Before revision:  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.
After revision:  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 emerge, suggesting spatial cognition is an instance of more URLanising 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 grid and place cells across environments.