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

TLDR

The hippocampal‑entorhinal system supports spatial and relational memory. The authors introduce the Tolman‑Eichenbaum machine to unify spatial and relational memory through generalization. The model posits medial entorhinal cells encode structural knowledge and hippocampal cells bind this basis to sensory inputs. TEM reproduces diverse entorhinal cell types, generates remapping place and landmark cells, aligns with non‑spatial task representations, predicts that remapping preserves structural knowledge, and this prediction is confirmed by simultaneous recordings.

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.