Generalisation of structural knowledge in the Hippocampal-Entorhinal system.

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 (containing place and grid cells), 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, generalise structural knowledge, and also exhibit neuronal representations mirroring those found in the brain. We experimentally support model assumptions, showing a preserved relationship between grid and place cells across environments.