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Habit learning in hierarchical cortex–basal ganglia loops
Author(s) -
Baladron Javier,
Hamker Fred H.
Publication year - 2020
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/ejn.14730
Subject(s) - basal ganglia , putamen , context (archaeology) , habit , key (lock) , set (abstract data type) , computer science , striatum , neuroscience , abstraction , cortex (anatomy) , selection (genetic algorithm) , artificial intelligence , process (computing) , psychology , cognitive science , machine learning , biology , social psychology , central nervous system , operating system , computer security , epistemology , dopamine , programming language , paleontology , philosophy
How do the multiple cortico‐basal ganglia‐thalamo‐cortical loops interact? Are they parallel and fully independent or controlled by an arbitrator, or are they hierarchically organized? We introduce here a set of four key concepts, integrated and evaluated by means of a neuro‐computational model, that bring together current ideas regarding cortex–basal ganglia interactions in the context of habit learning. According to key concept 1, each loop learns to select an intermediate objective at a different abstraction level, moving from goals in the ventral striatum to motor in the putamen. Key concept 2 proposes that the cortex integrates the basal ganglia selection with environmental information regarding the achieved objective. Key concept 3 claims shortcuts between loops, and key concept 4 predicts that loops compute their own prediction error signal for learning. Computational benefits of the key concepts are demonstrated. Contrasting with former concepts of habit learning, the loops collaborate to select goal‐directed actions while training slower shortcuts develops habitual responses.