a feedback loop that supports a cognitive-emotional resonance. If this resonance can be sustained long enough, it enables us to have feelings at the same time that we experience the categories that caused them.
|| Cognitive-Emotional resonance. Basis of "core consciousness" and "the feeling of what happens". (Damasio 1999) derives heuristic version of CogEM model from his clinical data. Drive-> amygdala-> prefrontal cortex-> sensory cortex, resonance around the latter 3. How is this resonance maintained long enough to become conscious?
image p514fig13.44 Analog of the COgEM model in Figure 6.1 of (Damasio 1999).
|| (a) map of object X-> map of proto-self at inaugural instant-> [, map of proto-self modified]-> assembly of second-order map. (b) map of object X enhanced-> second-order map imaged. p048idx consciousness Explained, (Dennett), also p135
Grossberg 2021 p047c2h0.20 the neural global workspace model that was published in 2014 by Stanislas Dehaene (Dehaene 2014) builds upon the global workspace of Bernard Baars (Baars 2005) and claims that "consciousness is global information broadcasting within the cortex [to achieve] massive sharing of pertinent information throughout the brain" (p13). Dehaene also makes a number of other useful observations, including that "the time that our conscious vision spends entertaining an interpretation is directly related to its likelihood, given the sensory information received" (p97) and that "the conditioning paradigm suggests that consciousness has a specific evolutionary role: learning over time, rather than simply living in the instant. The system formed by the prefrontal cortex and its interconnected areas, including the hippocampus, may serve the essential role of bridging temporal gaps" (p103). Such claims are consistent with the analysis in this book, but they do not describe the underlying organizational principles, neural mechanisms, or brain representations that embody subjective conscious aspects of experience.
Grossberg 2021 p048c1h0.25 In 2012 and 2015, Tononi further develops postulates for his Integrated Information Theory (IIT) for physical systems that include consciousness (Tononi 2012, 2015). These postulates are intrinsic existence, compositionality, information, integration, and exclusion, These postulates summarize some basic facts about consciousness, but do not explain them. p048c1h0.85 Both Dehaene and Tononi used the word "information" as a critical component of their hypothesis. But what is "information"? The scientific concept of "information" in the mathematical sense of Information Theory was defined and characterized in 1948 by the great American mathematician, electrical engineer, and cryptographer Claude Shannon (Shannon 1948). ... In order to even discuss what "information" is requires that a set of states exist whose information can be computed, and that fixed probabilities exist for transitions between these states. In contrast, the brain is a self-organizing system that continuously creates new states through development and learning, and whose probability structure is continually changing along with them. Without a theory that explains how their transition probabilities may change through time in response to changing environmental statistics, the classical concept of information is useless. How such states arise is a key explanatory target of ART, and is one reason why ART can offer a classification of the resonances that are proposed to embody specific conscious experiences.
Grossberg 2021 p048c2h0.25 The influential philosopher of mind, Daniel Dennett, wrote a highly cited book in 1991 with the arresting title Conciousness Explained (Dennett 1991). In this book, Dennett argued against the a Cartesian Theater model; that is, a place in the brain where "it all comes together" and generates subjective judgements. Instead, Dennett advocated a Multiple Drafts model where discriminations are distributed in space and time across the brain, a concept that, without elaboration, is too vague to have explanatory power.
Grossberg 2021 p048c2h0.90 Perhaps the theory of the highly influential Portugese-American neuroscientist and author, Antonio Damasio, comes closest to theoretically linking brain to mind in his beautifully written 1999 book with the title The Feeling of What Happens: Body and Emotion in the making of consciousness (Damasio 1999). ... Damasio used the cliical data that he elegantly summarized in his book to guide him to what is, in effect, a heuristic derivation of the Cognitive-Emotional-Motor, or CogEM, model that I had first published in 1991. ... Unlike CogEM and its refinements, Damasio's theory provides no mechanistic account, and could therefore provide no data simulations or predictions based on the model's emergent properties. Nor could he situate his heuristic concepts within a larger theory of how brain resonances and conciousness may be linked.
Grossberg 2021 p204c2h0.30 In their 1990 article, Crick and Koch described two forms of consciousness : "a very fast form, linked to iconic memory...; and a slower one [wherein] an attentional mechanism transiently binds together all those neurons whose activity relates to the relevant features of a single visual object]". This conclusion was consistent with available results about ART at that time, but did not offer a linking hypothesis between brain dynamics and the perceptual, cognitive, and cognitive-emotional representations whose resonances support different conscious qualia. A great deal of additional experimental evidence for neural correlates of consciousness has been reported since 1990, but has typically led to theoretical conclusions that fail to make the crucial linking hypothesis between specific conscious pyschological qualia (eg Baars 2005; Dehaene 2014; Dennett 1991; Edelman, Tononi 2000; Koch etal 2016; Tononi 2004, 2015). ... Such claims are consistent with the analysis in this book, but they do not describe the underlying organizational principles, neural mechanisms, or brain representations that embody subjective conscious aspects of experience.
Howell: 11Jun2023 Does one need to understand sub-conscious [architecture, process]s in order to understand consciousness? For most people, NO. Consciousness is all you need, for example almost all concepts of consiousness to date, from philosophy to [Bernard Baars, Dehaene global workspace model] and almost all other concepts. For those building substantive models of consciousness, YES. [Philosophy, Poetry, Yapping and arm-waving] aren't enough, and don't lead to [profound, workable] understanding. Still, they may be inspiring, there are [needles in the haystack, gems in the rough], and a few may lead somewhere.

random fun themes

art (painting etc)

grepStr = DaVinci|Monet|Matisse|Seurat

p105sec From Seurat to SAR
p106sec Did Matisse know that "all boundaries are invisible"?
p110sec Claude Monet and Impressionist painting: Seeing the forest and the leaves
p118sec Back to Monet: Gist, multiple-scale attentional shrouds, and scene recognition
p171sec How are DaVinci stereopsis surfaces that are monocularly viewed seen in depth?
p177sec V2 and V4: Recognizing occluded objects, seeing unoccluded surfaces, and DaVinci again
p377sec Explaining DaVinci stereopsis percepts without invoking a biological optics hypothesis
p380sec Surface-to-boundary surface contours and fixation plane bias contribute to DaVinci percepts
image p105fig03.23 The pointillist painting A Sunday on la Grande Jatte by Georges Seurat illustrates how we group together both large-scale coherence among the pixels of the painting, as well as forming small groupings around the individual dabs of color.
||
image p107fig03.25 The Roofs of Collioure by Matisse. See the text for details
|| p107c1h0.6 "... [Matisse] showed how patches of pure color, when laid down properly on a canvas, could be grouped by the brain into emergent boundarues, without the intervention of visible outlines. ... The trick was that these emergent boundaries, being invisible, or amodal, did not darken the colors in the surface representations. In this sense, Matisse intuitively realized that "all boundaries are invisible" through the masterful way in which he arranged his colors on canvas to generate boundaries that could support compelling surface representations. ..."
image p108fig03.27 Matisse's painting Open Window, Collioure 1905 combines continuously colored surfaces with color patches that created surface representations using amodal boundaries, as in Figure 3.26. Both kinds of surfaces cooperate to form the final painterly percept.
||
image p110fig03.32 Claude Monet's painting of Poppies Near Argenteuil. See the text for details.
|| Claude Monet Poppies Near Argenteuil 1873. p110c2h0.35 "... the red poppies and the green field around them are painted to have almost the same luminescence; that is, they are almost equiluminant. As a result, the boundaries between the red and green regions are weak and positionally unstable, thereby facilitating an occasional impression of the poppies moving in a gentle breeze, especially as one's attention wanders over the scene. ...".
image p120fig03.43 Four paintings by Monet of the Rouen cathedral under different lighting conditions (top row) and their monochromatic versions (bottom row). See the text for details.
|| p119c2h0.25 "... Monet uses nearby colors that are nearly equiluminant, and sharp, high-contrast luminance defined edges are sparse. He hereby creates weaker boundary signals within and between the parts of many forms, and stronger boundary signals between the forms. This combination facilitates color spreading within the forms and better separation of brightness and collor differences between forms. ... The grayscale versions of these paintings demonstrate the near equiluminance of the brushstrokes within forms, and places in which brightness and color differences significantly influence the groupings that differentiate between forms, including the differentiation between the cathedral and the sky. ..."
image p120fig03.44 The Rouen cathedral at sunset generates very different boundary webs than it does in full sunlight, as illustrated by Figure 3.45.
|| Rouen Cathedral at sunset (Monet 1892-1894).
- Lighting almost equiluminant
- Most boundaries are thus caused by color differences, not luminance differences
- Fine architectural details are obscured, leading to...
- Coarser and more uniform boundary webs, so...
- Less depth in the painting.
image p121fig03.45 The Rouen cathedral in full sunlight.
|| Rouen Cathedral full sunlight (Monet 1892-1894).
- Lighting is strongly non-uniform across most of the painting
- Strong boundaries due to both luminance and color differences
- Fine architectural details are much clearer, leading to...
- Finer and more non-uniform boundary webs, so...
- Much more detail and depth
image p121fig03.46 The Rouen cathedral in full sunlight contains T-Junctions that are not salient in the painting of it at sunset. These are among the painting's features that give it a much more depthful appearance.
|| Rouen Cathedral full sunlight (Monet 1892-1894).
- There are also more T-junctions where vertical boundaries occlude horizontal boundaries, or conversely...
- Leading to more depth.
p119c2h1.0 "... Such T-junction boundary occlusions ... can generate percepts of depth in the absence of any other visual clues. ...".
image p171fig04.49 An example of DaVinci stereopsis in which the left eye sees more of the wall between A and C than the right eye does. The region between B and C is seen only by the left eye because the nearer wall between C and D occludes it from the right eye view.
image p377fig11.11 DaVinci stereopsis phenomena occur when only one eye can receive visual inputs from part of a 3D scene due to occlusion by a nearer surface.
|| How does monocular information contribute to depth perception? DaVinci steropsis (Gillam etal 1999). Only by utilizing monocular information can visual system create correct depth percept. [left, right] eye view
image p379fig11.13 How the 3D LAMINART model explains DaVinci stereopsis. All the stages of boundary and surface formation are color coded to clarify their explanation. Although each mechanism is very simple, when all of them act together, the correct depthful surface representation is generated. See the text for details.
|| DaVinci stereopsis (Nakayama, Shimojo 1990). An emergent property of the previous simple mechanisms working together. [V1 binocular boundaries -> V2 initial boundaries -> V2 final boundaries -> V4 surface] pair [Binocular match: boundaries of thick bar -> Add monocular boundaries along lines-of-sight -> Line-of-sight inhibition kills weaker vertical boundaries -> 3D surface percept not just a disparity match!] pair [Binocular match: right edge of thin and thick bars -> Strongest boundaries: binocular and monocular boundaries add -> Vertical boundaries from monocular left edge of thin bar survive -> Filling-in contained by connected boundaries]. cart [very near, near, fixation plane, far, very far]
image p380fig11.14 The model explanation of DaVinci stereopsis when the input stimuli have opposite contrast polarities.
|| Polarity-reversed Da Vinci stereopsis (Nakayama, Shimojo 1990). Same explanation! (... as Figure 11.13 ...) [V1 binocular boundaries -> V2 initial boundaries -> V2 final boundaries -> V4 surface] cart [very near, near, fixation plane, far, very far]
image p381fig11.15 The same model mechanisms explain the surface percept that is generated by the variant of DaVinci stereopsis that Gillam, Blackburn, and Nakayama studied in 1999.
|| DaVinci stereopsis (Gillam, Blackburn, Nakayama 1999). same model mechanisms. [V1 binocular boundaries -> V2 initial boundaries -> V2 final boundaries -> V4 surface] cart [very near, near, fixation plane, far, very far]
image p382fig11.16 The version of DaVinci steropsis wherein three narrow rectangles are binocularly matched with one thick rectangle can also be explained is a similar way.
|| DaVinci stereopsis of [3 narrow, one thick] rectangles (Gillam, Blackburn, Nakayama 1999). [V1 binocular boundaries -> V2 initial boundaries -> V2 final boundaries -> V4 surface] cart [very near, near, fixation plane, far, very far]

biology, evolution, paleontology

grepStr = biology|evolution|paleontology|brain evolution

p618 Chapter 17 A universal development code - Mental measurements embody universal laws of cell biology and physics
p029sec Two revolutionary paradigms: [complementary, laminar] computing
p053sec A century of controversy as major scientific revolution is born
p070sec Cooperation and comptition are universal in biology, including in brain networks
p179sec An evolutionary design tension between requirements of recognition and reaching
p490sec Universal evolutionary constraints: The synchronization and persistence problems
p495sec Homologous sensory and drive representations: Evolutionary precursors?
p500sec Transmitter acculmulation and release: Infinity does not exist in biology! Mental measurements embody universal laws of cell biology and physics
p625sec A universal development code for biology: Computing with cellular patterns
image p073fig02.19 Computing with cells: infinity does not exist in biology!
|| Computing in a bounded activity domain, Gedanken experiment (Grossberg 1970). Vm sub-areas [xm, B - xm], I(all m)], m=[1, i, B].
B excitable sites
xi(t) excited sites (activity, potential)
B - xi(t) unexcited sites
image p082fig02.37 My models begin with behavioral data, since brains are designed to achieve behavioral success. The text explains how models evolve in stages, through a process of successive refinements, or unlumpings. These unlumpings together carry out a kind of conceptual evolution, leading to models that can explain and predict ever larger psychological and neurobiological databases.
|| Modelling method and cycle.
Behavioral data -(art of modeling)-> Design principles <- Neural data <-(brain predictions)- Mathematical model and analysis -(behavioral predictions)-> Behavioural data
Operationalizes "proper level of abstraction"
Operationalizes that you cannot "derive a brain" in one step.
image p501fig13.26 A simple differential equation describes the processes of transmitter accumulation and release that do their best, at a finite rate, to carry out unbiased transduction.
|| Transmitter accumulation and release. Transmitter y cannot be restored at an infinite rate: T = S*ym y ~= B, Differential equations: d[dt: y] = A*(B - y) - S*y = accumulate - release. Transmitter y tries to recover to ensure unbiased transduction. What if it falls behind? Evolution has exploited the good properties that happen then. p040idx consciousness, evolution of
Grossberg 2021 page xii I will explain that "all conscious states are resonant states". The importance of this assertion motivated the title of this book. I will describe the resonances that seem to underlie our conscious experiences of seeing, hearing, feeling, and knowing. These explanations will include where in the brain these resonances take place, how they occur there, and why evolution may have been driven to discover conscious mental states in the first place. I will also clarify how these resonances interact when we simultaneously see, hear, feel, and know something, all at once, about a person or na event in our world. This description is part of a burgeoning classification of resonances. I will also explain why not all resonant states are conscious, and why not all brain dynamics are resonant. These results contribute to solving what has been called the Hard Problem of Consciousness.
Grossberg 2021 p081-83 The cycle of model evolution :
- The next step is to show how these new design principles can be incorporated into the evolved model in a self-consistent way, without undermining its previous mechanisms, thereby leading to a progressively more realistic model, one that can explain and predict ever more behavioral and neural data.
- In this way, the model undergoes a type of evolutionary development, as it becomes able to cope behaviorally with environmental constraints of ever increasing subtlety and complexity.
- The Method of Minimal Anatomies may hereby be viewed as way to functionally understand how increasingly demanding combinations of environmental pressures were incorporated into brains during the evolutionary process.
(keys : brain evolution, cycle of model evolution)
Grossberg 2021 p081c2h0.66 The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved
(keys : mind-body -classical- problem)
Grossberg 2021 p081c2h0.66 sub-section: How does one evolve a computational brain?
The above discussion illustrates that no single step of theoretical derivation can derive a whole brain. One needs a method for deriving a brain in stages, or cycles, much as evolution has incrementally discovered ever more complex brains over many thousands of years. The following theoretical method has been successfully applied many times since I first used it in 1957. It embodies a kind of conceptual evolutionary process for deriving a brain.

Because "brain evolution needs to achieve behavioural success", we need to start with data that embodiey indices of behavioral success. That is why, as illustrated in Figure 2.37 Modelling method and cycle, one starts with Behavioral Data from scores or hundreds of psychological experiments. These data are analyszed as the result of an individual adapting autonomously in real time to a changing world. This is the Arty of Modeling. It requires that one be able to infer from static data curves the dynamical processes that control individual behaviors occuring in real time. One of the hardest things that I teach to my students to do is "how to think in real time" to be able to carry out this speculative leap.

Properly carried out, this analysis leads to the discovery of new Design Principles that are embodied by these behavioral processes. The Design Principles highlight the functional meaning of the data, and clarify how individual behaviors occurring in real time give rise to these static data curves.

These principles are then converted into the simplest Mathematical Model using a method of minimal anatomies, which is a form of Occam's Razor, or principle of parsimony. Such a mathematical model embodies the psychological principles using the simplest possible differential equations. By "simplest" I mean that, if any part of the derived model is removed, then a significant fraction of the targeted data could no longer be explained. One then analyzes the model mathematically and simulates it on the computer, showing along the way how variations on the minimal anatomy can realize the design principles in different individuals or species.

This analysis has always provided functional explanations and Behavioral Predictions for much larger behavioral data bases than those used to discover the Design Principles. The most remarkable fact is, however, that the behaviorally derived model always looks like part of a brain, thereby explaining a body of challenging Neural Data and making novel Brain Predictions.

The derivation hereby links mind to brain via psychological organizational principles and their mechanistic realization as a mathematically defined neural network. This startling fact is what I first experienced as a college Freshman taking Introductory Psychology, and it changed my life forever.

I conclude from having had this experience scores of times since 1957 that brains look the way they do because they embody a natural computational realization for controlling autonomous adaptation in real-time to a changing world. Moreover, the Behavior -> Principles -> Model -> Neural derivation predicts new functional roles for both known and unknown brain mechanisms by linking the brain data to how it helps to ensure behavioral success. As I noted above, the power of this method is illustrated by the fact that scores of these predictions about brain and behavior have been supported by experimental data 5-30 years after they were first published.

Having made the link from behavior to brain, one can then "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything. At each stage of development, the model can cope with certain environmental challenges but not others. An important part of the mathematical and computational analysis is to characterize the boundary between the known and unknown; that is which challenges the model can cope with and which it cannot. The shape of this boundary between the known and unknown helps to direct the theorist's attention to new design principles that have been omitted from previous analysis.

The next step is to show how these new design principles can be incorporated into the evolved model in a self-consistent way, without undermining its previous mechanisms, thereby leading to a progressively more realistic model, one that can explain and predict ever more behavioral and neural data. In this way, the model undergoes a type of evolutionary development, as it becomes able to cope behaviorally with environmental constraints of ever increasing subtlety and complexity. The Method of Minimal Anatomies may hereby be viewed as way to functionally understand how increasingly demanding combinations of environmental pressures were incorporated into brains during the evolutionary process.

If such an Embedding Principle cannot be carried out - that is, if the model cannot be unlumped or refined in a self-consistent way - then the previous model was, put simply, wrong, and one needs to figure out which parts must be discarded. Such a model is, as it were, an evolutionary dead end. Fortunately, this has not happened to me since I began my work in 1957 because the theoretical method is so conservative. No theoretical addition is made unless it is supported by multiple experiments that cannot be explained in its absence. Where multiple mechanistic instantiations of some Design Principles were possible, they were all developed in models to better underestand their explanatory implications. Not all of these instantiations could survive the pressure of the evolutionary method, but some always could. As a happy result, all earlier models have been capable of incremental refinement and expansion.

The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved.

Howell: bold added for emphasis.
(keys : Principles-Principia, behavior-mind-brain link, brain evolution, cycle of model evolution)
see also quotes: Charles William Lucas "Universal Force" and others (not retyped yet).
Grossberg 2021 p047c2h0.20 the neural global workspace model that was published in 2014 by Stanislas Dehaene (Dehaene 2014) builds upon the global workspace of Bernard Baars (Baars 2005) and claims that "consciousness is global information broadcasting within the cortex [to achieve] massive sharing of pertinent information throughout the brain" (p13). Dehaene also makes a number of other useful observations, including that "the time that our conscious vision spends entertaining an interpretation is directly related to its likelihood, given the sensory information received" (p97) and that "the conditioning paradigm suggests that consciousness has a specific evolutionary role: learning over time, rather than simply living in the instant. The system formed by the prefrontal cortex and its interconnected areas, including the hippocampus, may serve the essential role of bridging temporal gaps" (p103). Such claims are consistent with the analysis in this book, but they do not describe the underlying organizational principles, neural mechanisms, or brain representations that embody subjective conscious aspects of experience.
p416fig12.13 Howell: DIRECT model reaching: reminds me of awesome work by Jan Peters & colleagues (Technische Universitaet Darmstadt, Germany) "Robot Learning" (he zoomed 21Jun2022)
Howell: DIRECT model reaching: reminds me of Yoonsuck Choe etal work on Evolutionary Computation: autonomous emergence of tool use.
Grossberg: The DIRECT model learns, using a circular reaction that is energized by an Endogenous Random Generator, or ERG, to make motor-equivalent volitionally-activated reaches. This circular reaction learns a spatial representation of a target in space. It can hereby make accurate reaches with clamped joints and on its first try using a tool under visual guidance; see Figure 12.16. || DIRECT model (Bulloch, Grossberg, Guenther 1993). learns by circular reaction. learns spatial reresentation to me4diate between vision and action. motor-equivalent reaching. can reach target with clamped joints. can reach target with a TOOL on the first try under visual guidance. How did tool use arise?!

brain disorders and disease

p021sec Learned helplessness in the lab and society From vigilant conscious awareness to autism, amnesia, and Alzheimer's disease
p184sec hallucinate during mental disorders
p188sec HM, hippocampus, and amnesia
p196sec We learn, therefore we can imagine, think, plan, and hallucinate
p227sec Memory consolidation, amnesia, and lesions of amygdala, hippocampus, and neocortex
p237sec Vigilence diseases: Autism, media temporal amnesia, and Alzheimer's disease
p237sec High vigilance and hyperspecific category learning in autism
p237sec Low vigilance in corticohippocampal dynamics during medial temporal amnesia
p242sec Is there a collapse of tonic and phasic vigilance control during Alzheimer's disease?
p244sec Relating sleep and Alzheimer disease pathology
p247sec From grouping and attention during waking to Up and Down states during slaw wave sleep
p248sec Slow wave sleep can disrupt and be disrupted by Alzheimer's disease: A vicious circle
p248sec Can novelty-seeking behaviours lower the chance of developing Alzheimer's?
p248sec Sleep disturbances in autism and schizophrenia: Due to vigilance abnormalities?
p259sec Visual agnosia
p278sec Agnosia: Parietal attention and intention control seeing and reaching without knowing
p507sec Inverted-U and arousal: Golden Mean, depression, autism, schizophrenia, and ADHD
p515sec Breakdowns during mental disorders: Theory of Mind, autism, and schizophrenia
p556sec Interactions of multiple imbalanced brain regions during autism
p562sec A calcium spectrum in HeLa cancer cells, Xenopus oocytes, and cardiac myocytes
p568sec Prolonged gate opening and involuntary repetitive movements in autism
p622sec The Dark Side: Learned helplessness, self-punitive behaviors, and fetishes
image p248fig05.49 This circuit of the LAMINART model helps to explain properties of Up and Down states during slow wave sleep, and how disturbances in ACh dynamics can disrupt them.
||
image p278fig06.28 If the feature-category resonances cannot form, say due to a lesion in IT, then a surface-shroud resonance can still support conscious seeing of an attended object, and looking at or reaching for it, even if the individual doing so knows nothing about the object, as occurs during visual agnosia. The surface-shroud resonance supports both spatial attention and releases commands that embody the intention to move towards the attended object.
|| What kinds of resonances support knowing vs seeing? visual agnosia: reaching without knowing Patient DF (Goodale etal 1991). Attention and intention both parietal cortical functions (Anderson, Essick, Siegel 1985; Gnadt, Andersen 1988; Synder, Batista, Andersen 1997, 1998)
image p557fig15.26 Brain regions and processes that contribute to autistic behavioral symptoms when they become imbalanced in prescribed ways.
|| Basal Gamglia prolonged gate opening <-> { Amygdala emotionally depressed-> [hippocampus- hyperspecific learning; Cerebellum- adaptive timing fails; hypofrontal blocking fails, no Theory of Mind]-> Neocortex; Neocortex- rewards not received-> Amygdala}.
p184 Howell: grepStr 'conscious' - hallucinate during mental disorders
p598fig16.34 Howell: wow! theta rythm modulates place and grid cell firing? Wolfgang Singer: gamma dis-synchrony and schizophrenia (and/or autism)? Goes with theme of Schumann electro-magnetic resonances in atmosphere between ionosphere & ground?
Grossberg: The spiking GridPlaceMap model generates theta-modulated place and grid cell firing, unlike the rate-based model. || Theta-modulated cells in spiking model. [place, grid] cell vs [membrane potential (mV vs time), frequency vs inter-spike intervals (s), power spectra (normalized power vs frequency (Hz))].

hippocampus IS a cognitive map!

grepStr = hippocampus|hippocampal|cognitive map

p188sec HM, hippocampus, and amnesia
p227sec Memory consolidation, amnesia, and lesions of amygdala, hippocampus, and neocortex
p233sec Beta oscillations during the learning of hippocampal place fields in novel environments
p234sec Inverted-U in beta power through time during hippocampal place cell learning
p237sec Low vigilance in corticohippocampal dynamics during medial temporal amnesia
p532sec Perirhinal and parahippocampal cortices store object and spatial contexts
p540sec Conditioing and consciousness: Trace conditioning, hippocampus, and "time cells"
p549sec Coordinating adaptively timed learning in the hippocampus and cerebellun
p552sec Hippocampal, amygdala, and orbitofrontal lesions affect memory consolidation
p553sec Why do early hippocampal lesions interfere with memory consolidation but late ones do not?
p555sec Two kinds of hippocampally-mediated memory consolidation
p573sec Place cells and the hippocampus as a cognitive map
p585sec How do hippocampal place cells learn to represent much larger spaces than grid cells can?
p600sec An ART spatial category learning system: The hippocampus IS a cognitive map!
p600sec The hippocampus is MORE than a cognitive map: Three interacting learning processes
p601sec Beta and gamma oscialltions in hippocampus and cortical areas V1, V2, and V4
p602sec When hippocampal feedback fails: Grid cell spatial periodicity and firing rate collapse
p609sec Continuous attractor with top-down uniform positive hippocampal-to-entorhinal feedback
image p189fig05.04 The hippocampus is one of several brain regions that are important in learning and remembering about objects and events that we experience throughout life. The book will describe several hippocampal processes that contribute to this achievement in different ways.
|| hypothalmic nuclei, amygdala, hippocampus, cingulate gyrus, corpus callosum, thalamus
image p228fig05.37 A macrocircuit of the neurotrophic Spectrally Timed ART, or nSTART, model. I developed nSTART with my PhD student Daniel Franklin. It proposes how adaptively timed learning in the hippocampus, bolstered by Brain Derived Neurotrophic Factor, or BDNF, helps to ensure normal memory consolidation.
|| habituative gates, CS, US, Thalamus (sensory cortex, category learning, conditioned reinforcer learning, adaptively time learning and BDNF), Amygdala (incentive motivation learning), Hippocampus (BDNF), Prefrontal Cortex (attention), Pontine nuclei, Cerebellum (adaptively timed motor learning)
image p233fig05.42 Mismatch-induced beta oscillations have been reported in at least three parts of the brain: V1, V4, and hippocampus. Althpough there may be other reasons for beta oscillations in the brain, those that are caused by a mismatch should be studied in concert with the gamma oscillations that occur during a good enough match. See tyhe text for details.
|| Is there evidence for the [gamma, beta] prediction? Yes, in at least three parts of the brain, (Buffalo EA, Fries P, Ladman R, Buschman TJ, Desimone R 2011, PNAS 108, 11262-11267) Does this difference in average oscillation frequencies in the superficial and deep layers reflect layer 4 reset? Superficial recording γ (gamma), Deep recording β (beta) (Berke etal 2008, hippocampus; Buschman and Miller 2009, FEF)
image p520fig14.02 Macrocircuit of the main brain regions, and connections between them, that are modelled in the unified predictive Adaptive Resonance Theory (pART) of cognitive-emotional and working memory dynamics. Abbreviations in red denote brain regions used in cognitive-emotional dynamics. Those in green denote brain regions used in working memory dynamics. Black abbreviations denote brain regions that carry out visual perception, learning and recognition of visual object categories, and motion perception, spatial representation and target tracking. Arrow denote non-excitatory synapses. Hemidiscs denote adpative excitatory synapses. Many adaptive synapses are bidirectional, thereby supporting synchronous resonant dynamics among multiple cortical regions. The output signals from the basal ganglia that regulate reinforcement learning and gating of multiple cortical areas are not shown. Also not shown are output signals from cortical areas to motor responses. V1: striate, or primary, visual cortex; V2 and V4: areas of prestriate visual cortex; MT: Middle Temporal cortex; MST: Medial Superior Temporal area; ITp: posterior InferoTemporal cortex; ITa: anterior InferoTemporal cortex; PPC: Posterior parietal Cortex; LIP: Lateral InterParietal area; VPA: Ventral PreArculate gyrus; FEF: Frontal Eye Fields; PHC: ParaHippocampal Cortex; DLPFC: DorsoLateral Hippocampal Cortex; HIPPO: hippocampus; LH: Lateral Hypothalamus; BG: Basal Ganglia; AMYG: AMYGdala; OFC: OrbitoFrontal Cortex; PRC: PeriRhinal Cortex; VPS: Ventral bank of the Principal Sulcus; VLPFC: VentroLateral PreFrontal Cortex. See the text for further details.
||
image p532fig14.08 Macrocircuit of the ARTSCENE Search neural model for learning to search for desired objects by using the sequences of already experienced objects and their locations to predict what and where the desired object is. V1 = First visual area or primary visual cortex; V2 = Second visual area; V4 = Fourth visual area; PPC = Posterior Parietal Cortex; ITp = posterior InferoTemporal cortex; ITa = anterior InferoTemporal cortex; MTL = Medial Temporal Lobe; PHC = ParaHippoCampal cortex; PRC = PeriRhinal Cortex; PFC = PreFrontal Cortex; DLPFC = DorsoLateral PreFrontal Cortex; VPFC = Ventral PFC; SC = Superior Colliculus.
||
image p541fig15.02 The neurotrophic Spectrally Timed Adaptive Resonance Theory, or nSTART, model of (Franklin, Grossberg 2017) includes hippocampus to enable adaptively timed learning that can bridge a trace conditioning gap, or other temporal gap between CS and US.
|| Hippocampus can sustain a Cognitive-Emotional resonance: that can support "the feeling of what happens" and knowing what event caused that feeling. [CS, US] -> Sensory Cortex (SC) <- motivational attention <-> category learning -> Prefrontal Cortex (PFC). SC conditioned reinforcement learning-> Amygdala (cannot bridge the temporal gap) incentive motivational learning-> PFC. SC adaptively timer learning and BDNF-> Hippocampus (can bridge the temporal gap) BDNF-> PFC. PFC adaptively timed motor learning-> cerebellum.
image p543fig15.06 The circuit between dentate granule cells and CA1 hippocampal pyramid cells seems to compute spectrally timed responses. See the text for details.
|| Hippocampal interpretation. 1. Dentate granule cells (Berger, Berry, Thompson 1986): "increasing firing...in the CS period...the latency...was constant". 2. Pyramidal cells: "Temporal model" Dentate granule cells-> CA3 pyramids. 3. Convergence (Squire etal 1989): 1e6 granule cells, 1.6e5 CA3 pyramids. 80-to-1 (ri).
image p549fig15.19 How the adaptively timed hippocampal spectrum T inhibits (red arrow) the orienting system A as motivated attention in orbitofrontal cortex Si(2) peaks at the ISI.
|| Conditioning, Attention, and Timing circuit. Hippocampus spectrum-> Amgdala orienting system-> neocortex motivational attention. Adaptive timing inhibits orienting system and maintains adaptively timed Motivated Attention on the CS.
image p557fig15.26 Brain regions and processes that contribute to autistic behavioral symptoms when they become imbalanced in prescribed ways.
|| Basal Gamglia prolonged gate opening <-> { Amygdala emotionally depressed-> [hippocampus- hyperspecific learning; Cerebellum- adaptive timing fails; hypofrontal blocking fails, no Theory of Mind]-> Neocortex; Neocortex- rewards not received-> Amygdala}.
image p573fig16.01 The experimental chamber (A) and neurophysiological recordings from a rat hippocampus (B) that led to the discovery of place cells. See the text for details.
||
image p575fig16.03 As a rat navigates in its experimental chamber (black curves), neurophysiological recordings disclose the firing patterns (in red) of (a) a hippocampal place cell and (b) an entrorhinal grid cell.
||
image p578fig16.04 Cross-sections of the hippocampal regions and the inputs to them. See the text for details.
|| EC-> CA1-> CA3-> DG. Layers [V/V1, II, II].
image p583fig16.10 The GRIDSmap model is embedded into a more complete representation of the processing stages from receipt of angular head velocity and linear velocity signals to this learning of place cells.
|| GRIDSmap. Pre-wired 2D stripe cells, learns 2D grid cells. vestibular cells [angular head velocity-> head direction cells, linear velocity]-> stripe cells- small scale 1D periodic spatial code (ECIII)-> SOM grid cells entorhinal cortex- small scale 2D periodic spatial scale-> SOM place cells hippocampal cortex- large scale 2D spatial code (dentate/CA3). Unified hierarchy of SOMs.
image p600fig16.36 The entorhinal-hipppocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories. See the text for details.
|| Entorhinal-hippocampal interactions as an ART system. Hippocampal place cells as spatial categories. Angular head velocity-> head direction cells-> stripe cells- small scale 1D periodic code (ECIII) SOM-> grid cells- small scale 2D periodic code (ECII) SOM-> place cells- larger scale spatial map (DG/CA3)-> place cells (CA1)-> conjunctive-coding cells (EC V/VI)-> top-down feedback back to stripe cells- small scale 1D periodic code (ECIII). stripe cells- small scale 1D periodic code (ECIII)-> place cells (CA1).
image p602fig16.37 Data showing the effect of hippocampal inactivation by muscimol on grid cell firing before, during, and six hours after the muscimol, reading from left to right.
|| Hippocampal inactivation disrupts grid cells (Bonnevie etal 2013). muscimole inactivation. spikes on trajectory: [before, after min [6-20, 20-40, 40-60, 6h]]. rate map (Hz) [18.6, 11.4, 9.5, 6.7, 10.8]. spatial autocorrelogram g=[1.12, 0.05, -0.34, 0.09, 1.27].
image p603fig16.38 Role of hippocampal feedback in maintaining grid fields. (a) Data showing the effect of hippocampal inactivation before and during muscimol inhibition of hippocampal cells, as in Figure 16.37. (b) Model simulation with normal grid fields. (c) Model simulation that emulates the effect of hippocampal inhibition on grid fields.
|| (a) Data: hippocampal inactivation [before, after] cart [spikes on trajectory (p: [18.6, 6.7] Hz), spatial autocorrelogram (g= [1.12, 0.09])]. (b) Model: noise-free path integration, [spikes on trajectory (p: 14.56 Hz), rate map, spatial autocorrelogram (g= 1.41), dynamic autocorrelogram (g=0.6)]. (c) Model: noisy path integration + non-specific tonic inhibition, [spikes on trajectory (p: 11.33 Hz), rate map, spatial autocorrelogram (g= 0.05), dynamic autocorrelogram (g=0.047)].
image p617fig16.50 The perirhinal and parahippocampal cortices enable adaptively timed reinforcement learning and spatial navigational processes that are modeled by Spectral Spacing models in the What and Where cortical streams, respectively, to be fused in the hippocampus.
|| What and Where inputs to the hippocampus (Diana, Yonelinas, Ranganath 2007). Adaptively timed conditioning and spatial naviga039tbl01.03 tion. Hippocampus <-> Entorhinal Cortex <-> [Perirhinal Cortex <-> what, Parahippocampal Cortex <-> where].
Grossberg 2021 p229c1h0.50 (Scott Brincat, Earl Miller 2015) reported neurophysiological data that support the ART distinction between category learning within the attentional system, that includes prefrontal cortex (PFC), and the orienting system, that includes the hippocampus (HPC). ... These results contribute to a long history of neurobiological experiments that have implicated the hippocampus in mismatch processing, notably the processing of novel events, including experiments and theoretical concepts about habituation of novelty in the hippocampus as learning proceeds. ... The ART hypothesis testing and category learning cycle hereby clarifies and unifies an important series of psychophysiological experiments that go back at least 50 years.
Grossberg 2021 p047c2h0.20 the neural global workspace model that was published in 2014 by Stanislas Dehaene (Dehaene 2014) builds upon the global workspace of Bernard Baars (Baars 2005) and claims that "consciousness is global information broadcasting within the cortex [to achieve] massive sharing of pertinent information throughout the brain" (p13). Dehaene also makes a number of other useful observations, including that "the time that our conscious vision spends entertaining an interpretation is directly related to its likelihood, given the sensory information received" (p97) and that "the conditioning paradigm suggests that consciousness has a specific evolutionary role: learning over time, rather than simply living in the instant. The system formed by the prefrontal cortex and its interconnected areas, including the hippocampus, may serve the essential role of bridging temporal gaps" (p103). Such claims are consistent with the analysis in this book, but they do not describe the underlying organizational principles, neural mechanisms, or brain representations that embody subjective conscious aspects of experience.

p190 Howell: [neural microcircuits, modal architectures] used in ART -

bottom-up filters	top-down expectations	purpose
instar learning	outstar learning	p200fig05.13 Expectations focus attention: [in, out]star often used to learn the adaptive weights. top-down expectations to select, amplify, and synchronize expected patterns of critical features, while suppressing unexpected features
LGN Lateral Geniculate Nucleus	V1 cortical area	p192fig05.06 focus attention on expected critical features
EC-III entorhinal stripe cells	CA1 hippocampal place cells	p600fig16.36 entorhinal-hippocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories
auditory orienting arousal	auditory category	p215fig05.28 How a mismatch between bottom-up and top-down patterns can trigger activation of the orienting system A and, with it, a burst of nonspecific arousal to the category level. ART Matching Rule: TD mismatch can suppress a part of F1 STM pattern, F2 is reset if degree of match < vigilance
auditory stream with/without [silence, noise] gaps	perceived sound continues?	p419fig12.17 The auditory continuity illusion: Backwards in time - How does a future sound let past sound continue through noise? Resonance!
visual perception, learning and recognition of visual object categories	motion perception, spatial representation and target tracking	p520fig14.02 pART predictive Adaptive Resonance Theory. Many adaptive synapses are bidirectional, thereby supporting synchronous resonant dynamics among multiple cortical regions. The output signals from the basal ganglia that regulate reinforcement learning and gating of multiple cortical areas are not shown. red - cognitive-emotional dynamics green - working memory dynamics black - see [bottom-up, top-down] lists
EEG with mismatch, arousal, and STM reset events	expected [P120, N200, P300] event-related potentials (ERPs)	p211fig05.21 Sequences of P120, N200, and P300 event-related potentials (ERPs) occur during oddball learning EEG experiments under conditions that ART predicted should occur during sequences of mismatch, arousal, and STM reset events, respectively.
Cognitive	Emotional	p541fig15.02 nSTART neurotrophic Spectrally Timed Adaptive Resonance Theory: Hippocampus can sustain a Cognitive-Emotional resonance: that can support "the feeling of what happens" and knowing what event caused that feeling. Hippocampus enables adaptively timed learning that can bridge a trace conditioning gap, or other temporal gap between Conditioned Stimuluus (CS) and Unconditioned Stimulus (US).

backgound colours in the table signify :

white	general microcircuit : a possible component of ART architecture
lime green	sensory perception [attention, expectation, learn]. Table includes [see, hear, !!must add touch example!!], no Grossberg [smell, taste] yet?
light blue	post-perceptual cognition?
pink	"the feeling of what happens" and knowing what event caused that feeling

p578fig16.04 Howell: reminds me of work by Ted Berger etal on hippocampal prosthesis regarding conversion of short-term to long-term memory, "telepathic rats" (fun even if crazy).
Grossberg: Cross-sections of the hippocampal regions and the inputs to them. See the text for details.

auditory continuity illusion

grepStr = auditory continuity

p419sec Auditory continuity illision and spectral-pitch-and-timbre resonances
image p419fig12.17 The auditory continuity illusion illustrates the ART Matching Rule at the level of auditory streaming. Its "backwards in time" effect of future context on past conscious perception is a signature of resonance.
|| Auditory continuity illusion. input, percept. Backwards in time - How does a future sound let past sound continue through noise? Resonance! - It takes a while to kick in. After it starts, a future tone can maintain it much more quickly. Why does this not happen if there is no noise? - ART Matching Rule! TD harmonic filter is maodulatory without BU input. It cannot create something out of nothing.
image p420fig12.18 The ARTSTREAM model explains and simulates the auditory continuity illusion as an example of a spectral-pitch resonance. Interactions of ART Matching Rule and asymmetric competition mechanisms in cortical strip maps explain how the tone selects the consistent frequency from the noise in its own stream while separating the rest of the noise into another stream.
|| ARTSTREAM model (Grossberg 1999; Grossberg, Govindarajan, Wyse, Cohen 2004). SPINET. Frequency and pitch strips. Bottom Up (BU) harmonic sieve. Top Down (TD) harmonic ART matching. Exclusive allocation. Learn pitch categories based on early harmonic processing. A stream is a Spectral-Pitch Resonance!
image p425fig12.23 ARTSTREAM simulations of the auditory continuity illusion and other streaming properties (left column, top row). When two tones are separated by silence (Input), a percept of silence also separates them in a spectral-pitch resonance. (left column, bottom row). When two tones are separated by broadband noise, the percept of tone continues through the noise in one stream (stream 1) while the remainder of the noise occurs in a different stream (stream 2). (right column) Some of the other streaming properties that have been simulated by the ARTSTREAM model.
|| Auditory continuity does not occur without noise. Auditory continuity in noise. Other simulated streaming data.

p190 Howell: [neural microcircuits, modal architectures] used in ART -

bottom-up filters	top-down expectations	purpose
instar learning	outstar learning	p200fig05.13 Expectations focus attention: [in, out]star often used to learn the adaptive weights. top-down expectations to select, amplify, and synchronize expected patterns of critical features, while suppressing unexpected features
LGN Lateral Geniculate Nucleus	V1 cortical area	p192fig05.06 focus attention on expected critical features
EC-III entorhinal stripe cells	CA1 hippocampal place cells	p600fig16.36 entorhinal-hippocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories
auditory orienting arousal	auditory category	p215fig05.28 How a mismatch between bottom-up and top-down patterns can trigger activation of the orienting system A and, with it, a burst of nonspecific arousal to the category level. ART Matching Rule: TD mismatch can suppress a part of F1 STM pattern, F2 is reset if degree of match < vigilance
auditory stream with/without [silence, noise] gaps	perceived sound continues?	p419fig12.17 The auditory continuity illusion: Backwards in time - How does a future sound let past sound continue through noise? Resonance!
visual perception, learning and recognition of visual object categories	motion perception, spatial representation and target tracking	p520fig14.02 pART predictive Adaptive Resonance Theory. Many adaptive synapses are bidirectional, thereby supporting synchronous resonant dynamics among multiple cortical regions. The output signals from the basal ganglia that regulate reinforcement learning and gating of multiple cortical areas are not shown. red - cognitive-emotional dynamics green - working memory dynamics black - see [bottom-up, top-down] lists
EEG with mismatch, arousal, and STM reset events	expected [P120, N200, P300] event-related potentials (ERPs)	p211fig05.21 Sequences of P120, N200, and P300 event-related potentials (ERPs) occur during oddball learning EEG experiments under conditions that ART predicted should occur during sequences of mismatch, arousal, and STM reset events, respectively.
Cognitive	Emotional	p541fig15.02 nSTART neurotrophic Spectrally Timed Adaptive Resonance Theory: Hippocampus can sustain a Cognitive-Emotional resonance: that can support "the feeling of what happens" and knowing what event caused that feeling. Hippocampus enables adaptively timed learning that can bridge a trace conditioning gap, or other temporal gap between Conditioned Stimuluus (CS) and Unconditioned Stimulus (US).

backgound colours in the table signify :

white	general microcircuit : a possible component of ART architecture
lime green	sensory perception [attention, expectation, learn]. Table includes [see, hear, !!must add touch example!!], no Grossberg [smell, taste] yet?
light blue	post-perceptual cognition?
pink	"the feeling of what happens" and knowing what event caused that feeling

p419fig12.17 Howell: important indicator of resonance, which is a requirement for consciousness (but not all resonance is conscious)
Howell: grepStr 'auditory continuity' - DeLiang Wang "Auditory scene analysis"
Grossberg: The auditory continuity illusion illustrates the ART Matching Rule at the level of auditory streaming. Its "backwards in time" effect of future context on past conscious perception is a signature of resonance. ||

see-reach to hear-speak

grepStr = reach|speak|see-reach to hear-speak

p404 Chapter 12From seeing and reaching to hearing and speaking - Circular reaction, streaming, working memory, chunking, and number
p013sec speaker-normalized speech recognition
p179sec An evolutionary design tension between requirements of recognition and reaching
p259sec A link between visual neglect and motor planning deficits: Seeing to reach
p278sec Agnosia: Parietal attention and intention control seeing and reaching without knowing
Chapter 12 - From seeing and reaching to hearing and speaking
p405sec Circular reactions in vision and audition: Babbling to reach and to speak
p414sec Motor-equivalent reaching and tool use
p417sec From motor-equivalent reaching to motor-quivalent speech production and coarticulation
p417sec From eating to speaking
p427sec From streaming to speech: Learning to imitate speech requires speaker normalization
p427sec From circular reactions to language imitation: Speaker normalization and imitative maps
p429sec NormNet: A shared design for auditory streaming and speaker normalization
p430sec Strip maps and assymetric competition for auditory streaming and speaker normalization
p466sec Learning and storage of speaker-invariant and rate-invariant working memories
p466sec From speaker normalization to rate normalization: Transients and two kinds of gain control
p479sec From speaking to singing
p577sec Homologous circuits for looking/reaching and spatial navigation
p639sec Web-based curricula to reach millions of students and adults around the world
Chapter 12 - From seeing and reaching to hearing and speaking

WHAT	WHERE
spatially-invariant object learning and recognition	spatially-variant reaching and movement
fast learning without catastrophic forgetting	continually update sensory-motor maps and gains
IT InterferoTemporal Cortex	PPC Posterior Parietal Cortex

	What	Where
matching	excitatory	inhibitory
learning	match	mismatch

image p032fig01.21 At least three parallel visual cortical streams respond to visual inputs that reach the retina. Two parvocellular streams process visual surfaces (blob stream) and visual boundaries (interblob stream). The magnocellular stream processes visual motion.
|| [Retina, LGNs, V[1,2,3,4], MT] to [What- inferotemporal areas, Where- parietal areas]: visual parallel streams [2x blob, 1x bound]
image p039tbl01.03 The link between consciousness and movement
||
VISUAL seeing, knowing, and reaching
AUDITORY hearing, knowing, and speaking
EMOTIONAL feeling, knowing, and acting

image p092fig03.05 A cross-section of the retinal layer. Note that light stimuli need to go through all retinal layers before they reach the photoreceptor layer at which the light signals are registered.
|| light stimuli ->

retinal layers	cellular composition
inner limiting membrane
retinal nerve fibre	ganglion nerve fibres
ganglion cell	ganglion
inner plexiform	amacrine
inner nuclear	horizontal
outer plexiform
outer limiting membrane
photoreceptor	rod
photoreceptor	cone
retinal pigment epithelium

<- signal transduction. http://brain.oxfordjournals.org/content/early/2011/01/20/brain.awq346

image p232fig05.41 (a)-(c). The sequence of interlaminar events that SMART predicts during a mismatch reset. (d) Some of the compatible neurophysiological data.
|| Mismatch causes layer 5 dendritic spike that trigger reset. (a) Arousal causes increase in nonspecific thalamic nuclei firing rate and layer 5 dendritic and later somatic spikes (Karkum and Zhu 2002, Williams and Stuart 1999) (b) Layer 5 spikes reach layer 4 via layer 6i and inhibitory neurons (Lund and Boothe 1975, Gilbert and Wiesel 1979) (c) habituative neurotransmitters in layer 6i shift the balance of active cells in layer 4 (Grossberg 1972, 1976) (d) Dendritic stimulation fires layer 5 (Larkum and Zhu 2002) stimulation apical dendrites of nonspecific thalamus
image p278fig06.28 If the feature-category resonances cannot form, say due to a lesion in IT, then a surface-shroud resonance can still support conscious seeing of an attended object, and looking at or reaching for it, even if the individual doing so knows nothing about the object, as occurs during visual agnosia. The surface-shroud resonance supports both spatial attention and releases commands that embody the intention to move towards the attended object.
|| What kinds of resonances support knowing vs seeing? visual agnosia: reaching without knowing Patient DF (Goodale etal 1991). Attention and intention both parietal cortical functions (Anderson, Essick, Siegel 1985; Gnadt, Andersen 1988; Synder, Batista, Andersen 1997, 1998)
image p303fig08.20 The G-wave speeds up with the distance between flashes at a fixed delay, and has a consitent motion across multiple spatial scales.
|| G-wave properties (Grossberg 1977). Theorem 2 (Equal half-time property) The time at which the motion signal reaches position w=L/2. Apparent motion speed-up with distance: this half-time is independent of the distance L between the two flashes. Consistent motion across scales: half-time is independent of the scale size K. Method of proof: elementary algebra and calculus (Grossberg, Rudd 1989 appendix)
image p304fig08.21 A computer simulation of the equal half-time property whereby the apparent motions within different scales that respond to the same flashes all reach the half-way point in the motion trajectory at the same time.
|| Equal half-time property: how multiple scales cooperate to generate motion percept. Travelling waves from Gaussian filters of different sizes bridge the same distance in comparable time. The time needed to bridge half the distance between flashes is the same.
image p335fig08.61 Behavioral data (left image) and simulation (right image) about speed in correct and error trials of the RT task. See text for details.
|| Behavioral data: speed, correct and error trials (RT task) (Roltman, Shadien 2002). More coherence in the motion causes faster reaction time.
image p350fig09.22 How the negative Gaussian of an obstacle causes a peak shift to avoid the obstacle without losing sight of how to reach the goal.
|| Steering dynamics: obstacle avoidance. body-centered coordinates [obstacle, goal, heading] -> steering
image p351fig09.25 By the time MT+ is reached, directional transient cells and directional filters have begun to extract more global directional information from the image.
|| M+ computes global motion estimate. Estimate global motion from noisy local motion estimates.
Chapter 12 - From seeing and reaching to hearing and speaking
image p414fig12.11 Neurophysiological data from cortical areas 4 and 5 (every other column) and simulations thereof (other columns) during a reach.
|| activation vs time. (a) area 4 phasic RT (IFV) (b) area 4 tonic (OPV) (c) area 4 phasic-tonic (OFPV) (d) area 4 phasic MT (DVV) (e) area 5 phasic (DV) (f) area 5 tonic (PPV)
image p416fig12.13 The DIRECT model learns, using a circular reaction that is energized by an Endogenous Random Generator, or ERG, to make motor-equivalent volitionally-activated reaches. This circular reaction learns a spatial representation of a target in space. It can hereby make accurate reaches with clamped joints and on its first try using a tool under visual guidance; see Figure 12.16.
|| DIRECT model (Bulloch, Grossberg, Guenther 1993). learns by circular reaction. learns spatial reresentation to me4diate between vision and action. motor-equivalent reaching. can reach target with clamped joints. can reach target with a TOOL on the first try under visual guidance. How did tool use arise?!
image p416fig12.14 Computer simulations of DIRECT reaches with (b) a tool, (c) a clamped elbow, and (d) with a blindfold, among other constraints.
|| Computer simulationsd of direct reaches [unconstrained, with TOOL, elbow clamped at 140°, blindfolded]
image p417fig12.15 The DIRECT and DIVA models have homologous circuits to learn and control motor-equivalent reaching and speaking, with tool use and coarticulation resulting properties. See the text for why.
|| From Seeing and Reaching to Hearing and Speaking, Circular reactions (Piaget 1945, 1951, 1952). Homologous circuits for development and learning of motor-equivalent REACHING and SPEAKING. DIRECT TOOL use (Bullock, Grossberg, Guenther 1993), DIVA Coarticulation (Guenther 1995)
image p428fig12.25 (left architecture) Auditory-articulatory feedback loop whereby babbled sounds active learning in an imitative map that is later used to learn to reproduce the sounds of other speakers. An articulatory-to-auditory expectation renders learning possible by making the auditory and motor data dimensionally consistent, as in the motor theory of speech. (right architecture) Parallel streams in the ARTSPEECH model for learning speaker-independent speech and language meaning, including a mechanism for speaker normalization (right cortical stream) and for learning speaker-dependent vocalic qualities (left cortical stream).
|| left: Speaker-dependent vocalic qualities; right: Speaker-independent speech and language meaning
image p430fig12.26 The NormNet model shows how speaker normalization can be achieved using specializations of the same mechanisms that create auditory streams. See the text for how.
|| [Anchor vs Stream] log frequency map. -> diagonals-> Speaker-independent acoustic item information-> [BU adaptive filter, TD learned expectation]-> leaned item recognition categories
image p446fig12.44 (left column, top row) LIST PARSE can model linguistic data from human subjects. In this figure, model parameters are fixed to enable a close fit to data about error-type distributions in immediate free recall experiments, notably transposition errors. (right column, top row) Simulation and data showing bowing of the serial position curve, including an extended primacy gradient. (left column, bottom row) The simulation curve overlays data about list length effects, notably the increasing recall difficulty of longer lists during immediate serial recall (ISR). (right column, bottom row) Simulation (bottom image) and data (top image) of the limited temporal extent for recall.
|| (1. TL) Error-type distributions in immediate serial recall (Hanson etal 1996). % occurence vs serial position. Graph convention: Data- dashed lines; Simulations- solid lines. Six letter visual ISR. Order errors- transpositions of neighboring items are the most common. Model explanation: Noisy activation levels change relative oorder in primacy gradient. Similar activation of neighboring items most susceptible to noise. Model paramenters fitted on these data. (2. TR) Bowing of serial position curve (Cowan etal 1999). % correct vs serial position. Auditory ISR with various list lengths (graphs shifted rightward): For [, sub-]span lists- extended primacy, with one (or two) item recency; Auditory presentation- enhanced performance for last items. LIST PARSE: End effects- first and last items half as many members; Echoic memory- last presented item retained in separate store. (3. BL) List length effects, circles (Crannell, Parrish 1968), squares (Baddeley, Hitch 1975), solid line- simulation. % list correct vs list length. Variable list length ISR: longer lists are more difficult to recall. LIST PARSE: More items- closer activation levels and lower absolute activity level with enough inputs; Noise is more likely to produce order errors, Activity levels more likely to drop below threshold;. (4. BR) Limited temporal extent for recall (Murdoch 1961). % recalled vs retention interval (s). ISR task with distractor-filled retention intervals (to prevent rehersal): Increasing retention interval - decreases probabilty of recalling list correctly; Load dependence- longer list more affected by delays; Performance plateau- subjects reach apparent asymptote. LIST PARSE: Increase convergence of activities with time; loss of order information;.
p416fig12.13 Howell: DIRECT model reaching: reminds me of awesome work by Jan Peters & colleagues (Technische Universitaet Darmstadt, Germany) "Robot Learning" (he zoomed 21Jun2022)
Howell: DIRECT model reaching: reminds me of Yoonsuck Choe etal work on Evolutionary Computation: autonomous emergence of tool use.
Grossberg: The DIRECT model learns, using a circular reaction that is energized by an Endogenous Random Generator, or ERG, to make motor-equivalent volitionally-activated reaches. This circular reaction learns a spatial representation of a target in space. It can hereby make accurate reaches with clamped joints and on its first try using a tool under visual guidance; see Figure 12.16. || DIRECT model (Bulloch, Grossberg, Guenther 1993). learns by circular reaction. learns spatial reresentation to me4diate between vision and action. motor-equivalent reaching. can reach target with clamped joints. can reach target with a TOOL on the first try under visual guidance. How did tool use arise?!
p079 Howell: grepStr 'noise suppression|rate spectra' - Gerald Pollack: likes attract likes, speaking of ions (effect of inter-layer)

neurotransmitter

p604sec Acetocholine modulates vigilance control of cognitive, motor, and spatial category learning
image p231fig05.39 The SMART hypothesis testing and learning cycle predicts that vigilance increases when a mismatch in subcortical regions like the nonspecific thanlamus activates the nucleus basalis of Meynert which, in turn, broadcasts a burst of the neurotransmitter acetylcholine, or ACh, to deeper cortical layers. Due to the way in which LAMINART proposes that cortical matching and mismatching occurs, this ACh burst can increase vigilance and thereby trigger a memory search. See the text for details.
|| [BU input, [, non]specific thalamic nucleus, thalamic reticulate nucleus, neocortical laminar circuit] cart [Arousal, Reset, Search, Vigilance]
image p232fig05.41 (a)-(c). The sequence of interlaminar events that SMART predicts during a mismatch reset. (d) Some of the compatible neurophysiological data.
|| Mismatch causes layer 5 dendritic spike that trigger reset. (a) Arousal causes increase in nonspecific thalamic nuclei firing rate and layer 5 dendritic and later somatic spikes (Karkum and Zhu 2002, Williams and Stuart 1999) (b) Layer 5 spikes reach layer 4 via layer 6i and inhibitory neurons (Lund and Boothe 1975, Gilbert and Wiesel 1979) (c) habituative neurotransmitters in layer 6i shift the balance of active cells in layer 4 (Grossberg 1972, 1976) (d) Dendritic stimulation fires layer 5 (Larkum and Zhu 2002) stimulation apical dendrites of nonspecific thalamus
image p461fig12.58 The lisTELOS model built upon key processes that were earlier modeled by the TELOS model. See the text for details.
|| TELOS model (Brown, Bulloch, Grossberg 1999, 2004). shows [BG nigro-[thalamic, collicular], FEF, ITa, PFC, PNR-THAL, PPC, SEF, SC, V1, V4/ITp, Visual Cortex input] and [GABA].
image p523fig14.03 (a) The MOTIVATOR neural model generalizes CogEM by also including the basal ganglia. It can hereby explain and simulate complementary functions of the amygdala and basal ganglia (SNc) during conditioning and learned performance. The basal ganglia generate Now Print signals in response to unexpected rewards. These signals modulate learning of new associations in many brain regions. The amygdala supports motivated attention to trigger actions that are expected to occur in response to conditioned or unconditioned stimuli. Object Categories represent visual or gustatory inputs in anterior inferotemporal (ITA) and rhinal (RHIN) cortices, respectively. Value Categories represent the value of anticipated outcomes on the basis of hunger and satiety inputs, in amygdala (AMYG) and lateral hypothalamus (LH). Object-Value Categories resolve the value of competing perceptual stimuli in medial (MORB) and lateral (ORB) orbitofrontal cortex. The Reward Expectation Filter detects the omission or delivery of rewards using a circuit that spans ventral striatum (VS), ventral pallidum (VP), striosomal delay (SD) cells in the ventral striatum, the pedunculopontine nucleus (PPTN) and midbrain dopaminergic neurons of the substantia nigra pars compacta/ventral tegmental area (SNc/VTA). The circuit that processes CS-related visual information (ITA, AMTG, ORB) operates in parallel with a circuit that processes US-related visual and gustatory information (RHIN, AMYG, MORB). (b) Reciprocal adaptive connections between hypothalamus and amygdala enable amygdala cells to become learned value categories. The bottom region represents hypothalmic cells, which receive converging taste and metabolite inputs whereby they become taste-drive cells. Bottom-up signals from activity patterns across these cells activate competing value category, or US Value Representations, in the amygdala. A winning value category learns to respond selectively to specific combinations of taste-drive activity patterns and sends adaptive top-down priming signals back to the taste-drive cells that activated it. CS-activated conditioned reinforcer signals are also associatively linked to value categories. Adpative connections end in (approximately) hemidiscs. See the text for details.
||
image p524fig14.04 (a) Model basal ganglia circuit for the control of dopaminergic Now Print signals from the substantia nigra pars compacta, or SNc, in response to unexpected rewards. Cortical inputs (Ii), activated by conditioned stimuli, learn to excite the SNc via a multi-stage pathway from the vantral striatum (S) to the ventral pallidum and then on to the PPTN (P) and the SNc (D). The inputs Ii excite the ventral striatum via adaptive weights WIS, and the ventral striatum excites the SNc with strength W_PD. The striosomes, which contain an adaptive spectral timing mechanism [xij, Gij, Yij, Zij], learn to generate adaptively timed signals that inhibit reward-related activation of the SNc. Primary reward signals (I_R) from the lateral hypothalamus both excite the PPTN directly (with strength W_RP) and act as training signals to the ventral striatum S (with strength W_RS) that trains the weights W_IS. Arrowheads denote excitatory pathways, circles denote inhibitory pathways, and hemidiscs denote synapses at which learning occurs. Thick pathways denote dopaminergic signals.
||
image p559fig15.27 Brain regions and processes that contribute to the release of dopaminergic Now Print signals by the substantia nigra pars compacta, or SNc, in response to unexpected reinforcing events. See the text for details.
|| Model of spectrally timed SNc learning (Brown, Bulloch, Grossberg 1999). Delayed inhibitory expectations of reward. Dopamine cells signal an error in reqard prediction timing or magnitude. Immediate excitatory predictions of reward. Lateral hypothalamus (Primary Reward Input)-> [(+)ventral striatum <-> ventral pallidium (+)-> PPTN(+)-> SNc]. SNc-> [dopamine signal -> ventral striatum, Striosomal cells]. Conditioned Stimuli (CS)(+)-> [ventral striatum, striosomal cells]. Striosomal cells(-)-> SNc.
image p560fig15.29 Excitatory pathways that support activation of the SNc by a US and the conditioning of a CS to the US.
|| Excitatory pathway. Primary reward (apple juice) briefly excites lateral hypothalamus. Hypothalamic-PPTN excitation causes SNc dopamine burst. Hypothalamic activity excites ventral striatum for training. Active CS working memory signals learn to excite ventral striatum. Lateral hypothalamus (Primary Reward Input)-> [(+)ventral striatum <-> ventral pallidium(+)-> PPTN(+)-> SNc]. SNc-> [dopamine signal -> ventral striatum. Conditioned Stimuli working memory trace (CS)(+)-> ventral striatum.
image p560fig15.30 The inhibitory pathway from striosomal cells to the SNc is able to inhibit the SNc when a reward occurs with expected timing and magnitude.
|| Inhibitory pathway. Learning: CS-striosomal LTP occurs due to a three-way coincidence [An active CS working memory input, a Ca2+ spike, a dopamine burst]; Signaling: The delayed Ca2+ spike facilitates striosomal-SNc inhibition;. Striosomal cells learn to predict both timing and magnitude of reward signal to cancel it: reward expectation;. Conditioned stimuli (CS) LTP-> Striosomal cells <- dopamine | (-)-> SNc->.
image p561fig15.32 The SNc can generate both dopamine bursts and dips in response to rewards whose amplitude is unexpectedly large or small.
|| Inhibitory pathway: expectation magnitude. 1. If reward is greater than expected, a dopamine burst causes striosomal expectation to increase. 2. If reward is less than expected, a dopamine dip causes striosomal expectation to decrease. 3. This is a negative feedback control system for learning. Conditioned stimuli (CS)-> Striosomal cells <- dopamine | (-)-> SNc->.
image p569fig15.40 The direct and indirect basal ganglia circuits that control GO and STOP movement signals. See the text for details.
|| [Direct path GO(+), Indirect path STOP(+), dopamine from SNc(+-)]-> striatum. GO-> GPi/SNr-> Thalamus (VA/Vlo) <-> frontal cortex. STOP-> GPe <-> STN-> GPi/SNr. NAc-> GPi/SNr.

learning and development

grepStr = infant development|adult learning|cortical development

p357sec Infant development and adult learning use similar laws: A universal development code
p366sec From cortical development in infants to adults grouping and attention
image p375fig11.06 The contrast constraint on binocular fusion is realized by obligate cells in layer 3B of cortical area V1.
|| Model implements contrast constraint on binocular fusion (cf. "obligate" cells Poggio 1991). An ecological constraint on cortical development. [left, right] eye cart V1-[4 monocular simple, 3B binocular simple, complex2/3A] cells. Inhibitory cells (red) ensure that fusion occurs when contrasts in left and right eye are approximately equal.
image p376fig11.09 The disparity filter in V2 helps to solve the correspondence problem by eliminating spurious contrasts using line-of-sight inhibition.
|| Model V2 disparity filter solves the correspondence problem. An ecological constraint on cortical development. [left, right] eye view: False matches (black) suppressed by line-of-sight inhibition (green lines). "Cells that fire together wire together".

Why are there hexagonal grid cell receptive fields?

grepStr = hexagonal

p580sec Why are there hexagonal grid cell receptive fields?
p584sec How many oriented stripe cells are needed to learn hexagonal grids?
image p581fig16.06 The learning of hexagonal grid cell receptive fields as an animal navigates an open field is a natural consequence of simple trigonometric properties of the positions at which the firing of stripe cells that are tuned to different directions will co-occur.
|| The Trigonometry of spatial navigation. Coactivation of stripe cells.
image p583fig16.09 The GRIDSmap model used algorithmically defined stripe cells to process realistic rat trajectories. The stripe cell outputs then formed inputs to the adaptive filter of a self-organizing map which learned hexagonal grid cell receptive fields.
|| GRIDSmap. Self-organizing map receives inputs from stripe cells and learns to respond to most frequent co-activation patterns. Stripe cells combine speed and head direction to create a periodic 1D position code. Virtual rat navigated using live rat trajectories from Moser Lab. Speed and head direction drives stripe cells.
image p584fig16.11 GRIDSmap simulation of the learning of hexagonal grid fields. See the text for details.
|| Simulation results. Multiple phases per scale. response vs lenght scale (0.5m+).
image p585fig16.13 Hexagonal grid cell receptive fields develop if their stripe cell directional preferences are separated by 7, 10, 15, 20, or random numbers degrees. The number and directional selectivities of stripe cells can thus be chosen within broad limits without undermining grid cell development.
||
image p585fig16.14 Superimposing firing of stripe cells whose directional preferences differ by 60 degrees supports learning hexagonal grid cell receptive fields in GRIDSmap.
|| GRIDSmap: from stripe cells to grid cells. Grid-cell Regularity from Integrated Distance through Self-organizing map. Superimposing firing of stripe cells oriented at intervals of 60 degrees. Hexagonal grid!
image p586fig16.15 Superimposing stripe cells oriented by 45 degrees does not lead to learning of rectangular grids in GRIDSmap, but it does in an oscillatory inference model.
|| Why is a hexagonal grid favored? Superimposing firing of stripe cells oriented at intervals of 45 degrees. Rectangular grid. This and many other possibilities do not happen in vivo. They do happen in the oscillatory inference model. How are they prevented in GRIDSmap?
image p587fig16.17 A finer analysis of the 2D trigonometry of spatial navigation showed that both the frequency and amplitude of coactivations by stripe cells determine the learning of hexagonal grid fields.
|| A refined analysis: SOM amplifies most frequent and energetic coactivations (Pilly, Grossberg 2012). [linear track, 2D environment]. (left) Stripe fields separated by 90°. 25 coactivations by 2 inputs. (right) Stripe fields separated by 60°. 23 coactivations by 3 inputs.
image p588fig16.18 Simulations of coordinated learning of grid cell receptive fields (second row) and unimodal place cell receptive fields (third row) by the hierarchy of SOMs in the GridPlaceMap model. Note the exquisite regularity of the hexagonal grid cell firing fields.
|| [stripe, grid, place] cells vs [spikes on trajectory, unsmoothed rate map, smoothed rate map].
image p605fig16.39 Data showing effects of medial septum (MS) inactivation on grid cells and network theta oscillations in medial entorhinal cortex (MEC). (A) Examples of disruption in the spatial expression of the hexagonal grid structure for two grid cells (Brandon etal 2011). (B) Temporal reduction in the power and frequency of network theta oscillations (Koenig etal 2011). (C) Temporary reduction in the gridness score, mean firing rate, and patial stability of grid cells (Koenig etal 2011).
|| Disruptive effects of Medial Septum inactivation in Medial Entorhinal Cortex (Brandon etal 2011; Koenig etal 2011). (A) Rate map [rate map, spatial autocorrelations, trajectory] vs [baseline, sub-sampled, medial septum inactivation, 3-6 hour recovery, 24 hour recovery], [rate map (Hz- m, p), spatial autocorrelations (gridness)][ 1.2, 7.2, 1.1; 0.25, 1.7, 0.6; 0.25, 2.5, -0.53; 0.7, 5.1, 0.55; 1.0, 5.3, 1.3; 2.1, 15, 0.19; 1.7, 12, 0.71; 1.7, 3.2, -0.22; 1.8, 9.1, 0.68; 2.5, 13, 0.46]. (B) [normalized power at 7-9 Hz, frequency (Hz)] vs 5-minute periods. (C) [mean gridness score (+-SEM), mean firing rate (% of baseline), mean correlation coeff (+-SEM)] vs 10-minute periods.

Strange themes

AI, machine intelligence, etc

grepStr = artificial intelligence|autonomous adaptive mobile intelligence|machine intelligence

p353 Chapter 10 Laminar computing by cerebral cortex - Towards a unified theory of biologucal and artificial intelligence Towards a unified theory of biological and artificial intelligence
p369sec Towards a synthesis of biological and artificial intelligence
p616sec From SOVEREIGN to true autonomous adaptive mobile intelligence

Brain is NOT Bayesian?

grepStr = Bayesian

p335sec Are decisions by our brains Bayesian?
p356sec Goes beyond Bayesian models!

image p011fig01.07 The choice of signal function f determines how an initial activity pattern will be transformed and stored in short-term memory (STM). Among [same, slower, faster]-than-linear signal functions, only the last one can suppress noise. It does so as it chooses the population that receives the largest input for storage, while suppressing the activities of all other population, thereby giving rise to a winner-take-all choice.
|| initial pattern (xi(0) vs i):

f	Xi(∞)= xi(∞)/sum[j: xj(∞)]	x(∞)
linear	perfect storage of any pattern	amplifies noise (or no storage)
slower-than-linear	saturates	amplifies noise
faster-than-linear	chooses max [winner-take-all, Bayesian], categorical perception	suppresses noise, [normalizes, quantizes] total activity, finite state machine

image p357fig10.04 Laminar Computing achieves its properties by computing in a new way that sythesizes the best properties of feedforward and feedback interactions, analog and digital computations, and preattentive and attentive learning. The property of analog coherence enables coherent groupings and decisions to form without losing sensitivity to the amount of evidence that supports them.
|| Laminar Computing: a new way to compute. 1. Feedforward and feedback: a) Fast feedforward processing when data are unambiguous (eg Thorpe etal), b) slower feedback chooses among ambiguous alternatives [self-normalizing property, real-time probabiligy theory], c) A self-organizing system that trades certainty against speed: Goes beyond Bayesian models! 2. Analog and Digital: Analog Coherence combines the stability of digital with the sensitivity of analog. 3. Preattentive and Attentive Learning: Reconciles the differences of (eg) Helmholtz and Kanizsa, "A preattentive grouping is its own 'attentional' prime"
p356 Howell: grepStr 'Bayesian' - Robert Hecht-Nielon's "Confabulation Theory": some Bayesian successes are not Bayesian, possibly often when "naive Baye's assumption" is used? (Howell: I forget the details)

brain rythms & Schuman resonances

grepStr = theta|gamma|beta|delta|Schuman

p229sec Gamma and beta oscillations during attentive resonance and mismatch reset
p230sec Beta oscillations in the deeper layers of visual cortex
p232sec Does varying the amount of novelty change the amount of beta?
p233sec Beta oscillations during spatial attention shifts in the frontal eye fields
p233sec Beta oscillations during the learning of hippocampal place fields in novel environments
p234sec Inverted-U in beta power through time during hippocampal place cell learning
p234sec The gamma-beta dichotomy described in terms of encoding and default states
p234sec Gamma and beta oscillations regulate working memory dynamics
p601sec Beta and gamma oscialltions in hippocampus and cortical areas V1, V2, and V4
p604sec Effects on grid cells of inactivating medial septum and the theta rythm
p609sec Intrinsic theta rythm from a dissociation between associative read-out and read-in
p610sec From READ circuits to SOM theta rythm: Dissociating associative read-out from read-in
p610sec SOM theta rythm dissociates read-out from read-in with backpropagating teaching signals
p611sec Why phase precession of the theta rythm?
image p230fig05.38 The Synchronous Matching ART, or SMART, model includes spiking neurons in a laminar cortical hierarchy. I developed SMART with my PhD student Massimiliano Versace. By unlumping LAMINART to include spiking neurons, finer details of neurodynamics, such as the existence of faster gamma oscillations during good enough matches, and slower beta oscillations during bad enough mismatches, could be shown as emergent properties of network interactions.
|| Second order thalamus -> specific thalamic nucleus -> Thalamic reticulate nucleus -> neocortical laminar circuit [6ll, 6l, 5, 2/3, 1] -> Higher order cortex. Similar for First order thalamus -> First order cortex, with interconnection to Second order, nonspecific thalamic nucleus
image p232fig05.40 Computer simulation of how the SMART mode generates (a) gamma oscillations if a good enough match occurs, or (c) beta oscillations if a bad enough match occurs. See the text for details.
|| Brain oscillations during match/mismatch, data, simulation. (a) TD corticothalamic feedback increases synchrony (Sillito etal 1994) (b) Match increases γ oscillations (c) Mismatch increases θ,β oscillations
image p233fig05.42 Mismatch-induced beta oscillations have been reported in at least three parts of the brain: V1, V4, and hippocampus. Althpough there may be other reasons for beta oscillations in the brain, those that are caused by a mismatch should be studied in concert with the gamma oscillations that occur during a good enough match. See tyhe text for details.
|| Is there evidence for the [gamma, beta] prediction? Yes, in at least three parts of the brain, (Buffalo EA, Fries P, Ladman R, Buschman TJ, Desimone R 2011, PNAS 108, 11262-11267) Does this difference in average oscillation frequencies in the superficial and deep layers reflect layer 4 reset? Superficial recording γ (gamma), Deep recording β (beta) (Berke etal 2008, hippocampus; Buschman and Miller 2009, FEF)
image p296fig08.07 When two flashes turn on and off out of phase with the correct range of interstimulus intervals, and not too far from one another, then either beta motion of phi motion are perceived.
|| Beta and Phi motion percepts. Beta motion: percepts of continuous motion of a well-defined object across empty intervening space. Phi motion: sense of "pure" motion without a concurrent percept of moving object. (Exner 1875) http://www.yorku.ca/eye/balls.htm
image p297fig08.08 When a second flash is more intense than the first flash, then apparent motion may occur from the second to the first flash.
|| Delta motion: motions from the second to the first flash. Data: (Kolers 1972; Korte 1915). Simulation: (Grossberg, Rudd 1992). This occurs when the luminance or contrast of the second flash is large compared to that of the first flash. Sustained and transient cells obey shunting dynamics whose averaging rates speed up with output intensity. The first flash to wane is the one that will be the source of the G-wave.
image p340fig09.07 Log polar remapping from the retina to cortical area V1 and beyond converts expansion, translation, and spiral flows on the retina into parallel flows, with different orientations, on the cortical map.
|| Log polar remapping of optic flow. retina -> cortex. Any combination of expansion and circular motion centered on the fovea maps to cortex as a single direction. Retinal Cartesian coordinates (x,y) map to cortical polar coordinates (r,theta). This makes it easy to compute directional receptive fields in the cortex!
image p423fig12.20 The Spatial Pitch Network, or SPINET, model shows how a log polar spatial representation of the sound frequency spectrum can be derived from auditory signals occuring in time. The spatial representation allows the ARTSTREAM model to compute spatially distinct auditory streams.
|| SPINET model (Spatial Pitch Network) (Cohen, Grossberg, Wyse 1995). 1. input sound 2. Gamma-tone filter bank 3. Shaort-term average energy spectrum 4. MAP transfer function 5. On-center off-surround and rectification 6. Harmonic weighting 7. Harmonic summation and competition -> PITCH
image p598fig16.34 The spiking GridPlaceMap model generates theta-modulated place and grid cell firing, unlike the rate-based model.
|| Theta-modulated cells in spiking model. [place, grid] cell vs [membrane potential (mV vs time), frequency vs inter-spike intervals (s), power spectra (normalized power vs frequency (Hz))].
image p605fig16.39 Data showing effects of medial septum (MS) inactivation on grid cells and network theta oscillations in medial entorhinal cortex (MEC). (A) Examples of disruption in the spatial expression of the hexagonal grid structure for two grid cells (Brandon etal 2011). (B) Temporal reduction in the power and frequency of network theta oscillations (Koenig etal 2011). (C) Temporary reduction in the gridness score, mean firing rate, and patial stability of grid cells (Koenig etal 2011).
|| Disruptive effects of Medial Septum inactivation in Medial Entorhinal Cortex (Brandon etal 2011; Koenig etal 2011). (A) Rate map [rate map, spatial autocorrelations, trajectory] vs [baseline, sub-sampled, medial septum inactivation, 3-6 hour recovery, 24 hour recovery], [rate map (Hz- m, p), spatial autocorrelations (gridness)][ 1.2, 7.2, 1.1; 0.25, 1.7, 0.6; 0.25, 2.5, -0.53; 0.7, 5.1, 0.55; 1.0, 5.3, 1.3; 2.1, 15, 0.19; 1.7, 12, 0.71; 1.7, 3.2, -0.22; 1.8, 9.1, 0.68; 2.5, 13, 0.46]. (B) [normalized power at 7-9 Hz, frequency (Hz)] vs 5-minute periods. (C) [mean gridness score (+-SEM), mean firing rate (% of baseline), mean correlation coeff (+-SEM)] vs 10-minute periods.
Grossberg 2021 p229c2h0.60 SMART computer simulations demonstrate that a good enough match of a top-down expectation with a bottom-up feature pattern generates an attentive resonance during which the spikes of active cells synchronize in the gamma frequency range of 20-70 Hz (Figure 5.40). Many labs have reported a link between attention and gamma oscillations in the brain, including two articles published in 2001, one from the laboratory of Robert Desimone when he was at the the National Institute of Mental Health in Bethseda (Fries, Reynolds, Rorie, Desimone 2001), and the other from the laboratory of Wolf Singer in Frankfurt (Engel, Fries, Singer 2001). You'll note that Pascal Fries participated in both studies, and is an acknowledged leader in neurobiological studies of gamma oscillations; eg (Fries 2009). .."
p598fig16.34 Howell: wow! theta rythm modulates place and grid cell firing? Wolfgang Singer: gamma dis-synchrony and schizophrenia (and/or autism)? Goes with theme of Schumann electro-magnetic resonances in atmosphere between ionosphere & ground?
Grossberg: The spiking GridPlaceMap model generates theta-modulated place and grid cell firing, unlike the rate-based model. || Theta-modulated cells in spiking model. [place, grid] cell vs [membrane potential (mV vs time), frequency vs inter-spike intervals (s), power spectra (normalized power vs frequency (Hz))].
p609 Howell: grepStr 'theta|gamma|beta|delta' - Schumann resonances in atmosphere down to Earths surface are the same as well-established brain waves.

Explainable AI

grepStr = Explainable AI

p??? Howell: grepStr 'Explainable AI' - Grossberg explains how ART easily leads to IF-THEN rules... somewhere in book

logic vs connectionist

grepStr = logic|connectionist

Successful [datamodelling, applications] showing effectiveness of cART etc

[bio, neuro, psycho]logy data

p618 Chapter 17 A universal development code - Mental measurements embody universal laws of cell biology and physics I will have to read the book several times to get this all to sink in, notwithstanding 3 decades of reading a small subset of Grossberg's [book, paper]s. Ultimately, though, only working with code and data goes far enough. My re-tying of this material will have errors.
p013sec medical database diagnosis and prediction
p013sec multidimensional data fusion
p013sec classification of data from artificial sensors with high dynamic noise and dynamic range
(synthetic aperture radar, laser radar, multi-spectral infra-red, night vision)
p070sec Cooperation and comptition are universal in biology, including in brain networks
p144sec A perceptual disaster: Uncontrolled filling-in
p147sec Neurophysiological data support end cut predictions
p156sec Explaining texture data with the double filter
p193sec Neurobiological data for ART matching by corticogeniculate feedback
p194sec Neurobiological data for ART matching in visual, auditory, and somatosensory cortex
p229sec Additional brain data about attentive category learning and orienting search
p241sec Explaining human categorization data with ART: Learning rules-plus-exceptions
p249sec Many kinds of psychological and neurobiological data have been explained by ART
p256sec Explaining data about visual neglect: Coordinates, competition, grouping, and action
p259sec Explaining data about visual crowding and situational awareness
p261sec Towards a unified explanation of data about crowding, visual search, and neglect
p262sec Explaining data about change blindness and motion-induced blindness: No map?
p264sec Explaining many data with the same model mechanisms
p267sec Human and monkey data support shroud reset properties: Explanations and predictions
p271sec Target swapping data: Why catastrophic forgetting of invariant categories does not happen
p276sec Two types of perceptual stability cooperate during active conscious vision
p316sec Data supporting the MT-MSTv prediction
p318sec Pyschological and neurophysiological data supporting feedback during motion capture
p331sec Converting motion into action during perceptual decision-making
p333sec Probabilistic motion-based decision-making in monkeys
p355sec Perceptual grouping and attention: Interactions, similarities, and differences
p356sec Analog coherence: solution of the binding problem for perceptual grouping without loss of analog sensitivity
p356sec Fast feedforward processing when data are unambiguous
p358sec Laminar mechanisms of preattentive perceptual grouping
p366sec A unified view of developmental, neurophysiological, and perceptual processes and data
p366sec When attention is not needed to learn: Perceptual learning without awareness
p388sec Dense RDS that induce perceptual completion of partially occluded objects
p398sec Explaining stimulus rivalry and eye rivalry data in a unified way: Habituation again!
p424sec Some simulated auditory streaming data
p435sec Mechanistic unifications of psychologically diverse
p441sec Psychological and neurophysiological data support predicted working memory properties
p471sec Data supporting MTM by activity-dependent habituative gates
p472sec Adaptive resonance in lexical decision tasks: Error rate vs. reaction time data
p474sec Explaining chunk data from the tachistoscopic condition using ART
p474sec Explaining data from the reaction time condition using ART: List item error trade-off
p500sec Transmitter acculmulation and release: Infinity does not exist in biology!
p528sec Some classical data about sequential dependencies influencing future choices
p534sec RTs in behavioural data and simulations about object and spatial searches
p553sec Paradoxical memory consolidation data follow three obvious behavioural facts
p592sec Data and model simulations of grid cell properties along the dorsoventral axis of MEC Mental measurements embody universal laws of cell biology and physics
p625sec A universal development code for biology: Computing with cellular patterns
p631sec Blastula to gastrula in the sea urchin
p634sec Slime mold aggregation and slug motion window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);}
image p025fig01.16 (left panel) The main processing stages of the Cognitive-Emotional-Motor (CogEM) model have anatomical interpretations in terms of sensory cortex, amygdala, and prefrontal cortex. Chapter 13 will describe in greater detail how CS cues activate invariant object categories in the sensory cortex, value categories in the amygdala, and object-value categories in the prefrontal cortex, notably the orbitofrontal cortex. The amygdala is also modulated by internal drive inputs like hunger and satiety. (right panel) Anatomical data support this circuit, as do many neurophysiological data.
|| drive -> amygdala -> prefrontal cortex <-> sensory cortex -> amygdala. [visual, somatosensory, auditory, gustatory, olfactory] cortex -> [amygdala, Orbital Prefrontal Cortex]. amygdala -> Lateral Prefrontal Cortex
image p058fig02.04 Serial learning paradigm: Learning the temporal order of events by practicing them in the order that they occur in time.
|| Learning a global arrow in time. How do we learn to encode the temporal order of events in LTM? serial learning. [w=intra, W=inter]trial interval. "... data about serial verbal learning (Figure 2.4) seemed to suggest that events can go "backwards in time". ..."
image p059fig02.05 Bowed serial position curve. This kind of data emphasizes the importance of modelling how our brains give rise to our minds using nonlinear systems of differential equations.
|| Effects of [inter, intra]trial intervals (Hovland 1938). # of errors vs list position. [w (sec), W (sec)] = (2 6) (4 6) (2 126) (4 126). Nonoccurance of future items reduces the number of errors in response to past items. Thes data require a real-time theory for their explanation! that is, DIFFERENTIAL equations.
image p059fig02.06 The bowed serial position curve illustrates the sense in which "events can go backwards in time" during serial learning.
|| Bow due to backward effect in time. If the past influenced the future, but no conversely: # of errors vs list position; Data (Hoyland Hull, Underwood, etc).
image p071fig02.16 To solve the noise-saturation dilemma, individual neurons in a network that is receiving a distributed spatial patterns of inputs need to remain sensitive to the ratio of input to them divided by all the inputs in that spatial pattern. Although the inputs are delivered to a finite number of neurons, the input and activity patterns are drawn continuously across the cells for simplicity.
|| Noise-Saturation Dilemma. [Ii, xi] vs t. [Input, Activity] pattern [small -> noise, large -> saturation]. Problem: remain sensitive to input RATIOS θi = Ii / sum[j: Ij] as total input I = sum[j: Ij] -> ∞. Many kinds of data exhibit sensitivity to ratios of inputs.
image p073fig02.19 Computing with cells: infinity does not exist in biology!
|| Computing in a bounded activity domain, Gedanken experiment (Grossberg 1970). Vm sub-areas [xm, B - xm], I(all m)], m=[1, i, B].
B excitable sites
xi(t) excited sites (activity, potential)
B - xi(t) unexcited sites
image p082fig02.37 My models begin with behavioral data, since brains are designed to achieve behavioral success. The text explains how models evolve in stages, through a process of successive refinements, or unlumpings. These unlumpings together carry out a kind of conceptual evolution, leading to models that can explain and predict ever larger psychological and neurobiological databases.
|| Modelling method and cycle.
Behavioral data -(art of modeling)-> Design principles <- Neural data <-(brain predictions)- Mathematical model and analysis -(behavioral predictions)-> Behavioural data
Operationalizes "proper level of abstraction"
Operationalizes that you cannot "derive a brain" in one step.
image p085fig02.38 Our models have been used in many large-scale applications to engineering and technology. Linking brain to behavior explains how brain mechanisms give rise to psychological functions, and do so autonomously. The combination of mechanism, function, and autonomy helps to explain their value in helping to solve outstanding problems in technology.
|| Modeling method and cycle.
Behavioral data -(art of modeling)-> Design principles <- Neural data <-(brain predictions)- Mathematical model and analysis -(behavioral predictions)-> Behavioural data
Technology: Mathematical model and analysis <-> Technological applications
At every stage, spin off new model designs and mechanisms to technologist who need autonomous intelligent applications.
image p134fig04.14 The kinds of displays that Michael Paradiso and Ken Nakayamas used to catch filling-in "in the act" and which Karl Arrington then simulated using the Grossberg and Todorovic 1988 model.
|| Experiments on filling-in. Catching "filling0in" in the act (Paradiso, Nakayama 1991). (Arrington 1994 Vision Research 34, 3371-3387) simulated these data using the model of Grossberg and Todorovic 1988.
image p145fig04.23 If end gaps were not closed by end cuts, then color would flow out of every line end!
|| A perceptual disaster in the feature contour system. feature contour, line boundary. input -> [boundary, surface]. boundary -> surface. Color would flow out of every line end! as it does during neon color spreading.
image p151fig04.29 Experimental evidence of bipole cells in cortical area V2 was reported by Von der Heydt, Peterhans, and Baumgarter (1984).
|| Bipoles: first neurophysiological evidence (V2) (von der Heydt, Peterhans, Baumgartner 1984, Peterhans, von der Heydt 1988). (Grossberg 1984) prediction.
Ordering:
Stimulus (S)
probe location * cells in V2
response?
...(S)*... YES
...*...(S) NO
(S)...*... NO
(S)...*...(S) YES
(S)...*...
(more contrast) NO
(S)...*.....(S) YES
Evidence for receptive field.
image p151fig04.30 Anatomical evidence for long-range horizontal connections has also been reported, as illustrated by the example above from (Bosking etal 1997).
|| Anatomy: horizontal connections (V1) (Bosking etal 1997). tree shrew. [10, 20]*[20, 10, 0, -10, -20] (degrees).
image p152fig04.31 The predicted bipole cell receptive field (upper left corner) has been supported by both neurophysiological data and psychophysical data, and used in various forms by many modelers. See the text for details.
|| Bipoles through the ages. (Grossberg 1984; Grossberg, Mongolla 1985). (Field, Hayes, Hess 1993) "association field". (Heitger, von der Heydt 1993). (Williams, Jacobs 1997). cf. "relatability" geometric constraints on which countours get to group (Kellman & Shipley 1991). Also "tensor voting" (Ullman, Zucker, Mumford, Guy, Medioni, ...).
image p159fig04.36 Graffiti art by Banksy exploits properties of amodal boundary completion and spatial impenetrability.
|| p159c1h0.75 perceptual psychologist Nava Rubin "... When the wall is smooth, Banksy leaves the regions previously covered by stencil unpainted, relying of observers' perception to segregate figural regions from the (identically colored) background. But when the wall is patterned with large-scale luminance edges - eg due to bricks - Banksy takes the extra time to fill in unpainted figural regions with another color (Rubin 2015). ..."

image p162fig04.38 How long-range cooperation among bipole cells and short-range competition by hypercomplex cells work together to generate the inverted-U in boundary strength that is found in the data of Figure 4.37 (right panel).
|| Cooperation and competition during grouping.

few lines	wide spacing, inputs outside spatial range of competition, more inputs cause higher bipole activity
more lines	narrower spacing, slightly weakens net input to bipoles from each inducer
increasing line density	causes inhibition to reduce net total input to bipoles

image p163fig04.39 A schematic of the LAMINART model that explains key aspects of laminar visual cortical anatomy and dynamics. LGN -> V1 [6, 4, 2/3] -> V2 [6, 4, 2/3]
|| p163c1h0.6 "... The first article abount laminar computing ... proposed how the laminar cortical model could process 2D pictures using bottom-up filtering and horizontal bipole grouping interactions (Grossbeerg, Mingolla, Ross 1997). In 1999, I was able to extend the model to also include top-down circuits for expectation and attention (Grossberg 1999)(right panel). Such a synthesis of laminar bottom-up, horizontal, and top-down circuits is characteristic of the cerebral cortex (left panel). I called it LAMINART because it began to show how properties of Adaptive Resonance Theory, or ART, notably the ART prediction about how top-down expectations and attention work, and are realized by identical cortical cells and circuits. You can immediately see from the schematic laminar circuit diagram ... (right panel) that circuits in V2 seem to repeat circuits in V1, albeit with a larger spatial scale, despite the fact that V1 and V2 carry out different functions, How this anatomical similarity can coexist with functional diversity will be clarified in subsequent sections and chapters. It enable dfifferent kinds of biological intelligence to communicate seamlessly while carrying out their different psychological functions. ..."
image p165fig04.41 The Kanizsa-Minguzzi ring. See the text for details.
|| p165c1h0.6 "... (left panel), the annulus is divided by two line segments into annular sectors of unequal area. Careful viewing shows that the smaller sector looks a little brighter than the larger one. (Kanizsa, Minguzzi 1986) noted that "this unexpected effect is not easily explained. In fact, it cannot be accounted for by any simple psychological mechanism such as lateral inhibition or freuency filtering. Furthermore, it does not seem obvious to invoke oganizational factors, like figural belongingness or figure-ground articulation."". p165c2h0.35 "... (Grossberg, Todorovic 1988). Our main claim is that the two radial lines play two roles, one in the formation of boundaries with which to contain the filling-in process, and the other as a source of feature contour signals that are filled-in within the annular regions to create a surface brightness percept. ..."
image p232fig05.40 Computer simulation of how the SMART mode generates (a) gamma oscillations if a good enough match occurs, or (c) beta oscillations if a bad enough match occurs. See the text for details.
|| Brain oscillations during match/mismatch, data, simulation. (a) TD corticothalamic feedback increases synchrony (Sillito etal 1994) (b) Match increases γ oscillations (c) Mismatch increases θ,β oscillations
image p232fig05.41 (a)-(c). The sequence of interlaminar events that SMART predicts during a mismatch reset. (d) Some of the compatible neurophysiological data.
|| Mismatch causes layer 5 dendritic spike that trigger reset. (a) Arousal causes increase in nonspecific thalamic nuclei firing rate and layer 5 dendritic and later somatic spikes (Karkum and Zhu 2002, Williams and Stuart 1999) (b) Layer 5 spikes reach layer 4 via layer 6i and inhibitory neurons (Lund and Boothe 1975, Gilbert and Wiesel 1979) (c) habituative neurotransmitters in layer 6i shift the balance of active cells in layer 4 (Grossberg 1972, 1976) (d) Dendritic stimulation fires layer 5 (Larkum and Zhu 2002) stimulation apical dendrites of nonspecific thalamus
image p252fig06.01 A surface-shroud resonance begins to form when the surface representations of objects bid for spatial attention. In addition to these topographic excitatory inputs, there is long-range inhibition of the spatial attention cells that determines which inputs will attract spatial attention.
|| Bottom-up spatial attention competition. [more, less] luminous perceptual surfaces -> competition -> spatial attention
image p253fig06.02 After bottom-up surface inputs activate spatial attentional cells, they send top-down topographic excitatory signals back to the surface representations. This recurrent shunting on-center off-surround network contrast enhances larger attentional activities while approximately normalizing the total spatial attentional activity. A surface-shroud resonance hereby forms that selects an attentional shroud, enhances the perceived contrast of the attended surface (light blue region), and maintains spatial attention on it.
|| Surface-shroud resonance. perceptual surfaces -> competition -> spatial attention. (Carrasco, Penpeci-Talgar, and Eckstein 2000, Reynolds and Desimone 2003)
image p257fig06.05 A curve tracing task with monkeys was used by Roelfsema, Lamme, and Spekreijse in 1998 to demonstrate how spatial attention can flow along object boundaries. See the text for details.
|| Attention flows along curves: Roelfsema etal 1998: Macaque V1. fixation (300ms) -> stimulus (600ms RF - target curve, distractor) -> saccade. Crossed-curve condition: attention flows across junction between smoothly connected curve segments, Gestalt good continuation
image p258fig06.06 Neurophysiological data and simulation of how attention can flow along a curve. See the text for details.
|| Simulation of Roelfsema etal 1998, data & simulation. Attention directed only to far end of curve. Propagates along active layer 2/3 grouping to distal neurons.
image p265fig06.13 The basal ganglia gate perceptual, cognitive, emotional, and more processes through parallel loops.
|| [motor, ocularmotor, dorsolateral, ventral-orbital, anterior cingulate] vs. [Thalamus, pallidum-subs, nigra, Striatum, Cortex]
image p267fig06.14 Feedback from object surfaces to object boundaries uses surface contours. This feedback assures complementary consistency and enables figure-ground separation. A corollary discharge of the surface contours can be used to compite salient object feature positions.
|| Perceptual consistency and figure-ground separation.
image p271fig06.17 Persistent activity in IT cells is just what is needed to enable view-invariant object category learning by ARTSCAN to be generalized to [view, position, size]-invariant category learning by positional ARTSCAN, or pARTSCAN. See the text for details.
|| Persistent activity in IT. Physiological data show that persistent activity exist in IT (Fuester and Jervey 1981, Miyashita and Chang 1988, Tomita etal 1999). Adapted from (Tomita etal 1999 Nature)
image p273fig06.20 pARTSCAN can simulate the IT cell recoding that Li and DiCarlo reported in their swapping experiments because the swapping procedure happens without causing a parietal reset burst to occur. Thus the originally activated invariant category remains activated and can get associated with the swapped object features.
|| Simulation of Li and DiCarlo swapping data. data (Li and DiCarlo 2008), model (Cao, Grossberg, Markowitz 2011). normalized response vs. exposure (swaps and/or hours)
image p274fig06.21 pARTSCAN can also simulate the trade-off in IT cell responses between position invariance and selectivity that was reported by Zoccolan etal 2007. This trade-off limits the amount of position invariance that can be learned by a cortical area like V1 that is constrained by the cortical magnification factor.
|| Trade-off in IT cell response properties. Inferotemporal cortex cells with greater position invariance respond less selectively to natural objects. invariance-tolerance, selectivity-sparseness. data (Zoccolan etal 2007) model (Grossberg, Markowitzm, Cao 2011). position tolerance (PT, degrees) vs sparseness (S)
image p275fig06.23 Data from (Akrami etal 2009) and our simulation of it. See the text for details.
|| IT responses to image morphs. data vs model
image p284fig07.02 Psychophysical data (top row) and simulation (bottom row) of how persistence decreases with flash illuminance and duration.
|| Persistence data and simulations. (Francis, Grossberg, Mingolia 1994 Vision Research, 34, 1089-1104). Persistence decreases with flash illuminance and duration (Bowen, Pola, Matin 1974; Breitmeyer 1984; Coltheart 1980). Higher luminance or longer duration habituates the gated dipole ON channel more. Causes larger and faster rebound in the OFF channel to shut persisting ON activity off.
image p285fig07.03 Persistence decreases with flash illuminance and duration due to the way in which habituative transmitters regulate the strength of the rebound in response to offset of a stimulating input, and how this rebound inhibits previously activated bipole cells.
|| Persistence data and simulations (Francis, Grossberg, Mingolia 1994 Vision Research, 34, 1089-1104). Persistence decreases with flash illuminance and duration. Horizontal input excites a horizontal bipole cell, which supports persistence. Offset of the horizontal input causes a rebound of activity in the vertical pathway, which inhibits the horizontal bipole cell, thereby terminating persistence.
image p286fig07.04 Illusory contours persist longer than real contours because real contours have more inducers whose rebound at contour offset can cause faster boundary reset. Illusory contours also take longer to form than real contours, which explains the increasing portion of the curve.
|| Persistence data and simulations (Meyer, Ming 1988; Reynolds 1981). Increasing portion of curve is due to formation time of the illusory contour. Longer persistence is due to fewer bottom-up inducers of an illusory contour that has the same length as a real contour: only illuminance-derived edges generate reset signals. When bottom-up inducers are inhibited by OFF cell rebounds, their offset gradually propagates to the center of the illusory contour.
image p286fig07.05 This figure shows the propagation through time of illusory contour offset from the rebounded cells that got direct inputs to the center of the contour.
|| Persistence data and simulations. Illusory contours persist longer than real contours (Meyer, Ming 1988; Reynolds 1981). When bottom-up inducers are inhibited by OFF cell rebounds, their offset gradually propagates to the center of the illusory contour.
image p287fig07.06 The relative durations of persistence that occur due to an adaptation stimulus of the same or orthogonal orientation follow from the properties of the habituative gated dipoles that are embedded in the boundary completion system.
|| Persistence data and simulations. Change in persistence depends on whether adaptation stimulus has same or orthogonal orientation as test grating (Meyer, Lawson, Cohen 1975). If adaptation stimulus and test stimulus have the same orientation, they cause cumulative habituation, which causes a stronger reset signal, hence less persistence. When they are orthogonal, the competition on the ON channel is less, hence more persistence.
image p287fig07.07 Persistence increases with distance between a target and a masking stimulus due to weakening of the spatial competition in the first competitive stage of hypercomplex cells.
|| Persistence data and simulations. Persistence increases with distance between a target and a masking stimulus (Farrell, Pavel, Sperling 1990). There is less spatial competition from the masker to the target when they are more distant, hence the target is more persistent.
image p297fig08.08 When a second flash is more intense than the first flash, then apparent motion may occur from the second to the first flash.
|| Delta motion: motions from the second to the first flash. Data: (Kolers 1972; Korte 1915). Simulation: (Grossberg, Rudd 1992). This occurs when the luminance or contrast of the second flash is large compared to that of the first flash. Sustained and transient cells obey shunting dynamics whose averaging rates speed up with output intensity. The first flash to wane is the one that will be the source of the G-wave.
image p297fig08.09 Simulation of motion in opposite directions that is perceived when two later flashes occur on either side of the first flash.
|| Split motion. Data: (H.R. Silva 1926), Simulation: (Grossberg, Rudd 1992)
image p298fig08.10 Simulation of the motion speed-up that is perceived when flash duration decreases.
|| "The less you see it, the faster it moves". Data: (Giaschi, Anstis 1989), Simulation: (Grossberg, Rudd 1992). ISI = 0, flash duration decreases; SOA = constant, flash duration decreases
image p304fig08.22 Data (top image) and simulation (bottom image) of Korte's laws. The laws raise the question of how ISIs in the hundreds of milliseconds can cause apparent motion.
|| Korte's Laws, Data: (Korte 1915) Simulation: (Francis, Grossberg 1996)
image p311fig08.30 The data of (Castet etal 1993) in the left image was simulated in the right image by the 3D FORMOTION model that I developed with my PhD student Jonathan Chey. These data provide insight into how feature tracking signals propagate from the ends of a line to its interior, where they capture consistent motion directional signals and inhibit inconsistent ones.
|| Solving the aperture problem. A key design problem: How do amplified feature tracking signals propagate within depth to select the cirrect motion directions at ambiguous positions? This propagation from feature tracking signals to the line interior determines perceived speed in Castet etal data, which is why speed depends on line tilt and length. Data: (Castet etal 1993), Simulation: (Chey etal 1997)
image p319fig08.38 The neurophysiological data from MT (left image) confirms the prediction embodied in the simulation of MT (right image) concerning the fact that it takes a long time for MT to compute an object's real direction of motion.
|| Solving the aperture problem takes time. MT Data (Pack, Born 2001), MT simulation (Chey, Grossberg, Mingolia 1997)
image p333fig08.58 Neurophysiological data (left image) and simulation (right image) of LIP data during correct trials on the RT task. See the text for details.
|| LIP responses during RT task correct trials (Roltman, Shadlen 2002). More coherence in favored direction causes faster cell activation. More coherence in opposite direction causes faster cell inhibition. Coherence stops playing a role in the final stages of LIP firing.
image p334fig08.59 Neurophysiological data (left column) and simulations (right column) of LIP responses for the FD task during both [correct, error] trials. See the text for details.
|| LIP responses for the FD task during both [correct, error] trials (Shadlen, Newsome 2001). LIP encodes the perceptual decision regardless of the true direction of the dots. Predictiveness of LIP responses on error trials decreases with increasing coherence.
image p334fig08.60 Behavioral data (left image) and simulation (right image) about accuracy in both the RT and FD tasks. See text for details
|| Behavioral data: % correct vs % coherence (Mazurek etal 2003; Roltman, Shadien 2002). More coherence in the motion causes more accurate decisions. RT task accuracy at weaker coherence levels is slightly better than FD task accuracy.
image p335fig08.61 Behavioral data (left image) and simulation (right image) about speed in correct and error trials of the RT task. See text for details.
|| Behavioral data: speed, correct and error trials (RT task) (Roltman, Shadien 2002). More coherence in the motion causes faster reaction time.
image p335fig08.62 More remarkable simulation fits (right column) to LIP neurophysiology data (left column) about where and when to move the eyes.
|| LIP encodes not only where, but also when, to move the eyes. ...No Bayes(Roltman, Shadien 2002). Firing rate (sp/s) vs time (ms). Slope of firing rate (sp/s^2) vs % correct.
image p342fig09.11 Psychophysical data (left panel) and computer simulation (right column) of the importance of efference copy in real movements. See the text for details.
|| Heading: move to wall and fixate stationary object (adapted from Warren, Hannon 1990). Inaccurate for simulated eye rotation, accurate for real eye rotation, need confirmation by efference copy!
image p343fig09.13 When one scans the three different types of pears in the left image, as illustrated by the jagged blue curve with red movement end positions, and transforms the resulting retinal images via the cortical magnification factor, or log polar mapping, the result is the series of images in the right column. How do our brains figure out from such confusing data which views belong to which pear?
|| View-invariant object learning and recognition Three pears: Anjou, Bartlett, Comice. Which is the Bartlett pear? During unsupervised scanning and learning about the world, no one tells the brain what views belong to which objects while it learns view-invariant object categories. Cortical magnificantion in V1.
image p349fig09.20 Using virtual reality displays (left image), (Fajen, Warren 2003) collected data (right two images) about how observers avoid obstacles (open circular disks) as a function of their distance and angular position as they navigate towards a fixed goal (x). These data illustrate how goals act as attractors while obstacles act as repellers.
|| Steering from optic flow (Fajen, Warren 2003). goals are attractors, obstacles are repellers. Damped spring model explains human steering data.
image p352fig09.26 The final stage of the model computes a beautiful expansion optic flow that permits an easy estimate of the heading direction, with an accuracy that matches that of human navigators.
|| The model generates accurate heading (Warren, Hannon 1990; Royden, Crowell, Banks 1994). Maximally active MSTd cell = heading estimate. Accuracy matches human data. Random dots [mean +-1.5°, worst +-3.8°], Random dots with rotation [accurate with rotations <1°/s, rotation increases, error decreases], OpenGL & Yosemite benchmark +-1.5°, Driving video +-3°.
image p356fig10.03 Laminar computing achieves at least three basic properties of visual processing that have analogs in all biologically intelligent behaviors. These properties may be found in all cortical circuits in specialized form.
|| What does Laminar Computing achieve? 1. Self-stabilizing development and learning; 2. Seamless fusion of a) pre-attentive automatic bottom-up processing, b) attentive task-selective top-down processing; 3. Analog coherence: Solution of Binding Problem for perceptual grouping without loss of analog sensitivity. Even the earliest visual cortical stages carry out active adaptive information processing: [learn, group, attention]ing
image p357fig10.04 Laminar Computing achieves its properties by computing in a new way that sythesizes the best properties of feedforward and feedback interactions, analog and digital computations, and preattentive and attentive learning. The property of analog coherence enables coherent groupings and decisions to form without losing sensitivity to the amount of evidence that supports them.
|| Laminar Computing: a new way to compute. 1. Feedforward and feedback: a) Fast feedforward processing when data are unambiguous (eg Thorpe etal), b) slower feedback chooses among ambiguous alternatives [self-normalizing property, real-time probabiligy theory], c) A self-organizing system that trades certainty against speed: Goes beyond Bayesian models! 2. Analog and Digital: Analog Coherence combines the stability of digital with the sensitivity of analog. 3. Preattentive and Attentive Learning: Reconciles the differences of (eg) Helmholtz and Kanizsa, "A preattentive grouping is its own 'attentional' prime"
image p360fig10.09 Perceptual grouping is carried out in layer 2/3 by long-range horizontal excitatory recurrent connections, supplemented by short-range disynaptic inhibitory connections that together realize the bipole grouping properties that are diagrammed in Figure 10.10.
|| Grouping starts in layer 2/3. LGN-> 6-> 4-> 2/3: 1. Long-range horizontal excitation links collinear, coaxial receptive fields (Gilbert, Wiesel 1989; Bosking etal 1997; Schmidt etal 1997) 2. Short-range disynaptic inhibition of target pyramidal via pool of intraneurons (Hirsch, Gilbert 1991) 3. Unambiguous groupings can form and generate feedforward outputs quickly (Thorpe etal 1996).
image p361fig10.10 Bipole grouping is achieved by long-range horizontal recurrent connections that also give rise to short-range inhibitory interneurons which inhibit nearby bipole cells as well as each other.
|| Bipole property controls perceptual grouping. Collinear input on both sides. Excitatory inputs summate. Inhibitory inputs normalize, Shunting inhibition! Two-against-one. Cell is excited.
image p367fig10.15 Data (left column) and simulation (right column) of how attention prevents a masking stimulus from inhibiting the response to the on-center of the cell from which the recording was made.
|| Attention protects target from masking stimulus (Reynolds etal 1999; Grossberg, Raizada 2000).
image p367fig10.16 Neurophysiological data (left image) and simulation (right image) of how a low-contrast target can be facilitated if it is surrounded by a paid (31May2023 Howell - is word correct?) of collinear flankers, and suppresssed by them if it has high contrast.
|| Flankers can enhance or suppress targets (Polat etal 1998; Grossberg, Raizada 2000). target alone, target + flankers, flankers alone.
image p368fig10.17 Neurophysiological data (left image) and simulation (right image) showing that attention has a greater effect on low contrast than high contrast targets.
|| Attention has greater effect on low contrast targets (DeWeerd etal 1999; Raizada, Grossberg 2001). Threshold increase (deg) vs Grating contrast (%), [no, with] attention
image p368fig10.18 Neurophysiological data (left image) and simulation (right image) of relative on-cell activities when the input to that cell may also be surroubded by iso-orientation or perpendicular textures.
|| Texture reduces response to a bar: iso-orientation suppression (Knierim, van Essen 1992), perpendicular suppression (Raizada, Grossberg 2001)
image p369fig10.19 Data from (Watanabe etal 2001) showing perceptual learning of the coherent motion direction, despite the lack of extra-foveal attention and awareness of the moving stimuli.
|| Unconscious perceptual learning of motion direction, % correct for two tests, compared to chance level results.
image p393fig11.31 (Todd, Akerstrom 1987) created a series of 2D images from discrete black patches on a white disk and showed how the perceived depth varies with the factors summarized in the figure. The LIGHTSHAFT model quantitatively simulated their data.
|| Factors determining depth-from-texture percept. Perceived depth varies with texture element width, but only when elements are elongated and sufficiently aligned with one another to form long-range groupings. Data of (Todd, Akerstrom 1987) simulated by the LIGHTSHAFT model of (Grossberg, Kuhlmann 2007). [HP, LP, CCE, CCS, RO]
image p399fig11.39 Simulation of the eye rivalry data of (Lee, Blake 1999).
|| [Binocular, [left, right] eye] activity
image p402fig11.43 A pair of disparate images of a scene from the University of Tsukuba. Multiview imagre database.
|| input [left, right]
image p407fig12.03 Neurophysiological data showing how motor cortical cells code different vectors that are sensitive to both the direction of the commanded movement and its length.
|| (a) Single primary motor cortex neuron, onset of movement -> on..., radial architecture... (b) Motor cortex neuronal population, radial architecture...
image p409fig12.04 (top half) Neurophysiological data of vector cell responses in motor cortex. (bottom half) VITE model simulations of a simple movement in which the model's difference vector simulates the data as an emergent property of network interactions.
|| Neurophysiological data. VITE model [Present Position vector, Difference vector, Outflow velocity vector, go signal].
image p410fig12.06 Monkeys seamlessly transformed a movement initiated towards the 2 o'clock target into one towards the 10 o'clock target when the later target was substituted 50 or 100 msec after activation of the first target light.
||
image p414fig12.11 Neurophysiological data from cortical areas 4 and 5 (every other column) and simulations thereof (other columns) during a reach.
|| activation vs time. (a) area 4 phasic RT (IFV) (b) area 4 tonic (OPV) (c) area 4 phasic-tonic (OFPV) (d) area 4 phasic MT (DVV) (e) area 5 phasic (DV) (f) area 5 tonic (PPV)
image p424fig12.21 One of the many types of data about pitch processing that are simulated by the SPINET model. See the text for details.
|| Pitch shifts with component shifts (Patterson, Wightman 1976; Schouten 1962). Pitch vs lowest harmonic number.
image p425fig12.23 ARTSTREAM simulations of the auditory continuity illusion and other streaming properties (left column, top row). When two tones are separated by silence (Input), a percept of silence also separates them in a spectral-pitch resonance. (left column, bottom row). When two tones are separated by broadband noise, the percept of tone continues through the noise in one stream (stream 1) while the remainder of the noise occurs in a different stream (stream 2). (right column) Some of the other streaming properties that have been simulated by the ARTSTREAM model.
|| Auditory continuity does not occur without noise. Auditory continuity in noise. Other simulated streaming data.
image p428fig12.25 (left architecture) Auditory-articulatory feedback loop whereby babbled sounds active learning in an imitative map that is later used to learn to reproduce the sounds of other speakers. An articulatory-to-auditory expectation renders learning possible by making the auditory and motor data dimensionally consistent, as in the motor theory of speech. (right architecture) Parallel streams in the ARTSPEECH model for learning speaker-independent speech and language meaning, including a mechanism for speaker normalization (right cortical stream) and for learning speaker-dependent vocalic qualities (left cortical stream).
|| left: Speaker-dependent vocalic qualities; right: Speaker-independent speech and language meaning
image p432fig12.28 (left image) The SpaN model simulates how spatial representations of numerical quantities are generated in the parietal cortex. (right image) Behavior numerosity data and SpaN model simulations of it.
|| (Left) preprocessor-> spatial number map-> Comparison wave. (Right) data axis: number of lever presses; model axis: node position in the spatial number axis
image p437fig12.32 Data from a free recall experiment illustrate the bowed serial position curve.
|| Serial position function for free recall Data: (Murdock 1962 JEP 64, 482-488). % correct vs position of word on a 40-word list. Primacy gradient can be a mixture of STM and LTM read-out.
image p437fig12.33 Item and Order working memory models explain free recall data, as well as many other psychological and neurobiological data, by simulating how temporal series of events are stored as evolving spatial patterns of activity at content-addressable item categories. The categories with the largest activities are rehearsed first, and self-inhibit their activity as they do so in order to prevent tem from being rehearsed perseveratively. The laws whereby the items are stored in working memory obey basic design principles concerning list categories, or chunks, of sequences of stored items can be stably remembered.
|| Working memory models: item and order, or competitive queuing (Grossberg 1978; Houghton 1990; Page, Norris 1998). Event sequence in time stored as an evolving spatial pattern of activity. Primacy gradient of working memory activation stores correct temporal order at content-addressable cells. Maximally activated cell populations is performed next when a rehearsal wave is turned on. Output signal from chosen cell population inhibits its own activity to prevent perseveration: inhibition of return. Iterate until entire sequence is performed.
image p443fig12.41 Neurophysiological data from the Averbeck etal sequential copying experiments show the predicted primacy gradient in working memory and the self-inhibition of activity as an item is stored. When only the last item remains stored, it has the highest activity becasuse it has been freed from inhibition by earlier items.
|| Neurophysiology of sequential copying
image p444fig12.42 The LIST PARSE laminar cortical model of working memory and list chunking that I published with Lance Pearson in 2008 simulated the Averbeck etal data in Figure 12.41, as in the left column of the figure. It also simulated cognitive data about working memory storage by human subjects. See the text for details.
|| LIST PARSE: Laminar cortical model of working memory and list chunking (Grossberg, Pearson 2008). Simulates data about: [immediate, delayed, continuous] distractor free recall; immediate serial recall; and variable-speed sequential perfomance of motor acts. [velocity, acceleration] vs time (ms) from recall cue.
image p446fig12.44 (left column, top row) LIST PARSE can model linguistic data from human subjects. In this figure, model parameters are fixed to enable a close fit to data about error-type distributions in immediate free recall experiments, notably transposition errors. (right column, top row) Simulation and data showing bowing of the serial position curve, including an extended primacy gradient. (left column, bottom row) The simulation curve overlays data about list length effects, notably the increasing recall difficulty of longer lists during immediate serial recall (ISR). (right column, bottom row) Simulation (bottom image) and data (top image) of the limited temporal extent for recall.
|| (1. TL) Error-type distributions in immediate serial recall (Hanson etal 1996). % occurence vs serial position. Graph convention: Data- dashed lines; Simulations- solid lines. Six letter visual ISR. Order errors- transpositions of neighboring items are the most common. Model explanation: Noisy activation levels change relative oorder in primacy gradient. Similar activation of neighboring items most susceptible to noise. Model paramenters fitted on these data. (2. TR) Bowing of serial position curve (Cowan etal 1999). % correct vs serial position. Auditory ISR with various list lengths (graphs shifted rightward): For [, sub-]span lists- extended primacy, with one (or two) item recency; Auditory presentation- enhanced performance for last items. LIST PARSE: End effects- first and last items half as many members; Echoic memory- last presented item retained in separate store. (3. BL) List length effects, circles (Crannell, Parrish 1968), squares (Baddeley, Hitch 1975), solid line- simulation. % list correct vs list length. Variable list length ISR: longer lists are more difficult to recall. LIST PARSE: More items- closer activation levels and lower absolute activity level with enough inputs; Noise is more likely to produce order errors, Activity levels more likely to drop below threshold;. (4. BR) Limited temporal extent for recall (Murdoch 1961). % recalled vs retention interval (s). ISR task with distractor-filled retention intervals (to prevent rehersal): Increasing retention interval - decreases probabilty of recalling list correctly; Load dependence- longer list more affected by delays; Performance plateau- subjects reach apparent asymptote. LIST PARSE: Increase convergence of activities with time; loss of order information;.
image p447fig12.45 (left column) LIST PARSE simulations of the proportion of order errors as a function of serial position for 6 item lists with (a) an extended pause of 7 time units between the third and fourth items, and (b) pauses of 5 time units (solid curve) and 10 time units (dashed curve) between all items. (right column) Simulations (solid curves) and data (dashed curves) illustrating close model fits in various immediate free recall tasks.
|| (Left) Temporal grouping and presentation variability. Temporal grouping: Inserting an extended pause leads to inter-group bowing; Significantly different times of integration and activity levels across pause, fewer interchanges. (Right) Immediate free recall, and [delayed, continuous] distractor-free recall. Overt rehersal IFR task with super-span (ie 20 item) lists: Extended recency- even more extended with shorter ISIs; Increased probability of recall with diminished time from last rehearsal; Early items in list rehearsed most;. LIST PARSE (unique) for long lists: Incoming items from recency gradient; Rehearsal (re-presentation) based upon level of activity.
image p452fig12.48 (left column) In experiments of (Repp etal 1978), the silence duration between the words GRAY and SHIP was varied, as was the duration of the fricative noise in S, with surprising results. (right column) The red arrow directs our attention to surprising perceptual changes as silence and noise durations increase. See the text for details.
|| Perceptual integration of acoustic cues, data (Repp etal 1978). GRAY-> silence duration-> SHIP (noise duration from start of word). Noise duration vs silence duration: GRAY SHIP <-> [GREAT SHIP <-> GRAY CHIP] <-> GREAT CHIP.
image p453fig12.49 The ARTWORD model that I published in 2000 with my PhD student Christopher Myers simulates data such as the (Repp etal 1978) data in Figure 12.68. See the text for details.
|| ARTWORD model (Grossberg, Myers 2000). Input phonetic features-> Phonemic item working memory-> Masking Field unitized lists-> Automatic gain control-> Phonemic item working memory. [habituative gate, adaptive filter]s.
image p465fig12.63 Neurophysiological data (left image) and lisTELOS stimulation (right figure) showing how microstimulation biases saccadic performance order but not the positions to which the saccades will be directed. See the text for details.
|| Saccade trajectories converge to a single location in space. Microstimulation biased selection so saccade trajectories converged toward a single location in space. [Data, model] contra <-> Ipsi (msec)
image p468fig12.65 Linguistic properties of the PHONET model and some of the data that it simulates. The upper left image summarizes the asymmetric transient-to-sustained gain control that helps to create invariant intraword ratios during variable-rate speech. The lower left image summarizes the rate-dependent gain control of the ARTPHONE model that creates rate-invariant working memory representations in response to sequences of variable-rate speech. The right image summarizes the kind of paradoxical VC-CV category boundary data of (Repp 1980) that ARTPHONE simulates. See the text for details.
|| (left upper) [transient, sustained] [working memory, filter, category]. (left lower) phone inputs-> [input rate estimate, features], Features w <- habituative transmitter gates -> categories-> rate invariant phonetic output, input rate estimate-> gain control-> [features, categories] rate-dependent integration of categories and features. (right) % 2-stop vs VC-CV silent interval (msec): [ib-ga, ib-ba, iga, iba].
image p469fig12.66 (left column) A schematic of how preserving relative duration, as in the first and third images, of consonant and vowel pairs can preserve a percept, in this case of /ba/, but not doing so, as in the first and second images, can cause a change in percept, as from /ba/ to /wa/, as in the data of (Miller, Liberman 1979) that PHONET simulates. (right column) Changing frequency extent can also cause a /ba/ - /wa/ transition, as shown in data of (Schwab, Sawusch, Nusbaum 1981) that PHONET also simulates.
|| (left image) Maintaining relative duration as speech speeds up preserves percept (Miller, Liberman 1979). frequency vs time- [/ba/, /wa/, /ba/] (right image) Changing frequency extent causes /b/-/wa/ transition (Schwab, Sawusch, Nusbaum 1981). frequency vs time- [/ba/, /wa/] Dt extent.
image p473fig12.69 Error rate and mean reaction time (RT) data from the lexical decision experiments of (Schvanevelt, McDonald 1981). ART Matching Rule properties explain these data in (Grossberg, Stone 1986).
|| (left) Error rate vs type of prime [R, N, U], [non,] word. (right) Mean RT (msec) vs type of prime [R, N, U], [non,] word.
image p474fig12.70 The kind of model macrocircuit that was used in (Grossberg, Stone 1986) to explain lexical decision task data.
|| inputs-> A1 <-> A2 oconic sensory features <-> A3 item and order in sensory STM <-> A4 list parsing in STM (masking field) <-> A5 semantic network (self-feedback). [A4, A5] <-> V* visual object recognition system. M1-> [outputs, A1]. M1 <-> M2 iconic motor features <-> M3 item and order in motor STM. A2-> M2. A3-> M3.
image p476fig12.71 Word frequency data of (Underwood, Freund 1970) that were explained in (Grossberg, Stone 1986).
|| percent errors vs frequency of old words [L-H to H-H, L-L to H-L].
image p484fig13.04 The top-down feedback from the orbitofrontal cortex closes a feedback loop that supports a cognitive-emotional resonance. If this resonance can be sustained long enough, it enables us to have feelings at the same time that we experience the categories that caused them.
|| Cognitive-Emotional resonance. Basis of "core consciousness" and "the feeling of what happens". (Damasio 1999) derives heuristic version of CogEM model from his clinical data. Drive-> amygdala-> prefrontal cortex-> sensory cortex, resonance around the latter 3. How is this resonance maintained long enough to become conscious?
image p485fig13.06 (left column) An inverted-U occurs in conditioned reinforcer strength as a function of the ISI between the CS and the US. Why is learning attenuated at 0 ISI? (right column) Some classical conditioning data that illustrate the inverted-U in conditioning as a function of the ISI.
|| InterStimulus Interval (ISI) effect. Data from (Dmith etal 1969; Schneiderman, Gormezano 1964).
image p490fig13.15 The CogEM circuit is an ancient design that is found even in mollusks like Aplysia. See the text for details.
|| Aplysia (Buononamo, Baxter, Byrne, Neural Networks 1990; Grossberg, Behavioral and Brain Sciences 1983). Facilitator neuron ~ drive representation.
image p504fig13.31 Behavioral contrast can occur during reinforcement learning due to decreases in either positive or negative reinforcers. See Figure 13.32 for illustrative operant conditioning data.
|| Behavioral contrast: rebounds! Shock level vs trials. 1. A sudden decrease in frequency or amount of food can act as a negative reinforcer: Frustration. 2. A sudden decrease in frequency or amount of shock can act as a positive reinforcer: Relief.
image p523fig14.03 (a) The MOTIVATOR neural model generalizes CogEM by also including the basal ganglia. It can hereby explain and simulate complementary functions of the amygdala and basal ganglia (SNc) during conditioning and learned performance. The basal ganglia generate Now Print signals in response to unexpected rewards. These signals modulate learning of new associations in many brain regions. The amygdala supports motivated attention to trigger actions that are expected to occur in response to conditioned or unconditioned stimuli. Object Categories represent visual or gustatory inputs in anterior inferotemporal (ITA) and rhinal (RHIN) cortices, respectively. Value Categories represent the value of anticipated outcomes on the basis of hunger and satiety inputs, in amygdala (AMYG) and lateral hypothalamus (LH). Object-Value Categories resolve the value of competing perceptual stimuli in medial (MORB) and lateral (ORB) orbitofrontal cortex. The Reward Expectation Filter detects the omission or delivery of rewards using a circuit that spans ventral striatum (VS), ventral pallidum (VP), striosomal delay (SD) cells in the ventral striatum, the pedunculopontine nucleus (PPTN) and midbrain dopaminergic neurons of the substantia nigra pars compacta/ventral tegmental area (SNc/VTA). The circuit that processes CS-related visual information (ITA, AMTG, ORB) operates in parallel with a circuit that processes US-related visual and gustatory information (RHIN, AMYG, MORB). (b) Reciprocal adaptive connections between hypothalamus and amygdala enable amygdala cells to become learned value categories. The bottom region represents hypothalmic cells, which receive converging taste and metabolite inputs whereby they become taste-drive cells. Bottom-up signals from activity patterns across these cells activate competing value category, or US Value Representations, in the amygdala. A winning value category learns to respond selectively to specific combinations of taste-drive activity patterns and sends adaptive top-down priming signals back to the taste-drive cells that activated it. CS-activated conditioned reinforcer signals are also associatively linked to value categories. Adpative connections end in (approximately) hemidiscs. See the text for details.
||
image p533fig14.09 Search data and ARTSCENE Search simulations of them in each pair of images from (A) to (F). See the text for details.
|| 6*[data vs simulation], [Response time (ms) versus epoch].
image p542fig15.04 Conditioning data from (Smith 1968; Millenson etal 1977). The former shows the kind of Weber Law and inverted U that were simulated in Figure 15.3. The latter shows that, if there are two ISIs during an experiment, then the animals learn to adaptively time their responses with two properly scaled Weber laws.
|| (left) One ISI (Smith 1968) [mean membrane extension (mm) versus time after CS onset (msec)]. (right) Two ISIs (Millenson etal 1977) [200, 100] msec CS test trials, [mean momentary CS amplitude (mm) vs time after CS onset (msec)]. (bottom) Conditioned eye blinks, made with nictitating membrane and/or eyelid, are adaptively timed: peak closure occurs at expected time(s) of arrival of the US following the CS and obeys a Weber Law.
image p543fig15.05 Simulation of conditioning with two ISIs that generate their own Weber Laws, as in the data shown in Figure 15.4.
|| Learning with two ISIs: simulation: R = sum[all: f(xi)*yi*xi] vs msec. Each peak obeys Weber Law! strong evidence for spectral learning.
image p556fig15.24 (a) Data showing normally timed responding (solid curve) and short latency responses after lesioning cerebellar cortex (dashed curve). (b) computer simulation of short latency response after ablation of model cerebellar cortex.
||
image p559fig15.28 Neurophysiological data (left column) and model simulations (right column) of SNc responses. See the text for details.
|| membrane potential vs time
image p573fig16.01 The experimental chamber (A) and neurophysiological recordings from a rat hippocampus (B) that led to the discovery of place cells. See the text for details.
||
image p574fig16.02 Neurophysiological recordings of 18 different place cell receptive fields. See the text for details.
||
image p575fig16.03 As a rat navigates in its experimental chamber (black curves), neurophysiological recordings disclose the firing patterns (in red) of (a) a hippocampal place cell and (b) an entrorhinal grid cell.
||
image p582fig16.08 Some experimental evidence for stripe-like cell receptive fields has been reported. The band cells posited by Neil Burgess also exhibit the one-dimensional firing symmetry of stripe cells, but are modeled by oscillatory intererence. See the text for details.
|| Evidence for stripe-like cells. Entorhinal cortex data (Sargolini, Fyhn, Hafting, McNaughton, Witter, Moser, Moser 2006; Krupic, Burgess, O'Keefe 2012). Similar hypothetical construct used by Interference model but position is decoded by grid cell oscillatory interference- Band Cells (Burgess 2008).
image p589fig16.19 Neurophysiological data showing the smaller dorsal grid cell scales and the larger ventral grid cell scales.
|| Spatial scale of grid cells increase along the MEC dorsoventral axis (Hafting etal 2005; Sargolini etal 2006; Brun etal 2008). [dorsal (left), ventral (right)] cart [rate map, autocortelogram]. How does the spatial scale increase along the MEC dorsoventral axis?
image p593fig16.26 Data (left column) and simulations (right column) of the gradient of increasing grid cell spacing along the dorsoventral axis of MEC.
|| Gradient of grid spacing along dorsoventral axis of MEC (Brun etal 2008). data-[Distance (m?), Median grid spacing (m?)] simulations-[Grid spacing (cm), Grid spacing (cm)] vs response rate.
image p594fig16.27 Data (left column) and simulations (right column) of the gradient of increasing grid cell field width along the dorsoventral axis of MEC.
|| Gradient of field width along dorsoventral axis of MEC (Brun etal 2008). data-[Distance (m?), Width autocorr peak (m?)] simulations-[Grid field width (cm), Width autocorr peak (cm)] vs response rate.
image p595fig16.28 Data (left column) and simulations (right column) about peak and mean grid cell response rates along the dorsoventral axis of MEC.
|| Peak and mean rates at different locations along DV axis of MEC (Brun etal 2008). Peak rate (Hz) vs [data- DV quarter, simulations- Response rate].
image p596fig16.29 Data (top row) and simulations (bottom row) showing decreasing frequency of subthreshold membrane potential oscillations along the DV axis of MEC.
|| Subthreshold membrane potential oscillations at different locations along DV axis of MEC (Giocomo etal 2020; Yoshida etal 2011). Data [oscillations (Hz) vs distance from dorsal surface (mm) @[-50, -45] mV, Frequency (Hz) vs [-58, -54, -50] mV]. Simulations MPO frequency (Hz) s [response, habituation] rate.
image p596fig16.30 Data (top row) and simulations (bottom row) of spatial phases of learned grid and place cells.
|| Spatial phases of learned grid and place cells (Hafting etal 2005). Data: Cross-correlogram of rate maps of two grid cells; Distribution of phase difference: distance from origin to nearest peak in cross-correlogram. Simulations: Grid cell histogram of spatial correlation coefficients; Place cell histogram of spatial correlation coefficients.
image p597fig16.31 Data (a) and simulations (b-d) about multimodal place cell receptive fields in large spaces. The simulations are the result of learned place fields.
|| Multimodal place cell firing in large spaces (Fenton etal 2008; Henriksen etal 2010; Park etal 2011). Number of cells (%) vs Number of place fields. [2, 3] place fields, 100*100 cm space.
image p597fig16.32 Data (top row) and simulations (bottom row) about grid cell development in juvenile rats. Grid score increases (a-b and d), whereas grid spacing remains fairly flat (c and e).
|| Model fits data about grid cell development (Wills etal 2010; Langston etal 2010). Data: [Gridness, grid score, inter-field distance (cm)]. Simulations: [Gridness score, Grid spacing (cm)] vs trial.
image p598fig16.33 Data (top row) and simulations (bottom row) of changes in place cell properties in juvenile rats, notably about spatial information (a,c) and inter-trial stability (b,d).
|| Model fits data about grid cell development (Wills etal 2010). [Data, Simulation] vs [spatial information, inter-trial stability]. x-axis [age (postnatal day), trial].
image p599fig16.35 Data (a) and simulations (b,c) about anatomically overlapping grid cell modules. (a) shows the anatomical distribution of grid cells belonging to different modules in one animal. DV location (mm) vs postrhinal border. (b) shows the simulated distribution of learned grid cell spacings from two stripe cell scales. frequency (%) vs grid spacing (cm). mu = [1, 0.6]. (c) shows what happens when half the cells respond with one rate and half another rate. (d) shows the same with three rates. (e-g) show spatial maps and autocorrelograms of grid cells that arise from the different rates in (d). [rate map, autocorelogram] vs [score [1.07, 0.5, 0.67], spacing (cm) [23.58, 41, 63.64]].
||
image p602fig16.37 Data showing the effect of hippocampal inactivation by muscimol on grid cell firing before, during, and six hours after the muscimol, reading from left to right.
|| Hippocampal inactivation disrupts grid cells (Bonnevie etal 2013). muscimole inactivation. spikes on trajectory: [before, after min [6-20, 20-40, 40-60, 6h]]. rate map (Hz) [18.6, 11.4, 9.5, 6.7, 10.8]. spatial autocorrelogram g=[1.12, 0.05, -0.34, 0.09, 1.27].
image p603fig16.38 Role of hippocampal feedback in maintaining grid fields. (a) Data showing the effect of hippocampal inactivation before and during muscimol inhibition of hippocampal cells, as in Figure 16.37. (b) Model simulation with normal grid fields. (c) Model simulation that emulates the effect of hippocampal inhibition on grid fields.
|| (a) Data: hippocampal inactivation [before, after] cart [spikes on trajectory (p: [18.6, 6.7] Hz), spatial autocorrelogram (g= [1.12, 0.09])]. (b) Model: noise-free path integration, [spikes on trajectory (p: 14.56 Hz), rate map, spatial autocorrelogram (g= 1.41), dynamic autocorrelogram (g=0.6)]. (c) Model: noisy path integration + non-specific tonic inhibition, [spikes on trajectory (p: 11.33 Hz), rate map, spatial autocorrelogram (g= 0.05), dynamic autocorrelogram (g=0.047)].
image p605fig16.39 Data showing effects of medial septum (MS) inactivation on grid cells and network theta oscillations in medial entorhinal cortex (MEC). (A) Examples of disruption in the spatial expression of the hexagonal grid structure for two grid cells (Brandon etal 2011). (B) Temporal reduction in the power and frequency of network theta oscillations (Koenig etal 2011). (C) Temporary reduction in the gridness score, mean firing rate, and patial stability of grid cells (Koenig etal 2011).
|| Disruptive effects of Medial Septum inactivation in Medial Entorhinal Cortex (Brandon etal 2011; Koenig etal 2011). (A) Rate map [rate map, spatial autocorrelations, trajectory] vs [baseline, sub-sampled, medial septum inactivation, 3-6 hour recovery, 24 hour recovery], [rate map (Hz- m, p), spatial autocorrelations (gridness)][ 1.2, 7.2, 1.1; 0.25, 1.7, 0.6; 0.25, 2.5, -0.53; 0.7, 5.1, 0.55; 1.0, 5.3, 1.3; 2.1, 15, 0.19; 1.7, 12, 0.71; 1.7, 3.2, -0.22; 1.8, 9.1, 0.68; 2.5, 13, 0.46]. (B) [normalized power at 7-9 Hz, frequency (Hz)] vs 5-minute periods. (C) [mean gridness score (+-SEM), mean firing rate (% of baseline), mean correlation coeff (+-SEM)] vs 10-minute periods.
image p607fig16.40 Effects of medial septum (MS) inactivation on grid cells. (a) Each row shows data and different data-derived measures of grid cell responsiveness, starting from the left with the baseline response to the middle column with maximal inhibition. (b) Data showing the temporary reduction in the gridness scores during MS inactivation, followed by recovery. (c) Simulation of the collapse in gridness, achieved by reduction in cell response rates to mimic reduced cholinergic transmission. (d,e) Simulations of the reduction in gridness scores in (d) by reduction of cell response rates, in (e) by changing the leak conductance. See the text for details.
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Grossberg 2021 p046c1h0.05 Of particular interest are data supporting ART's predicted link between synchronous oscillations and consciousness that neuroscientists such as ;
- (Victor Lamme 2006)
- (Rudolfo Llinas, Ribrary, Contreras, Pedroarena 1998)
- (Wolf Singer 1998)
- and their colleagues
have reported.
Grossberg 2021 p229c1h0.50 (Scott Brincat, Earl Miller 2015) reported neurophysiological data that support the ART distinction between category learning within the attentional system, that includes prefrontal cortex (PFC), and the orienting system, that includes the hippocampus (HPC). ... These results contribute to a long history of neurobiological experiments that have implicated the hippocampus in mismatch processing, notably the processing of novel events, including experiments and theoretical concepts about habituation of novelty in the hippocampus as learning proceeds. ... The ART hypothesis testing and category learning cycle hereby clarifies and unifies an important series of psychophysiological experiments that go back at least 50 years.
Grossberg 2021 p229c2h0.60 SMART computer simulations demonstrate that a good enough match of a top-down expectation with a bottom-up feature pattern generates an attentive resonance during which the spikes of active cells synchronize in the gamma frequency range of 20-70 Hz (Figure 5.40). Many labs have reported a link between attention and gamma oscillations in the brain, including two articles published in 2001, one from the laboratory of Robert Desimone when he was at the the National Institute of Mental Health in Bethseda (Fries, Reynolds, Rorie, Desimone 2001), and the other from the laboratory of Wolf Singer in Frankfurt (Engel, Fries, Singer 2001). You'll note that Pascal Fries participated in both studies, and is an acknowledged leader in neurobiological studies of gamma oscillations; eg (Fries 2009). .."
Grossberg 2021 p040c2h0.80 sub-section: Why did consciousness evolve? In particular, the book will show how and why multiple processing stages are needed before the brain can construct a complete and stable enough representation of the information in the world with which to control effective behaviours. This happens because the sensory signals that our brains process are often noisy and ambiguous, as are the representations that our brains form in response to them at the earliest processing stages. Complementary computing and hierarchical resolution of uncertainty overcome these problems until perceptual representations that are sufficiently complete, context-sensitive, and stable can be formed. The brain regions where these representations are completed are different for seeing, hearing, feeling, and knowing. But then how do our brains select these representations? My proposed answer is: A resonant state is generated that selectively "lights up" these representations and thereby renders them conscious. These conscious representations can then be used to trigger effective behaviours.

Consciousness hereby enables our brains to prevent the noisy and ambiguous information that is computed at earlier processing stages from triggering actions that could lead to disastrous consequences. Conscious states thus provide an extra degree of freedom whereby the brain ensures that its interactions with the environment, whether internal or external, are effective as possible, given the information at hand. These conclusions will be supported by theoretical analysis throughout the book that enable explanations of many psychological and neurobiological data about normal and clinical behaviours that have no other mechanistic explanations at the present time.
Grossberg 2021 p081-83 sub-section: How does one evolve a computational brain?
- Behavioral Success drives::>> Behavioral Data -> Art of Modelling -> Design Principles -> Mathematical Models -> Method of Minimal Anatomies (Occam's Razor) -> Behavioral Predictions -> explain Neural Data -> novel Brain Predictions <<::links behavior-mind-brain
- "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything.
- >characterize the boundary between the known and unknown: the shape of this boundary helps to direct attention to new design principles
(keys : behavior-mind-brain link)
Grossberg 2021 p081-83 The cycle of model evolution :
- The next step is to show how these new design principles can be incorporated into the evolved model in a self-consistent way, without undermining its previous mechanisms, thereby leading to a progressively more realistic model, one that can explain and predict ever more behavioral and neural data.
- In this way, the model undergoes a type of evolutionary development, as it becomes able to cope behaviorally with environmental constraints of ever increasing subtlety and complexity.
- The Method of Minimal Anatomies may hereby be viewed as way to functionally understand how increasingly demanding combinations of environmental pressures were incorporated into brains during the evolutionary process.
(keys : brain evolution, cycle of model evolution)
Grossberg 2021 p081c2h0.66 The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved
(keys : mind-body -classical- problem)
Grossberg 2021 p081c2h0.66 sub-section: How does one evolve a computational brain?
The above discussion illustrates that no single step of theoretical derivation can derive a whole brain. One needs a method for deriving a brain in stages, or cycles, much as evolution has incrementally discovered ever more complex brains over many thousands of years. The following theoretical method has been successfully applied many times since I first used it in 1957. It embodies a kind of conceptual evolutionary process for deriving a brain.

Because "brain evolution needs to achieve behavioural success", we need to start with data that embodiey indices of behavioral success. That is why, as illustrated in Figure 2.37 Modelling method and cycle, one starts with Behavioral Data from scores or hundreds of psychological experiments. These data are analyszed as the result of an individual adapting autonomously in real time to a changing world. This is the Arty of Modeling. It requires that one be able to infer from static data curves the dynamical processes that control individual behaviors occuring in real time. One of the hardest things that I teach to my students to do is "how to think in real time" to be able to carry out this speculative leap.

Properly carried out, this analysis leads to the discovery of new Design Principles that are embodied by these behavioral processes. The Design Principles highlight the functional meaning of the data, and clarify how individual behaviors occurring in real time give rise to these static data curves.

These principles are then converted into the simplest Mathematical Model using a method of minimal anatomies, which is a form of Occam's Razor, or principle of parsimony. Such a mathematical model embodies the psychological principles using the simplest possible differential equations. By "simplest" I mean that, if any part of the derived model is removed, then a significant fraction of the targeted data could no longer be explained. One then analyzes the model mathematically and simulates it on the computer, showing along the way how variations on the minimal anatomy can realize the design principles in different individuals or species.

This analysis has always provided functional explanations and Behavioral Predictions for much larger behavioral data bases than those used to discover the Design Principles. The most remarkable fact is, however, that the behaviorally derived model always looks like part of a brain, thereby explaining a body of challenging Neural Data and making novel Brain Predictions.

The derivation hereby links mind to brain via psychological organizational principles and their mechanistic realization as a mathematically defined neural network. This startling fact is what I first experienced as a college Freshman taking Introductory Psychology, and it changed my life forever.

I conclude from having had this experience scores of times since 1957 that brains look the way they do because they embody a natural computational realization for controlling autonomous adaptation in real-time to a changing world. Moreover, the Behavior -> Principles -> Model -> Neural derivation predicts new functional roles for both known and unknown brain mechanisms by linking the brain data to how it helps to ensure behavioral success. As I noted above, the power of this method is illustrated by the fact that scores of these predictions about brain and behavior have been supported by experimental data 5-30 years after they were first published.

Having made the link from behavior to brain, one can then "burn the candle from both ends" by pressing both top-down from Behavioral Data and bottom-up from Brain Data to clarify what the model can and cannot explain at its current stage of derivation. No model can explain everything. At each stage of development, the model can cope with certain environmental challenges but not others. An important part of the mathematical and computational analysis is to characterize the boundary between the known and unknown; that is which challenges the model can cope with and which it cannot. The shape of this boundary between the known and unknown helps to direct the theorist's attention to new design principles that have been omitted from previous analysis.

The next step is to show how these new design principles can be incorporated into the evolved model in a self-consistent way, without undermining its previous mechanisms, thereby leading to a progressively more realistic model, one that can explain and predict ever more behavioral and neural data. In this way, the model undergoes a type of evolutionary development, as it becomes able to cope behaviorally with environmental constraints of ever increasing subtlety and complexity. The Method of Minimal Anatomies may hereby be viewed as way to functionally understand how increasingly demanding combinations of environmental pressures were incorporated into brains during the evolutionary process.

If such an Embedding Principle cannot be carried out - that is, if the model cannot be unlumped or refined in a self-consistent way - then the previous model was, put simply, wrong, and one needs to figure out which parts must be discarded. Such a model is, as it were, an evolutionary dead end. Fortunately, this has not happened to me since I began my work in 1957 because the theoretical method is so conservative. No theoretical addition is made unless it is supported by multiple experiments that cannot be explained in its absence. Where multiple mechanistic instantiations of some Design Principles were possible, they were all developed in models to better underestand their explanatory implications. Not all of these instantiations could survive the pressure of the evolutionary method, but some always could. As a happy result, all earlier models have been capable of incremental refinement and expansion.

The cycle of model evolution has been carried out many times since 1957, leading today to increasing numbers of models that individually can explain and predict psychological, neurophysiological, anatomical, biophysical, and even biochemical data. In this specific sense, the classical mind-body problem is being incrementally solved.

Howell: bold added for emphasis.
(keys : Principles-Principia, behavior-mind-brain link, brain evolution, cycle of model evolution)
see also quotes: Charles William Lucas "Universal Force" and others (not retyped yet).
Grossberg 2021 p048c2h0.90 Perhaps the theory of the highly influential Portugese-American neuroscientist and author, Antonio Damasio, comes closest to theoretically linking brain to mind in his beautifully written 1999 book with the title The Feeling of What Happens: Body and Emotion in the making of consciousness (Damasio 1999). ... Damasio used the cliical data that he elegantly summarized in his book to guide him to what is, in effect, a heuristic derivation of the Cognitive-Emotional-Motor, or CogEM, model that I had first published in 1991. ... Unlike CogEM and its refinements, Damasio's theory provides no mechanistic account, and could therefore provide no data simulations or predictions based on the model's emergent properties. Nor could he situate his heuristic concepts within a larger theory of how brain resonances and conciousness may be linked.
Grossberg 2021 p204c2h0.30 In their 1990 article, Crick and Koch described two forms of consciousness : "a very fast form, linked to iconic memory...; and a slower one [wherein] an attentional mechanism transiently binds together all those neurons whose activity relates to the relevant features of a single visual object]". This conclusion was consistent with available results about ART at that time, but did not offer a linking hypothesis between brain dynamics and the perceptual, cognitive, and cognitive-emotional representations whose resonances support different conscious qualia. A great deal of additional experimental evidence for neural correlates of consciousness has been reported since 1990, but has typically led to theoretical conclusions that fail to make the crucial linking hypothesis between specific conscious pyschological qualia (eg Baars 2005; Dehaene 2014; Dennett 1991; Edelman, Tononi 2000; Koch etal 2016; Tononi 2004, 2015). ... Such claims are consistent with the analysis in this book, but they do not describe the underlying organizational principles, neural mechanisms, or brain representations that embody subjective conscious aspects of experience. window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);}

p190 Howell: [neural microcircuits, modal architectures] used in ART -

bottom-up filters	top-down expectations	purpose
instar learning	outstar learning	p200fig05.13 Expectations focus attention: [in, out]star often used to learn the adaptive weights. top-down expectations to select, amplify, and synchronize expected patterns of critical features, while suppressing unexpected features
LGN Lateral Geniculate Nucleus	V1 cortical area	p192fig05.06 focus attention on expected critical features
EC-III entorhinal stripe cells	CA1 hippocampal place cells	p600fig16.36 entorhinal-hippocampal system has properties of an ART spatial category learning system, with hippocampal place cells as the spatial categories
auditory orienting arousal	auditory category	p215fig05.28 How a mismatch between bottom-up and top-down patterns can trigger activation of the orienting system A and, with it, a burst of nonspecific arousal to the category level. ART Matching Rule: TD mismatch can suppress a part of F1 STM pattern, F2 is reset if degree of match < vigilance
auditory stream with/without [silence, noise] gaps	perceived sound continues?	p419fig12.17 The auditory continuity illusion: Backwards in time - How does a future sound let past sound continue through noise? Resonance!
visual perception, learning and recognition of visual object categories	motion perception, spatial representation and target tracking	p520fig14.02 pART predictive Adaptive Resonance Theory. Many adaptive synapses are bidirectional, thereby supporting synchronous resonant dynamics among multiple cortical regions. The output signals from the basal ganglia that regulate reinforcement learning and gating of multiple cortical areas are not shown. red - cognitive-emotional dynamics green - working memory dynamics black - see [bottom-up, top-down] lists
EEG with mismatch, arousal, and STM reset events	expected [P120, N200, P300] event-related potentials (ERPs)	p211fig05.21 Sequences of P120, N200, and P300 event-related potentials (ERPs) occur during oddball learning EEG experiments under conditions that ART predicted should occur during sequences of mismatch, arousal, and STM reset events, respectively.
Cognitive	Emotional	p541fig15.02 nSTART neurotrophic Spectrally Timed Adaptive Resonance Theory: Hippocampus can sustain a Cognitive-Emotional resonance: that can support "the feeling of what happens" and knowing what event caused that feeling. Hippocampus enables adaptively timed learning that can bridge a trace conditioning gap, or other temporal gap between Conditioned Stimuluus (CS) and Unconditioned Stimulus (US).

backgound colours in the table signify :

white	general microcircuit : a possible component of ART architecture
lime green	sensory perception [attention, expectation, learn]. Table includes [see, hear, !!must add touch example!!], no Grossberg [smell, taste] yet?
light blue	post-perceptual cognition?
pink	"the feeling of what happens" and knowing what event caused that feeling

[software, engineering, other] applications

grepStr = software|science|engineering|applications

p000XIIsec From brain science to mental disorders, irrational decisions, and the human condition
p027sec Affective neuroscience, mental disorders, and uppers that bring you down
Credibility from non-[bio, psycho]logical applications of Grossberg's ART
As stated in [Grossberg 2021 p13c1h1.0] : "... This range of applications is possible because ART models embody general-purpose properties that are needed to solve the stability-plasticity dilemma in many different types of environments. In all these applications, insights about cooperative-competitive dynamics also play a critical role. ..."
add content of subSection "Multiple applications of ART to large-scale problems in engineering and technology"
p084sec From brain theories to technological applications
p222sec Multiple applications of ART to large-scale problems in engineering and technology
image p085fig02.38 Our models have been used in many large-scale applications to engineering and technology. Linking brain to behavior explains how brain mechanisms give rise to psychological functions, and do so autonomously. The combination of mechanism, function, and autonomy helps to explain their value in helping to solve outstanding problems in technology.
|| Modeling method and cycle.
Behavioral data -(art of modeling)-> Design principles <- Neural data <-(brain predictions)- Mathematical model and analysis -(behavioral predictions)-> Behavioural data
Technology: Mathematical model and analysis <-> Technological applications
At every stage, spin off new model designs and mechanisms to technologist who need autonomous intelligent applications.
image p225fig05.33 Some early ARTMAP benchmark studies. These successes led to the use of ARTMAP, and many variants that we and other groups have developed, in many large-scale applications in engineering and technology that has not abated even today.
|| see Early ARTMAP benchmark studies
image p490fig13.15 The CogEM circuit is an ancient design that is found even in mollusks like Aplysia. See the text for details.
|| Aplysia (Buononamo, Baxter, Byrne, Neural Networks 1990; Grossberg, Behavioral and Brain Sciences 1983). Facilitator neuron ~ drive representation.
image p563fig15.33 The basal ganglia gate neural processing in many parts of the brain. The feedback loop through the lateral orbitofrontal cortex (blue arrow, lateral orbitofrontal) is the one that MOTIVATOR models.
|| MOTIVATOR models one of several thalamocortical loops through basal ganglia (Adapted from Fundamental Neuroscience. 2002 Copyright Elsevier). [cortex-> striatum-> pallidum S. nigra-> thalamus] vs [motor, oculomotor, dorsolateral prefrontal, lateral orbitofrontal, anterior cingulate]. thalamus-> [striatum, cortex].
image p563fig15.34 The colored regions are distinct parts of the basal ganglia in the loops depicted in Figure 15.33.
|| Distinct basal ganglia zones for each loop (Adapted from Fundamental Neuroscience. 2002 Copyright Elsevier).
Grossberg 2021 p067c1h0.60 Teuvo Kohonen at Helsinki University also made a transition from linear algebra concepts, such as the Moore-Penrose pseudo-inverse (eg Albert 1972), to more biologically motivated models that include nonlinear interactions. In the latter category, Kohonen used a simplified version of a Self-Organizing Map, or SOM, model in various applications (eg Kohonen 1984). ... The SOM had earlier been introduced and developed in the early 1970s by myself working in Cambridge and Boston, and Christof von der Malsburg, who was then at the Max-Planc Institute for Biophysical Chemistry in Gottingen(Grossberg 1972c, 1976a, 1976b; Malsburg 1973). I had even earlier introduced and mathematically analyszed the learning laws and competitive networks that go into the SOM model, starting in the 1960s (eg Grossberg 1968c, 1969c, 1971b, 1973, 1974).
Howell: Do you have to understand the subconscious processes in order to understand conscious processes? 1. Yourself - No. That's kind of a definition of consciousness or its effect. We do not understand the details of the subconscious, and don't know how it works, and that's fine. [Philosopy, classical science] are based on simple [rational, logical, scientific] reasoning, and they work OK. 2. Conscious systems construction: YES, you do have to undeerstand sub-conscious processes, and in order to do that you have to tie into them.

MTM	habituative transmitter gate	d[dt: yki(t)] = H(K - yki) - Lfk(xk)*yki
LTM	gated steepest descent learning	d[dt: zki(t)] = Mkfk(xk)(hi(xi) - zki)

V	Voltage
V(+), V(-), V(p)	Saturating voltages
g(+), g(-), g(p)	Conductances

How to compute the pattern-sensitive variable:	θi = Ii / sum[k=1 to n: Ik]?
Needs interactions! What type?	θi = Ii / sum[k ≠ i: Ik]

FIRST competitive stage	SECOND competitive stage
within orientation	across orientation
across position	within position

Boundary completion	Surface filling-in
outward	inward
oriented	unoriented
insensitive to direction of contrast	sensitive to direction-of-contrast

V1 layer	description
2/3A	complex cells
3B	binocular simple cells
4	monocular simple cells

VISUAL	seeing, knowing, and reaching
AUDITORY	hearing, knowing, and speaking
EMOTIONAL	feeling, knowing, and acting

B	excitable sites
xi(t)	excited sites (activity, potential)
B - xi(t)	unexcited sites

Ordering: Stimulus (S) probe location *	cells in V2 response?
...(S)*...	YES
...*...(S)	NO
(S)...*...	NO
(S)...*...(S)	YES
(S)...*... (more contrast)	NO
(S)...*.....(S)	YES

Thematic collections of Grossberg's [core, fun, strange] concepts

Table of Contents

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

Chapter 10 - Laminar computing by cerebral cortex

Chapter 10 - Laminar computing by cerebral cortex

computing with cellular patterns

family of ART.....base, linguistic

ART - Adaptive Resonance Theory

Credibility from non-[bio, psycho]logical applications of Grossberg's ART

LAMINART vison, speech, cognition

[, n]START learning & memory consolidation

ARTMAP learn maps of natural terrain

[, 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

conciousness

What is consciousness?

[Chapter, section]s of Stephen Grossberg 2021 "Conscious Mind, Resonant Brain"

Conscious mind, resonant brain: Table of Contents

Conscious mind, resonant brain: sub-section list

Chapter 6 - Conscious seeing and invariant recognition

[Figure, table]s of Stephen Grossberg 2021 "Conscious Mind, Resonant Brain"

Chapter 6 - Conscious seeing and invariant recognition

What is consciousness?

Grossberg's comments for some well-known consciousness theories

conscious vs non-conscious

behavior-mind-brain link

classical mind-brain problem

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

Chapter 12 - From seeing and reaching to hearing and speaking

Chapter 12 - From seeing and reaching to hearing and speaking

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

Credibility from non-[bio, psycho]logical applications of Grossberg's ART