title: Multiple representations and algorithms for reinforcement learning in the cortico-basal ganglia circuit.
abstract: Accumulating evidence shows that the neural network of the cerebral cortex and the basal ganglia is critically involved in reinforcement learning. Recent studies found functional heterogeneity within the cortico-basal ganglia circuit, especially in its ventromedial to dorsolateral axis. Here we review computational issues in reinforcement learning and propose a working hypothesis on how multiple reinforcement learning algorithms are implemented in the cortico-basal ganglia circuit using different representations of states, values, and actions.
authors.affiliation: Neural Computation Unit, Okinawa Institute of Science and Technology, Okinawa 904-0412, Japan.
authors.forename: Makoto
authors.initials: M
authors.lastname: Ito
authors.forename: Kenji
authors.initials: K
authors.lastname: Doya
mesh_terms: D000465:Algorithms; D000818:Animals; D001479:Basal Ganglia / Q000502:physiology*; D002540:Cerebral Cortex / Q000502:physiology*; D006801:Humans; D009434:Neural Pathways / Q000502:physiology; D012054:Reinforcement, Psychology