Authors: Ching Fang, Dmitriy Aronov, Larry Abbott, Emily L Mackevicius

Abstract: The predictive nature of the hippocampus is thought to support many cognitive behaviors, from memory to inferential reasoning. Inspired by the reinforcement learning literature, this notion has been formalized by describing the hippocampus as a predictive map called the successor representation (SR). The SR captures a number of observations about hippocampal activity. However, the algorithm does not provide a neural mechanism for how such representations arise. Here, we show the dynamics of a recurrent neural network naturally calculate the SR when the synaptic weights match the transition probability matrix. Interestingly, the predictive horizon can be flexibly modulated simply by changing the network gain. We derive simple, biologically plausible learning rules to learn the SR in a recurrent network. We show our model matches electrophysiological data. Taken together, our results suggest that predictive maps of the world are accessible in biological circuits and can support a broad range of cognitive functions.