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

By James C. R. Whittington, Timothy H. Muller, Shirley Mark, Guifen Chen, C. Barry, Neil Burgess, Timothy Edward John Behrens. 2019

604 citations recorded by the source corpus.

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 generalisation; 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 include grid, band, border and object-vector cells. Hippocampal cells include place and landmark cells, remapping between environments. Crucially, TEM also predicts empirically recorded representations in complex non-spatial tasks. TEM predicts hippocampal remapping is not random as previously believed. Rather structural knowledge is preserved across environments. We confirm this in simultaneously recorded place and grid cells. One Sentence Summary Simple principles of representation and generalisation unify spatial and non-spatial accounts of hippocampus and explain many cell representations.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “More recently, Whittington et al. (2020) proposed the Tolman Eichenbaum machine, where the HC network performs space and relational inference.”
Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input (2021, background): “Supporting this view, we showed that when the requirement to learn phonetic categories is abandoned,”
The geometry of abstraction in hippocampus and prefrontal cortex (2020, background): “The SD is higher in this interval than in the earlier time epoch: HPC 0.88, DLPFC 0.89, and ACC 0.88.”
If deep learning is the answer, what is the question? (2020, background): “For example, grid cell codes in the medial entorhinal cortex and elsewhere may be signatures of a neural code that has learned the geometry by which space itself is structured, potentially supporting transfer learning for navigation 111 .”
Learning Structures: Predictive Representations, Replay, and Generalization (2020, background): “Thus, prefrontal and entorhinal representations may compute and represent generalization and basis sets of structures, task sets, and schema [3,23,42].”