A Biologically Inspired Computational Model of Basal Ganglia in Action Selection

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2015-01, Vol.2015 (2015), p.148-171
Main Authors: Baston, Chiara, Ursino, Mauro
Format: Article
Language:English
Subjects:
Acetylcholine - physiology
Algorithms
Balancing
Basal ganglia
Basal Ganglia - physiology
Behavior
Cognition & reasoning
Cognition - physiology
Computation
Computer Simulation
Conflict (Psychology)
Corpus Striatum - physiology
Dipping
Dopamine
Dopamine - physiology
Ganglia
Humans
Interneurons - physiology
Learning
Machine Learning
Models, Neurological
Neural circuitry
Neural Networks (Computer)
Neurotransmitter Agents - physiology
Parkinson's disease
Pathways
Physiological aspects
Physiology
Reward
Synapses
Synapses - physiology
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Summary:The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.
ISSN:1687-5265
1687-5273
DOI:10.1155/2015/187417