Learning Sparse Spatial Codes for Cognitive Mapping Inspired by Entorhinal-Hippocampal Neurocircuit

TL;DR

This paper presents a biologically inspired computational model of the entorhinal-hippocampal circuit that uses a locality-sensitive hashing algorithm to generate sparse spatial codes, enabling robust environmental mapping.

Contribution

It introduces a novel model linking grid cells to place cells via Hebbian learning, inspired by fruit fly olfactory circuits, and demonstrates its effectiveness in large-scale outdoor mapping.

Findings

Model builds coherent topological maps in outdoor environments

Sparse encoding helps distinguish different locations effectively

Biological circuit functions as a variant locality-sensitive hashing algorithm

Abstract

The entorhinal-hippocampal circuit plays a critical role in higher brain functions, especially spatial cognition. Grid cells in the medial entorhinal cortex (MEC) periodically fire with different grid spacing and orientation, which makes a contribution that place cells in the hippocampus can uniquely encode locations in an environment. But how sparse firing granule cells in the dentate gyrus are formed from grid cells in the MEC remains to be determined. Recently, the fruit fly olfactory circuit provides a variant algorithm (called locality-sensitive hashing) to solve this problem. To investigate how the sparse place firing generates in the dentate gyrus can help animals to break the perception ambiguity during environment exploration, we build a biologically relevant, computational model from grid cells to place cells. The weight from grid cells to dentate gyrus granule cells is learned by competitive Hebbian learning. We resorted to the robot system for demonstrating our cognitive mapping model on the KITTI odometry benchmark dataset. The experimental results show that our model is able to stably, robustly build a coherent semi-metric topological map in the large-scale outdoor environment. The experimental results suggest that the entorhinal-hippocampal circuit as a variant locality-sensitive hashing algorithm is capable of generating sparse encoding for easily distinguishing different locations in the environment. Our experiments also provide theoretical supports that this analogous hashing algorithm may be a general principle of computation in different brain regions and species.