Abstract
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.
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Srinivasa Chakravarthy, V., Moustafa, A.A. (2018). Introduction. In: Computational Neuroscience Models of the Basal Ganglia. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-8494-2_1
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DOI: https://doi.org/10.1007/978-981-10-8494-2_1
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