Neural Dynamics of the Basal Ganglia During Perceptual, Cognitive, and Motor Learning and Gating

Grossberg, Stephen

doi:10.1007/978-3-319-42743-0_19

Cited by 13 publications

(13 citation statements)

References 155 publications

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“…In addition to the fact that both spectral timing and spectral spacing in the entorhinal-hippocampal system seem to exploit a similar temporal rate gradient to develop time cells and grid cells, respectively, spectral timing seems to be a variant of a brain design for adaptively timed learning that is also found in the cerebellum and basal ganglia, where it carries out different behavioral functions (Fiala et al, 1996;Brown et al, 1999;Grossberg, 2016b). In all cases, it seems that the timed spectrum is set up by a calcium gradient that modulates the dynamics of metabotropic glutamate (mGluR) receptors (e.g., Finch and Augustine, 1998;Takechi et al, 1998;Ichise et al, 2000;Miyata et al, 2000).…”

Section: Art Matching Rule Dynamically Stabilizes Learning In the Entmentioning

confidence: 99%

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

Grossberg

2020

Front. Neuroinform.

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Section: Art Matching Rule Dynamically Stabilizes Learning In the Entmentioning

confidence: 99%

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

Grossberg

2020

Front. Neuroinform.

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“…Although ART predicts that “all conscious states are resonant states,” it does not predict that “all resonant states are conscious states.” Resonant states that are not accessible to consciousness, but that nonetheless dynamically stabilize learned memories, include parietal-prefrontal resonances that trigger the selective opening of basal ganglia gates to enable the readout of contextually appropriate thoughts and actions (Brown, Bullock, & Grossberg, 2004; Buschman & Miller, 2007; Grossberg, 2016b) and entorhinal-hippocampal resonances that dynamically stabilize the learning of entorhinal grid cells and hippocampal place cells during spatial navigation (Grossberg & Pilly, 2014; Kentros, Agniotri, Streater, Hawkins, & Kandel, 2004; Morris & Frey, 1997; Pilly & Grossberg, 2012). These resonances do not include feature detectors that are activated by external senses—such as those that support vision or audition—or internal senses—such as those that support emotion.…”

Section: All Conscious States Are Resonant States But Not Converselymentioning

confidence: 99%

The resonant brain: How attentive conscious seeing regulates action sequences that interact with attentive cognitive learning, recognition, and prediction

Grossberg

2019

Atten Percept Psychophys

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This article describes mechanistic links that exist in advanced brains between processes that regulate conscious attention, seeing, and knowing, and those that regulate looking and reaching. These mechanistic links arise from basic properties of brain design principles such as complementary computing, hierarchical resolution of uncertainty, and adaptive resonance. These principles require conscious states to mark perceptual and cognitive representations that are complete, context sensitive, and stable enough to control effective actions. Surface–shroud resonances support conscious seeing and action, whereas feature–category resonances support learning, recognition, and prediction of invariant object categories. Feedback interactions between cortical areas such as peristriate visual cortical areas V2, V3A, and V4, and the lateral intraparietal area (LIP) and inferior parietal sulcus (IPS) of the posterior parietal cortex (PPC) control sequences of saccadic eye movements that foveate salient features of attended objects and thereby drive invariant object category learning. Learned categories can, in turn, prime the objects and features that are attended and searched. These interactions coordinate processes of spatial and object attention, figure–ground separation, predictive remapping, invariant object category learning, and visual search. They create a foundation for learning to control motor-equivalent arm movement sequences, and for storing these sequences in cognitive working memories that can trigger the learning of cognitive plans with which to read out skilled movement sequences. Cognitive–emotional interactions that are regulated by reinforcement learning can then help to select the plans that control actions most likely to acquire valued goal objects in different situations. Many interdisciplinary psychological and neurobiological data about conscious and unconscious behaviors in normal individuals and clinical patients have been explained in terms of these concepts and mechanisms.

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“…In addition to the role of the substantia nigra pars compacta (SNc) in generating widespread dopaminergic Now Print signals to support new associative learning (see Section 2.2), the basal ganglia also control the opening and closing of gates in the substantia nigra pars reticulata (SNr) that enable cognitive and motor processes to be carried out [ Figure 2 ; see Grossberg (2016) for a review]. In particular, neural models have explained how opening an SNr basal ganglia gate can release an action whose properties are controlled by downstream circuits (e.g., Bullock and Grossberg, 1988 ; Brown et al, 1999 , 2004 ; Grossberg and Paine, 2000 ; Grossberg and Pearson, 2008 ; Silver et al, 2011 ).…”

Section: Several Causes Of Perseverative Behaviors During Normal Amentioning

confidence: 99%

Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning

Grossberg

Kishnan

2018

Front. Psychol.

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This article develops the iSTART neural model that proposes how specific imbalances in cognitive, emotional, timing, and motor processes that involve brain regions like prefrontal cortex, temporal cortex, amygdala, hypothalamus, hippocampus, and cerebellum may interact together to cause behavioral symptoms of autism. These imbalances include underaroused emotional depression in the amygdala/hypothalamus, learning of hyperspecific recognition categories that help to cause narrowly focused attention in temporal and prefrontal cortices, and breakdowns of adaptively timed motivated attention and motor circuits in the hippocampus and cerebellum. The article expands the model’s explanatory range by, first, explaining recent data about Fragile X syndrome (FXS), mGluR, and trace conditioning; and, second, by explaining distinct causes of stereotyped behaviors in individuals with autism. Some of these stereotyped behaviors, such as an insistence on sameness and circumscribed interests, may result from imbalances in the cognitive and emotional circuits that iSTART models. These behaviors may be ameliorated by operant conditioning methods. Other stereotyped behaviors, such as repetitive motor behaviors, may result from imbalances in how the direct and indirect pathways of the basal ganglia open or close movement gates, respectively. These repetitive behaviors may be ameliorated by drugs that augment D2 dopamine receptor responses or reduce D1 dopamine receptor responses. The article also notes the ubiquitous role of gating by basal ganglia loops in regulating all the functions that iSTART models.

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Neural Dynamics of the Basal Ganglia During Perceptual, Cognitive, and Motor Learning and Gating

Cited by 13 publications

References 155 publications

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

The resonant brain: How attentive conscious seeing regulates action sequences that interact with attentive cognitive learning, recognition, and prediction

Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning

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