Hippocampal Spatial Mapping As Fast Graph Learning

TL;DR

This paper proposes a novel graph-based model of hippocampal spatial mapping, enabling rapid learning of environment parts and their relations, which improves efficiency over traditional lookup table approaches.

Contribution

It introduces a fast relation graph learning algorithm that models hippocampal spatial maps as graphs of environment parts with associated features and relations.

Findings

Efficiently learns spatial maps using graph structures.

Associates hippocampal engram cells with environment features.

Can be extended to non-spatial relational tasks.

Abstract

The hippocampal formation is thought to learn spatial maps of environments, and in many models this learning process consists of forming a sensory association for each location in the environment. This is inefficient, akin to learning a large lookup table for each environment. Spatial maps can be learned much more efficiently if the maps instead consist of arrangements of sparse environment parts. In this work, I approach spatial mapping as a problem of learning graphs of environment parts. Each node in the learned graph, represented by hippocampal engram cells, is associated with feature information in lateral entorhinal cortex (LEC) and location information in medial entorhinal cortex (MEC) using empirically observed neuron types. Each edge in the graph represents the relation between two parts, and it is associated with coarse displacement information. This core idea of associating arbitrary information with nodes and edges is not inherently spatial, so this proposed fast-relation-graph-learning algorithm can expand to incorporate many spatial and non-spatial tasks.