Hierarchical structure among invariant subspaces of chaotic neural networks

被引：0
作者：
Motomasa Komuro
Kazuyuki Aihara
机构：
[1] Teikyo University of Science & Technology,Department of Media Science
[2] School of Engineering,Department of Mathematical Engineering and Information Physics
[3] The University of Tokyo,undefined
[4] CREST,undefined
[5] Japan Science and Technology Corporation (JST),undefined
来源：
Japan Journal of Industrial and Applied Mathematics | 2001年 / 18卷
关键词：
chaos; neural networks; associative memory; invariant subspaces; isotropy groups;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We analyse symmetrical structure among invariant subspaces in chaotic neural networks with ability of dynamical association. In particular, we elucidate hierarchical structure, or lattice structure among invariant subspaces supporting a skelton of associative dynamics.
引用
收藏
页码：335 / 357
页数：22
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