Speed modulations in grid cell information geometry

Abstract Grid cells, with hexagonal spatial firing patterns, are thought critical to the brain’s spatial representation. High-speed movement challenges accurate localization as self-location constantly changes. Previous studies of speed modulation focus on individual grid cells, yet population-level...

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Main Authors: Zeyuan Ye, Ralf Wessel
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-025-62856-x
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author Zeyuan Ye
Ralf Wessel
author_facet Zeyuan Ye
Ralf Wessel
author_sort Zeyuan Ye
collection DOAJ
description Abstract Grid cells, with hexagonal spatial firing patterns, are thought critical to the brain’s spatial representation. High-speed movement challenges accurate localization as self-location constantly changes. Previous studies of speed modulation focus on individual grid cells, yet population-level noise covariance can significantly impact information coding. Here, we introduce a Gaussian Process with Kernel Regression (GKR) method to study neural population representation geometry. We show that increased running speed dilates the grid cell toroidal-like representational manifold and elevates noise strength, and together they yield higher Fisher information at faster speeds, suggesting improved spatial decoding accuracy. Moreover, we show that noise correlations impair information encoding by projecting excess noise onto the manifold. Overall, our results demonstrate that grid cell spatial coding improves with speed, and GKR provides an intuitive tool for characterizing neural population codes.
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spelling doaj-art-bd5da57aa8f545b78d80b790542d34182025-08-24T11:38:19ZengNature PortfolioNature Communications2041-17232025-08-0116111810.1038/s41467-025-62856-xSpeed modulations in grid cell information geometryZeyuan Ye0Ralf Wessel1Department of Physics, Washington University in St. LouisDepartment of Physics, Washington University in St. LouisAbstract Grid cells, with hexagonal spatial firing patterns, are thought critical to the brain’s spatial representation. High-speed movement challenges accurate localization as self-location constantly changes. Previous studies of speed modulation focus on individual grid cells, yet population-level noise covariance can significantly impact information coding. Here, we introduce a Gaussian Process with Kernel Regression (GKR) method to study neural population representation geometry. We show that increased running speed dilates the grid cell toroidal-like representational manifold and elevates noise strength, and together they yield higher Fisher information at faster speeds, suggesting improved spatial decoding accuracy. Moreover, we show that noise correlations impair information encoding by projecting excess noise onto the manifold. Overall, our results demonstrate that grid cell spatial coding improves with speed, and GKR provides an intuitive tool for characterizing neural population codes.https://doi.org/10.1038/s41467-025-62856-x
spellingShingle Zeyuan Ye
Ralf Wessel
Speed modulations in grid cell information geometry
Nature Communications
title Speed modulations in grid cell information geometry
title_full Speed modulations in grid cell information geometry
title_fullStr Speed modulations in grid cell information geometry
title_full_unstemmed Speed modulations in grid cell information geometry
title_short Speed modulations in grid cell information geometry
title_sort speed modulations in grid cell information geometry
url https://doi.org/10.1038/s41467-025-62856-x
work_keys_str_mv AT zeyuanye speedmodulationsingridcellinformationgeometry
AT ralfwessel speedmodulationsingridcellinformationgeometry