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

页码：335 / 357

页数：22

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