Why grid cells function as a metric for space
Why grid cells function as a metric for space
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DOI：
10.1016/j.neunet.2021.04.031
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发表时间：
2021-05-14
期刊：
影响因子：
7.8
通讯作者：
Tang, Huajin
中科院分区：
文献类型：
--
作者：
Dang, Suogui;Wu, Yining;Tang, Huajin
大脑能够根据网格单元计算到所需位置的距离和方向。广泛的神经生理学研究啮齿动物导航假设网格细胞的功能作为一个度量的空间，并激发了许多计算研究开发创新的导航方法。此外，网格单元可以为高阶非空间信息提供通用编码方案。基于现有的神经科学和机器学习工作，本文提供了理论上的清晰性，即网格细胞群代码可以作为空间的度量。该度量由核距离方法生成的平移不变正定核等距嵌入到欧氏空间中，网格细胞种群编码的内积指数收敛于核.我们还提供了一种方法来学习网格细胞群体的分布有效。网格单元作为一种可扩展的位置编码方法，可以对地点的空间关系进行编码，使网格单元在导航中的表现优于地点单元。此外，我们将网格单元扩展到图像编码，发现网格单元将图像嵌入到心理地图中，其中几何关系是图像的概念关系。理论模型和分析将有助于建立一个通用的空间和概念空间的编码方案的网格单元代码，并有希望在空间认知，机器学习和语义认知的众多问题。(C)2021由Elsevier Ltd.出版
The brain is able to calculate the distance and direction to the desired position based on grid cells. Extensive neurophysiological studies of rodent navigation have postulated the grid cells function as a metric for space, and have inspired many computational studies to develop innovative navigation approaches. Furthermore, grid cells may provide a general encoding scheme for high-order nonspatial information. Built upon existing neuroscience and machine learning work, this paper provides theoretical clarity on that the grid cell population codes can be taken as a metric for space. The metric is generated by a shift-invariant positive definite kernel via kernel distance method and embeds isometrically in a Euclidean space, and the inner product of the grid cell population code exponentially converges to the kernel. We also provide a method to learn the distribution of grid cell population efficiently. Grid cells, as a scalable position encoding method, can encode the spatial relationships of places and enable grid cells to outperform place cells in navigation. Further, we extend the grid cell to images encoding and find that grid cells embed images into a mental map, where geometric relationships are conceptual relationships of images. The theoretical model and analysis would contribute to establishing the grid cell code as a generic coding scheme for both spatial and conceptual spaces, and is promising for a multitude of problems across spatial cognition, machine learning and semantic cognition. (C) 2021 Published by Elsevier Ltd.