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单个网格模块内网格细胞属性的强大变异性增强了局部空间的编码。

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

作者信息

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

机构信息

Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, United States.

Intelligent Systems Center, Johns Hopkins University Applied Physics Lab, Laurel, United States.

出版信息

Elife. 2025 Feb 20;13:RP100652. doi: 10.7554/eLife.100652.

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.

摘要

尽管网格细胞是哺乳动物大脑中研究最为深入的神经元功能类别之一,但每个网格模块是否存在单一的方向和间距值尚未得到仔细测试。我们分析了最近对内嗅皮层的大规模记录,以表征单个模块内网格属性的异质性存在情况和程度。我们发现了虽小但显著的变异性证据,并推测网格编码的这一特性可增强局部空间信息的编码。对网格细胞的合成群体进行分析时,我们能够完全控制网格属性的异质程度,结果表明,与分析数据相似程度的网格属性变异性会导致解码误差显著降低。即便仅限于单个模块的活动,情况依然如此。我们的研究结果凸显了神经反应属性的异质性如何有益于编码,并为网格细胞的理论和实验分析开辟了新方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87c1/11841986/e5090994d398/elife-100652-fig1.jpg

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