Attractor neural networks storing multiple space representations: a model for hippocampal place fields

TL;DR

This paper analyzes a recurrent neural network model that stores multiple spatial maps, providing insights into hippocampal place cells and their capacity for representing space in rodents.

Contribution

It introduces a model for hippocampal place fields based on storing multiple spatial maps and analyzes its storage and information capacity.

Findings

Storage capacity depends on sparsity and connectivity.

Model links hippocampal function across species.

Quantitative predictions for spatial representation limits.

Abstract

A recurrent neural network model storing multiple spatial maps, or ``charts'', is analyzed. A network of this type has been suggested as a model for the origin of place cells in the hippocampus of rodents. The extremely diluted and fully connected limits are studied, and the storage capacity and the information capacity are found. The important parameters determining the performance of the network are the sparsity of the spatial representations and the degree of connectivity, as found already for the storage of individual memory patterns in the general theory of auto-associative networks. Such results suggest a quantitative parallel between theories of hippocampal function in different animal species, such as primates (episodic memory) and rodents (memory for space).