Author(s): Mallikarjun C Pujari, Vijay kumar SD

Abstract:
Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and corticoid-basal ganglia-thalami-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand key 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, Torte’s syndrome, attention-deficit/hyperactivity disorder, addiction, schizophrenia, and preclinical animal models used to screen novel antipsychotic drugs. The approach’s proven explanatory and predictive power bodes well for the continued growth of computational psychiatry and computational neurology.

Pages: 809-811  |  1003 Views  139 Downloads