Reservoir computing model of prefrontal cortex creates novel combinations of previous navigation sequences from hippocampal place-cell replay with spatial reward propagation

by Nicolas Cazin, Martin Llofriu Alonso, Pablo Scleidorovich Chiodi, Tatiana Pelc, Bruce Harland, Alfredo Weitzenfeld, Jean-Marc Fellous, Peter Ford Dominey As rats learn to search for multiple sources of food or water in a complex environment, they generate increasingly efficient trajectories between reward sites. Such spatial navigation capacity involves the replay of hippocampal place-cells during awake states, generating small sequences of spatial ly related place-cell activity that we call “snippets”. These snippets occur primarily during sharp-wave-ripples (SWRs). Here we focus on the role of such replay events, as the animal is learning a traveling salesperson task (TSP) across multiple trials. We hypothesize that snippet replay genera tes synthetic data that can substantially expand and restructure the experience available and make 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 prefrontal cortex (PFC) in sequence learning, in which large pools of prewired neural elements process information dynamically through reverberations. This PFC model consolidat es snippets into larger spatial sequences that may be later recalled by subsets of the original sequences. Our simulation experiments provide...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research