Topic: actor-critic reinforcement learning

Animals use knowledge of the environment to guide efficient foraging decisions. It is unclear how this knowledge is accumulated or maintained by recurrently connected circuits in the brain. In order to guide the study of how structures of knowledge are accumulated and maintained to support optimal foraging decisions, we reverse-engineered recurrent neural networks (RNNs) trained to perform dynamic foraging tasks. We highlight two variants of the task --- a highly structured block-switch variant and a weakly structured random walk variant --- and investigate how network dynamics are shaped by