文献类型：会议论文

英文题名：Non-negative approximation with thresholding for cortical visual representation

作者：Liu, Jiqian Song, Chunli Zeng, Chengbin

第一作者：刘继乾

通信作者：Liu, Jiqian|[144401d7d7e0d92bf7d29]刘继乾;

机构：[1] School of Information Engineering, Guizhou Institute of Technology, 1st, Caiguan Road, Yunyan District, Guiyang, 550003, China

第一机构：贵州理工学院电气与信息工程学院

通信机构：|贵州理工学院大数据学院

会议论文集：Intelligent Computing Theories and Methodologies - 11th International Conference, ICIC 2015, Proceedings

会议日期：August 20, 2015 - August 23, 2015

会议地点：Fuzhou, China

语种：英文

外文关键词：Computation theory - Intelligent computing

年份：2015

摘要：This paper presents a neurally plausible algorithm for the representation of visual inputs by cortical neurons. It has been demonstrated in previous theoretical studies that the main goal of the encoding of the input from lateral geniculate nucleus (LGN) by simple cell is to minimize the representation error. Based on the existing methods, we propose a non-negative approximation algorithm using thresholding. We validate the algorithm via simulation of several known response properties of simple cells, including the sharp and contrast invariant orientation tuning and surround suppression, and as cross orientation suppression. ? Springer International Publishing Switzerland 2015.