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

In probabilistic reversal learning, the choice option yielding reward at 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. In this study, we investigated how this reversal probability is represented in cortical neurons by analyzing the neural activity in prefrontal cortex of monkeys and recurrent neural networks trained on the task. We found that neural trajectories encoding reversal probability had substantial dynamics associated with intervening behaviors necessary to perform the task. Furthermore, the neural trajectories were translated systematically in response to whether outcomes were rewarded, and their position in the neural subspace captured information about reward outcomes. These findings suggested that separable dynamic trajectories, instead of fixed points on a line attractor, provided a better description of neural representation of reversal probability. Near the behavioral reversal, in particular, the trajectories shifted monotonically across trials with stable ordering, representing varying estimates of reversal probability around the reversal point. Perturbing the neural trajectory of trained networks biased when the reversal trial occurred, showing the role of reversal probability activity in decision-making. In sum, our study shows that cortical neurons encode reversal probability in a family of dynamic neural trajectories that accommodate flexible behavior while maintaining separability to represent distinct probabilistic values.