From grid cells to place cells: A gauss distribution activation function model - 文章详情页

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摘要：

It　has　been　found　that　in　biological　studies,　the　simple　linear　superposition　mathematical　model　cannot　be　used　to　express　the　feature　mapping　relationship　from　multiple　activated　grid　cells＇　grid　fields　to　a　single　place　cell＇s　place　field　output　in　the　hippocampus　of　the　cerebral　cortex　of　rodents.　To　solve　this　problem,　people　introduced　the　Gauss　distribution　activation　function　into　the　area.　We　in　this　paper　use　the　localization　properties　of　the　function　to　deal　with　the　linear　superposition　output　of　grid　cells＇　input　and　the　connection　weights　between　grid　cells　and　place　cells,　which　filters　out　the　low　activation　rate　place　fields.　We　then　obtained　a　single　place　cell　field　which　is　consistent　with　biological　studies.　Compared　to　the　existing　competitive　learning　algorithm　place　cell　model,　independent　component　analysis　method　place　cell　model,　Bayesian　positon　reconstruction　method　place　cell　model,　our　experimental　results　showed　that　the　model　on　the　neurophysiological　basis　can　not　only　express　the　feature　mapping　relationship　between　multiple　activated　grid　cells　grid　fields　and　a　single　place　cell＇s　place　field　output　in　the　hippocampus　of　the　cerebral　cortex　of　rodents,　but　also　make　the　algorithm　simpler,　the　required　grid　cells　input　less　and　the　accuracy　rate　of　the　output　of　a　single　place　field　higher.　©　2016,　Editorial　Office　of　Journal　of　Biomedical　Engineering.　All　right　reserved.

关键词：

Cytology Chemical activation Distribution functions Independent component analysis Gaussian distribution Mapping Mammals Cells

作者机构：

[ 1 ] [Yu, Naigong]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Yu, Naigong]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Fang, Lue]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Fang, Lue]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Luo, Ziwei]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Luo, Ziwei]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 7 ] [Yuan, Yunhe]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 8 ] [Yuan, Yunhe]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 9 ] [Jiang, Xiaojun]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 10 ] [Jiang, Xiaojun]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 11 ] [Cai, Jianxian]College of Electronic and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 12 ] [Cai, Jianxian]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China

通讯作者信息：

[fang, lue]beijing key laboratory of computational intelligence and intelligent system, beijing university of technology, beijing; 100124, china;;[fang, lue]college of electronic and control engineering, beijing university of technology, beijing; 100124, china

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