Ye Zeyuan, Wessel Ralf
Department of Physics, Washington University in St. Louis, St. Louis, MO, USA.
Nat Commun. 2025 Aug 19;16(1):7723. doi: 10.1038/s41467-025-62856-x.
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.
具有六边形空间放电模式的网格细胞被认为对大脑的空间表征至关重要。高速运动会对精确的定位提出挑战，因为自身位置在不断变化。以往关于速度调制的研究主要集中在单个网格细胞上，但群体水平的噪声协方差会显著影响信息编码。在这里，我们引入了一种带核回归的高斯过程（GKR）方法来研究神经群体表征几何。我们发现，跑步速度的增加会使网格细胞的环形表征流形扩张，并提高噪声强度，二者共同作用使得在更快速度下产生更高的费舍尔信息，这表明空间解码精度得到了提高。此外，我们还表明，噪声相关性通过将多余噪声投射到流形上而损害信息编码。总体而言，我们的结果表明网格细胞的空间编码随速度提高，并且GKR为表征神经群体编码提供了一个直观的工具。
