Evidence map›Paper›PMID 39976331›Full record

ArticleeLife2025

Robust variability of grid cell properties within individual grid modules enhances encoding of local space.

William T Redman, Santiago Acosta-Mendoza, Xue-Xin Wei, Michael J Goard

Abstract read
In one paragraph

Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

William T Redman *Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, United States.ORCID https://orcid.org/0000-0002-4147-2026
Santiago Acosta-Mendoza *Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, United States.ORCID https://orcid.org/0009-0003-6698-476X
Xue-Xin WeiDepartment of Neuroscience, The University of Texas at Austin, Austin, United States.
Michael J GoardDepartment of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, United States.ORCID https://orcid.org/0000-0002-5366-8501

Funding

Cortical visual processing for navigationR01NS121919 · NINDS · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI GOARD, MICHAEL, NIELL, CRISTOPHER M · 2024 to 2025
$1.9M
National Science Foundation 1934288National Science Foundation 2318065NIH HHS R01 NS121919NINDS NIH HHS R01 NS121919Whitehall Foundation 2022-05-009
6 · The paper itself

Abstract

Although grid cells are one of the most well-studied functional classes of neurons in the mammalian brain, whether there is a single orientation and spacing value per grid module has not been carefully tested. We analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the encoding of local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that grid property variability of a similar magnitude to the analyzed data leads to significantly decreased decoding error. This holds even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

Indexed as

Entorhinal CortexGrid CellsSpace PerceptionAction PotentialsAnimalsModels, Neurologicalentorhinal cortexgrid cellsmouseneurosciencespatial coding

Identifiers

PMID39976331
PMCPMC11841986

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.