Mesolimbic dopamine adapts the rate of learning from action.

ABSTRACT: Recent success in training artificial agents and robots derives from a combination of direct learning of behavioural policies and indirect learning through value functions^1-3. Policy learning and value learning use distinct algorithms that optimize behavioural performance and reward prediction, respectively. In animals, behavioural learning and the role of mesolimbic dopamine signalling have been extensively evaluated with respect to reward prediction⁴; however, so far there has been little consideration of how direct policy learning might inform our understanding⁵. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioural policies evolved as naive, head-restrained mice learned a trace conditioning paradigm. Individu

SUBMITTER: Coddington LT 

PROVIDER: S-EPMC9908546 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature