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网格细胞沿着几何边界扭曲。

Grid-Cell Distortion along Geometric Borders.

机构信息

Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, MTFS, Olav Kyrres gate 9, NO-7489 Trondheim, Norway.

Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, MTFS, Olav Kyrres gate 9, NO-7489 Trondheim, Norway.

出版信息

Curr Biol. 2019 Mar 18;29(6):1047-1054.e3. doi: 10.1016/j.cub.2019.01.074. Epub 2019 Mar 7.

Abstract

Grid cells fire in a triangular pattern that tessellates the environment [1]. The pattern displays a global distortion that is well described by a shearing transformation of an idealized grid [2]. However, in addition, distortions often differ across parts of the environment, suggesting that the grid interacts with the environment locally [2-5]. How this occurs is poorly understood. To further determine the nature of local distortions, we therefore analyzed the local spatial characteristics of the grid pattern. When rats ran in a large square enclosure, the grid pattern displayed several stereotypical distortions in relation to features of the environment. These distortions were stronger at edges than on open surfaces. Curved axis orientations and distortions of the grid pattern in the corners could be explained by a geometrical model where the pattern, in conjunction with being sheared, is compressed along the walls of the enclosure. The grid compression coincided with stereotypical running behavior where the animals moved faster in the areas where the grid had the most pronounced distortions. However, neither running direction nor speed influenced the distortions on a moment-to-moment basis, raising the possibility that the distortions are a learned feature.

摘要

网格细胞以三角形模式发射,这种模式在环境中进行平铺[1]。该模式显示出全局扭曲,这可以通过对理想化网格的剪切变换来很好地描述[2]。然而,此外,扭曲通常在环境的不同部分之间存在差异,这表明网格在局部与环境相互作用[2-5]。这种情况是如何发生的还不太清楚。为了进一步确定局部扭曲的性质,我们因此分析了网格模式的局部空间特征。当老鼠在一个大的正方形围栏中奔跑时,网格模式相对于环境特征显示出几种典型的扭曲。这些扭曲在边缘处比在开阔表面处更强。弯曲的轴取向和网格模式在角落处的扭曲可以用一个几何模型来解释,在这个模型中,图案与剪切相结合,沿围栏的墙壁被压缩。网格压缩与典型的跑步行为相吻合,即动物在网格扭曲最明显的区域移动得更快。然而,无论是跑步方向还是速度,都没有在瞬间影响到扭曲,这增加了这种扭曲是一种习得特征的可能性。

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