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Cited by 585

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Differential influences of environment and self-motion on place and grid cell firing

Open access
Chen, Guifen, Lu, Yi, King, John A, Cacucci, Francesca, Burgess, Neil·2019·Nature Communications

Abstract

Place and grid cells in the hippocampal formation provide foundational representations of environmental location, and potentially of locations within conceptual spaces. Some accounts predict that environmental sensory information and self-motion are encoded in complementary representations, while other models suggest that both features combine to produce a single coherent representation. Here, we use virtual reality to dissociate visual environmental from physical motion inputs, while recording place and grid cells in mice navigating virtual open arenas. Place cell firing patterns predominantly reflect visual inputs, while grid cell activity reflects a greater influence of physical motion. Thus, even when recorded simultaneously, place and grid cell firing patterns differentially reflect environmental information (or ‘states’) and physical self-motion (or ‘transitions’), and need not be mutually coherent.

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Metadata

Authors
Chen, G., Lu, Y., King, J. A., Cacucci, F., & Burgess, N.
Venue
Nature Communications
Published
2019-02-07
Volume
10
Issue
1
Pages
630-630
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2911805374
Cited by
585
References
41
FWCI
75.71
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Differential influences of environment and self-motion on place and grid cell firing. https://app.cochat.ai/papers/10.1038/s41467-019-08550-1
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Chen, G., Lu, Y., King, J. A., Cacucci, F., & Burgess, N. (2019). Differential influences of environment and self-motion on place and grid cell firing. Nature Communications. https://doi.org/10.1038/s41467-019-08550-1

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.