A neural searchlight processor that differentiates any images with common features by transitory synchronization

K. Murase, Y. Nakade, Y. Matsunaga, O. Yamakawa
{"title":"A neural searchlight processor that differentiates any images with common features by transitory synchronization","authors":"K. Murase, Y. Nakade, Y. Matsunaga, O. Yamakawa","doi":"10.1109/IJCNN.1991.170706","DOIUrl":null,"url":null,"abstract":"The neural cocktail-party processor (NCPP) is known as a theoretical model of the visual binding by coherent oscillation of neurons, a hypothesis that transitory synchronization of neuronal activities might link fragmentarily represented visual information in the widely spaced areas of the brain to establish coherent images. However, NCPP was made under an assumption that the images to be recognized have no common features. If there are any common features the synchronization among cells is disturbed and the network cannot recognize the images correctly. The authors therefore developed a network, called the neural searchlight processor (NSP), that recognizes images by transitory synchronization allowing common features between images in the input pattern. The mechanism and results of computer simulation of NCPP are described. Then the structure and simulation of NSP are explained by comparison with NCPP.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170706","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The neural cocktail-party processor (NCPP) is known as a theoretical model of the visual binding by coherent oscillation of neurons, a hypothesis that transitory synchronization of neuronal activities might link fragmentarily represented visual information in the widely spaced areas of the brain to establish coherent images. However, NCPP was made under an assumption that the images to be recognized have no common features. If there are any common features the synchronization among cells is disturbed and the network cannot recognize the images correctly. The authors therefore developed a network, called the neural searchlight processor (NSP), that recognizes images by transitory synchronization allowing common features between images in the input pattern. The mechanism and results of computer simulation of NCPP are described. Then the structure and simulation of NSP are explained by comparison with NCPP.<>
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种神经探照灯处理器,通过瞬时同步区分具有共同特征的图像
神经鸡尾酒会处理器(neural cocktail-party processor, NCPP)被认为是神经元相干振荡视觉结合的理论模型,这是一种假设,认为神经元活动的短暂同步可能会将大脑中间隔广泛的区域中零散的视觉信息联系起来,从而建立连贯的图像。然而,NCPP是在待识别图像没有共同特征的假设下进行的。如果存在共同特征,则会干扰单元间的同步,导致网络无法正确识别图像。因此,作者开发了一种称为神经探照灯处理器(NSP)的网络,该网络通过允许输入模式中图像之间的共同特征的短暂同步来识别图像。介绍了NCPP的机理和计算机模拟结果。然后通过与NCPP的比较,说明了NSP的结构和仿真。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Control of a robotic manipulating arm by a neural network simulation of the human cerebral and cerebellar cortical processes Neural network training using homotopy continuation methods A learning scheme of neural networks which improves accuracy and speed of convergence using redundant and diversified network structures The abilities of neural networks to abstract and to use abstractions Backpropagation based on the logarithmic error function and elimination of local minima
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1