V. Srinivasa Chakravarthy*, Ahmed A. Moustafa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingForeword/postscriptResearchpeer-review


The area of computational modeling of basal ganglia has seen an explosive growth in the last couple of decades. In this area, there is currently a multitude of modeling approaches, each approaching the functions of basal ganglia in a unique fashion, pursuing a specialized line of investigation. Existing models fall under certain prominent schools of thought, each successfully explaining a subset of basal ganglia functions that are amenable to that specific approach, while ignoring a host of other functions. The aim of this book is to describe a class of the basal ganglia models that comprehensively accommodates a wide range of the basal ganglia functions within a single modeling framework. This class of models is essentially based on reinforcement learning, a currently dominant paradigm for describing the basal ganglia function. However, the class of computational models described herein deviate significantly from some of the classical approaches like, for example, the Go-NoGo interpretation of the functional pathways of the basal ganglia. This class of models successfully explains a wide variety of motor functions, and some cognitive functions of the basal ganglia, in healthy and pathological conditions like the Parkinson’s disease and other disorders associated with the basal ganglia.

Original languageEnglish
Title of host publicationComputational Neuroscience Models of the Basal Ganglia
EditorsV. Srinivasa Chakravarthy, Ahmed A. Moustafa
Number of pages4
ISBN (Electronic)978-981-10-8494-2
ISBN (Print)978-981-13-4168-7, 978-981-10-8493-5
Publication statusPublished - 2018
Externally publishedYes

Publication series

NameCognitive Science and Technology
ISSN (Print)2195-3988


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