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A non-spatial account of place and grid cells based on clustering models of concept learning.

ABSTRACT: One view is that conceptual knowledge is organized using the circuitry in the medial temporal lobe (MTL) that supports spatial processing and navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so-called place and grid cell-like representations emerge because of the relatively uniform distribution of possible inputs in these tasks. The same mechanism applied to conceptual tasks, where the overall space can be higher-dimensional and sampling sparser, leading to representations more aligned with human conceptual knowledge. Although the types of memory supported by the MTL are superficially dissimilar, the information processing steps appear shared. Our account suggests that the MTL uses a general-purpose algorithm to learn and organize context-relevant information in a useful format, rather than relying on navigation-specific neural circuitry.

SUBMITTER: Mok RM

PROVIDER: S-EPMC6908717 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A non-spatial account of place and grid cells based on clustering models of concept learning.

Mok Robert M RM Love Bradley C BC

Nature communications 20191212 1

One view is that conceptual knowledge is organized using the circuitry in the medial temporal lobe (MTL) that supports spatial processing and navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so-called place and grid cell-like representations emerge because of the relatively uniform distribution of possible inputs in these tasks. The same mechani ...[more]

PMID: 31831749

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A non-spatial account of place and grid cells based on clustering models of concept learning.

Publications

A non-spatial account of place and grid cells based on clustering models of concept learning.

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