The hippocampus as a “Tolman-Eichenbaum Machine” https://pubmed.ncbi.nlm.nih.gov/33181068/

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https://pubmed.ncbi.nlm.nih.gov/33181068/

Cell. 2020 Nov 5;S0092-8674(20)31388-X. doi: 10.1016/j.cell.2020.10.024. Online ahead of print.

The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation

James C R Whittington ¹, Timothy H Muller ², Shirley Mark ³, Guifen Chen ⁴, Caswell Barry ⁵, Neil Burgess ⁶, Timothy E J Behrens ⁷Affiliations expand

• PMID: 33181068
• DOI: 10.1016/j.cell.2020.10.024

Abstract

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains, provide a mechanistic understanding of the hippocampal role in generalization, and offer unifying principles underlying many entorhinal and hippocampal cell types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells display diverse properties resembling apparently bespoke spatial responses, such as grid, band, border, and object-vector cells. TEM hippocampal cells include place and landmark cells that remap between environments. Crucially, TEM also aligns with empirically recorded representations in complex non-spatial tasks. TEM also generates predictions that hippocampal remapping is not random as previously believed; rather, structural knowledge is preserved across environments. We confirm this structural transfer over remapping in simultaneously recorded place and grid cells.

Keywords: entorhinal cortex; generalization; grid cells; hippocampus; neural networks; non-spatial reasoning; place cells; representation learning.