Adapting hippocampus multi-scale place field distributions in cluttered environments optimizes spatial navigation and learning.

ABSTRACT: Extensive studies in rodents show that place cells in the hippocampus have firing patterns that are highly correlated with the animal's location in the environment and are organized in layers of increasing field sizes or scales along its dorsoventral axis. In this study, we use a spatial cognition model to show that different field sizes could be exploited to adapt the place cell representation to different environments according to their size and complexity. Specifically, we provide an in-depth analysis of how to distribute place cell fields according to the obstacles in cluttered environments to optimize learning time and path optimality during goal-oriented spatial navigation tasks. The analysis uses a reinforcement learning (RL) model that assumes that place cells allow encoding the st

SUBMITTER: Scleidorovich P 

PROVIDER: S-EPMC9792172 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature