文献类型：期刊文献

英文题名：The place cell activity in three-dimensional space generated by multiple grid cell inputs

作者：Xu, Xuying[1];Wang, Yihong[1];Wang, Rubin[1,2]

机构：[1]East China Univ Sci & Technol, Sch Math, Shanghai, Peoples R China;[2]Hangzhou Dianzi Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China

年份：2022

卷号：108

期号：2

起止页码：1719

外文期刊名：NONLINEAR DYNAMICS

收录：;EI(收录号：20220711614691);WOS:【SCI-EXPANDED(收录号:WOS:000751201400003)】；

基金：This work is supported by the National Natural Science Foundation of China (Nos.12172132, 11802095, 11702096, 12072113) and the Natural Science Foundation of Shanghai (No.19zr1473100).

语种：英文

外文关键词：Cytology - Neurons - Cells - Encoding (symbols) - Gravitation

摘要：How the brain encodes spatial information is an important topic. Experimental and theoretical progresses achieved in this area mainly focused on the neuronal response in the lower-dimensional space such as a linear track or a horizontal flat arena. How the real three-dimensional (3-D) space is represented in the brain is unknown. Grid cells in the medial entorhinal cortex and the place cells in the hippocampus are the principal spatial neurons, and the grid cells provide important inputs to the place cells. In order to simulate the place cell activity in higher dimension, we proposed a rotating-integration model to generate the place field on non-horizontal surfaces for crawling animal in 3-D space. By referring to the gravity signal as an anchor, preferred directions of the grid cell will be rotated with the animal's body plane during navigating on the surfaces. Then, multiple grid cell patterns with distributed orientations and wavelengths are integrated to form the firing field(s) of a place cell. The results can not only account for the known experimental recordings but also predict a segment planar encoding property of place cell on novel complex surfaces. It suggests that the spatial cognition for crawling animal is achieved by a mosaic of lower-dimensional codes rather than the full volumetric perception. This work can help us understand how the spatial information provided by the external physical world is represented and processed by the neuronal systems.