文献类型：期刊文献

英文题名：An Energy Model of Place Cell Network in Three Dimensional Space

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

机构：[1]East China Univ Sci & Technol, Sch Sci, Shanghai, Peoples R China

年份：2018

卷号：12

外文期刊名：FRONTIERS IN NEUROSCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000430843900001)】；

基金：This work is supported by the National Natural Science Foundation of China (Nos. 11702096, 11232005& 11472104) and the Fundamental Research Funds for the Central Universities of China (Nos. 222201814025&222201714020).

语种：英文

外文关键词：place cells; three dimensional space; energy coding; spatial selectivity; locating system

摘要：Place cells are important elements in the spatial representation system of the brain. A considerable amount of experimental data and classical models are achieved in this area. However, an important question has not been addressed, which is how the three dimensional space is represented by the place cells. This question is preliminarily surveyed by energy coding method in this research. Energy coding method argues that neural information can be expressed by neural energy and it is convenient to model and compute for neural systems due to the global and linearly addable properties of neural energy. Nevertheless, the models of functional neural networks based on energy coding method have not been established. In this work, we construct a place cell network model to represent three dimensional space on an energy level. Then we define the place field and place field center and test the locating performance in three dimensional space. The results imply that the model successfully simulates the basic properties of place cells. The individual place cell obtains unique spatial selectivity. The place fields in three dimensional space vary in size and energy consumption. Furthermore, the locating error is limited to a certain level and the simulated place field agrees to the experimental results. In conclusion, this is an effective model to represent three dimensional space by energy method. The research verifies the energy efficiency principle of the brain during the neural coding for three dimensional spatial information. It is the first step to complete the three dimensional spatial representing system of the brain, and helps us further understand how the energy efficiency principle directs the locating, navigating, and path planning function of the brain.