Generalisation of structural knowledge in the hippocampal-entorhinal system

TL;DR

This paper explores how separating structural representations from entity representations in neural networks, inspired by the hippocampal-entorhinal system, enhances generalisation and spatial cognition, supported by biological evidence.

Contribution

It introduces a neural network model that learns structural statistics and generalises using hierarchical and Hebbian memory, inspired by neuroscience.

Findings

Emergence of spatial representations similar to brain cells

Unified basis functions for transition graphs

Preserved hippocampal-entorhinal relationships across environments

Abstract

A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities. We take inspiration from neuroscience, specifically the hippocampal-entorhinal system known to be important for generalisation. We propose that to generalise structural knowledge, the representations of the structure of the world, i.e. how entities in the world relate to each other, need to be separated from representations of the entities themselves. We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories and generalise structural knowledge. Spatial neuronal representations mirroring those found in the brain emerge, suggesting spatial cognition is an instance of more general organising principles. We further unify many entorhinal cell types as basis functions for constructing transition graphs, and show these representations effectively utilise memories. We experimentally support model assumptions, showing a preserved relationship between entorhinal grid and hippocampal place cells across environments.