Journal
NATURE NEUROSCIENCE
Volume 14, Issue 2, Pages 154-162Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nn.2723
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Funding
- New York State Psychiatric Institute
- Research Foundation for Mental Hygiene
- National Institute of Mental Health [R01 MH080066]
- Michael J. Fox Foundation for Parkinson's Research
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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.
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