020 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.
025 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
030 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. Thje 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!)
040 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]
100 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.
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200 p190sec Computational properties of the ART Matching Rule
205 p195sec Mathematical form of the ART Matching Rule
210 p206sec ART cycle of hypothesis testing and category learning
215 p227sec ART direct access solves the local minimum problem
220 p240sec Converting algebraic exemplar models into dynamical ART prototype models
230 p241sec Explaining human categorization data with ART: Learning rules-plus-exceptions
235 p246sec Self-normalizing inhibition during attentional priming with the ART Matching Rule
240 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?
300 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. ..."
300 p208sec ART links synchronous oscillations to attention and learning
305 p249sec Many kinds of psychological and neurobiological data have been explained by ART
310 p358sec Intracortical but interlaminar feedback also carries out the ART Matching Rule
315 p365sec ART Matching Rule in multiple cortical modalities
320 p600sec An ART spatial category learning system: The hippocampus IS a cognitive map!
325 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.
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330 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
335 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.
340 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 Matching Rule is restored.
|| Stabel and unstable learning, superset recoding
345 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
350 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.
355 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).
410 add content of subSection "Multiple applications of ART to large-scale problems in engineering and technology"
800 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)
900 p420sec SPINET and ARTSTREAM: Resonant dynamics doing auditory streaming
905 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.
