Movement and memory function in biological neural networks

N. Ishii, K. Naka
{"title":"Movement and memory function in biological neural networks","authors":"N. Ishii, K. Naka","doi":"10.1109/INBS.1995.404283","DOIUrl":null,"url":null,"abstract":"Asymmetrical neural networks are shown in a biological neural network, the catfish retina. Several mechanisms have been proposed for the detection of motion in biological system. Hassenstein and Reichardt network (1956) and Barlow and Levick network (1965) of movements are similar to the asymmetrical network developed here. To make clear the difference among these asymmetrical networks, we applied nonlinear analysis developed by N. Wiener. Then, we can derive the /spl alpha/-equation of movement, which shows the direction of movement. During the movement, we also can derive the movement equation, which implies that the movement holds regardless of the parameter /spl alpha/. By analyzing the biological asymmetric neural networks, it is shown that the asymmetric networks are excellent in the ability of spatial information processing on the retinal level. The symmetric network was discussed by applying nonlinear analysis. In the symmetric neural network, it was suggested that memory function is needed to perceive the movement.<<ETX>>","PeriodicalId":423954,"journal":{"name":"Proceedings First International Symposium on Intelligence in Neural and Biological Systems. INBS'95","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings First International Symposium on Intelligence in Neural and Biological Systems. INBS'95","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INBS.1995.404283","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Asymmetrical neural networks are shown in a biological neural network, the catfish retina. Several mechanisms have been proposed for the detection of motion in biological system. Hassenstein and Reichardt network (1956) and Barlow and Levick network (1965) of movements are similar to the asymmetrical network developed here. To make clear the difference among these asymmetrical networks, we applied nonlinear analysis developed by N. Wiener. Then, we can derive the /spl alpha/-equation of movement, which shows the direction of movement. During the movement, we also can derive the movement equation, which implies that the movement holds regardless of the parameter /spl alpha/. By analyzing the biological asymmetric neural networks, it is shown that the asymmetric networks are excellent in the ability of spatial information processing on the retinal level. The symmetric network was discussed by applying nonlinear analysis. In the symmetric neural network, it was suggested that memory function is needed to perceive the movement.<>
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
生物神经网络中的运动和记忆功能
不对称的神经网络显示在生物神经网络,鲶鱼视网膜。人们提出了几种检测生物系统运动的机制。运动的Hassenstein和Reichardt网络(1956)和Barlow和Levick网络(1965)与这里发展的不对称网络相似。为了明确这些不对称网络之间的区别,我们应用了N. Wiener开发的非线性分析。然后,我们可以推导出/spl alpha/-运动方程,该方程显示了运动方向。在运动过程中,我们还可以推导出运动方程,这意味着无论参数/spl α /如何,运动都保持不变。通过对生物非对称神经网络的分析,表明非对称神经网络在视网膜水平上具有优异的空间信息处理能力。应用非线性分析方法对对称网络进行了讨论。在对称神经网络中,我们认为需要记忆功能来感知运动。
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
Self-organized learning in multi-layer networks Gene classification artificial neural system Modeling sensory representations in brain: new methods for studying functional architecture reveal unique spatial patterns A genetic algorithm for decomposition type choice in OKFDDs The splicing as an operation on formal languages
×
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