title: From reinforcement learning models to psychiatric and neurological disorders.
abstract: Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and cortico-basal ganglia-thalamo-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand important aspects of several psychiatric and neurological disorders that involve disturbances of the dopaminergic system and CBGTC circuits. We review this approach and its existing and potential applications to Parkinson's disease, Tourette's syndrome, attention-deficit/hyperactivity disorder, addiction, schizophrenia and preclinical animal models used to screen new antipsychotic drugs. The approach's proven explanatory and predictive power bodes well for the continued growth of computational psychiatry and computational neurology.
authors.affiliation: Department of Psychiatry, Columbia University, New York, New York, USA. tmaia@columbia.edu
authors.forename: Tiago V
authors.initials: TV
authors.lastname: Maia
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Frank
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009422:Nervous System Diseases / Q000503:physiopathology*; D009474:Neurons / Q000502:physiology*; D012054:Reinforcement, Psychology