2022/09/18 by Yuanxiang Gao, Gao, Yuanxiang
Biochemistry, Genetics and Molecular Biology · Neuroscience · #Artificial Intelligence (cs.AI) #FOS: Biological sciences #FOS: Computer and information sciences #Memory and Neural Mechanisms #Neurons and Cognition (q-bio.NC) #Robotics (cs.RO) #Sleep and Wakefulness Research #Zebrafish Biomedical Research Applications
paper · pdf · doi:10.48550/arxiv.2209.08572
openalex publication_date 2022/09/18 · openalex created_date 2022/09/21 · openalex updated_date 2026/07/28
Recent experimental observations have shown that the reactivation of hippocampal place cells (PC) during sleep or immobility depicts trajectories that can go around barriers and can flexibly adapt to a changing maze layout. Such layout-conforming replay sheds a light on how the activity of place cells supports the learning of flexible navigation of an animal in a dynamically changing maze. However, existing computational models of replay fall short of generating layout-conforming replay, restricting their usage to simple environments, like linear tracks or open fields. In this paper, we propose a computational model that generates layout-conforming replay and explains how such replay drives the learning of flexible navigation in a maze. First, we propose a Hebbian-like rule to learn the inter-PC synaptic strength during exploring a maze. Then we use a continuous attractor network (CAN) with feedback inhibition to model the interaction among place cells and hippocampal interneurons. The activity bump of place cells drifts along a path in the maze, which models layout-conforming replay. During replay in rest, the synaptic strengths from place cells to striatal medium spiny neurons (MSN) are learned by a novel dopamine-modulated three-factor rule to store place-reward associations. During goal-directed navigation, the CAN periodically generates replay trajectories from the animal's location for path planning, and the trajectory leading to a maximal MSN activity is followed by the animal. We have implemented our model into a high-fidelity virtual rat in the MuJoCo physics simulator. Extensive experiments have demonstrated that its superior flexibility during navigation in a maze is due to a continuous re-learning of inter-PC and PC-MSN synaptic strength.