Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks.

ABSTRACT: In probabilistic reversal learning, the choice option yielding reward with higher probability switches at a random trial. To perform optimally in this task, one has to accumulate evidence across trials to infer the probability that a reversal has occurred. We investigated how this reversal probability is represented in cortical neurons by analyzing the neural activity in the prefrontal cortex of monkeys and recurrent neural networks trained on the task. We found that in a neural subspace encoding reversal probability, its activity represented integration of reward outcomes as in a line attractor model. The reversal probability activity at the start of a trial was stationary, stable, and consistent with the attractor dynamics. However, during the trial, the activity was associated with task

SUBMITTER: Kim CM 

PROVIDER: S-EPMC12456948 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature