Abstract
SummaryThe 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.
Highlights
Humans and other animals make complex inferences from sparse observations and rapidly integrate new knowledge to control their behavior. Tolman (1948) argued that these facilities rely on a systematic organization of knowledge called a cognitive map
One promising approach casts spatial and non-spatial problems as a connected graph, with neural responses as efficient representations of this graph (Gustafson and Daw, 2011; Stachenfeld et al, 2017). This has led to new potential interpretations for place cells (Stachenfeld et al, 2017) and grid cells (Stachenfeld et al, 2017; Dordek et al, 2016). Such approaches cannot account for the rapid inferences and generalizations characteristic of hippocampal function in both spatial and relational memory and do not explain the myriad types of spatial representations observed or predict how they will change across different environments
Manns and Eichenbaum (2006) propose that novel conjunctions of these two representations form the hippocampal representation for relational memory. We demonstrate that this factorization and conjunction approach is sufficient to build a relational memory system that generalizes structural knowledge in space and non-space, predicts a broad range of neuronal representations observed in spatial and relational memory tasks, and accounts for observed remapping phenomena in both the hippocampus and entorhinal cortex
Summary
Humans and other animals make complex inferences from sparse observations and rapidly integrate new knowledge to control their behavior. Tolman (1948) argued that these facilities rely on a systematic organization of knowledge called a cognitive map. One promising approach casts spatial and non-spatial problems as a connected graph, with neural responses as efficient representations of this graph (Gustafson and Daw, 2011; Stachenfeld et al, 2017) This has led to new potential interpretations for place cells (Stachenfeld et al, 2017) and grid cells (Stachenfeld et al, 2017; Dordek et al, 2016). Such approaches cannot account for the rapid inferences and generalizations characteristic of hippocampal function in both spatial and relational memory and do not explain the myriad types of spatial representations observed or predict how they will change across different environments (remapping)
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