Prefrontal cortex creates novel navigation sequences from hippocampal place-cell replay with spatial reward propagation

As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites, across multiple trials. This optimization capacity has been characterized in the Traveling Salesrat Problem (TSP) (de Jong et al (2011). Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatially related place-cell activity that we call “snippets”. These snippets occur primarily during sharp-wave-ripple (SWR) events. Here we focus on the role of replay during the awake state, as the animal is learning across multiple trials. We hypothesize that snippet replay generates synthetic data that can substantially expand and restructure the experience available to make PFC learning more optimal. We developed a model of snippet generation that is modulated by reward, propagated in the forward and reverse directions. This implements a form of spatial credit assignment for reinforcement learning. We use a biologically motivated computational framework known as ‘reservoir computing’ to model PFC in sequence learning, in which large pools of prewired neural elements process information dynamically through reverberations. This PFC model is ideal to consolidate snippets into larger spatial sequences that may be later recalled by subsets of the original sequences. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to “learn” trajectories that is has not physically experienced, both of which significantly contribute to the TSP behavior. Author Summary As rats search for multiple sources of food in a complex environment, they generate increasingly efficient trajectories between reward sites, across multiple trials, characterized in the Traveling Salesrat Problem (TSP). This likely involves the coordinated replay of place-cell “snippets” between successive trials. We hypothesize that “snippets” can be used by the prefrontal cortex (PFC) to implement a form of reward-modulated reinforcement learning. Our simulation experiments provide neurophysiological explanations for two pertinent observations related to navigation. Reward modulation allows the system to reject non-optimal segments of experienced trajectories, and reverse replay allows the system to “learn” trajectories that it has not physically experienced, both of which significantly contribute to the TSP behavior.