Singular-continuous nowhere-differentiable attractors in neural systems

被引：19

作者：

Tsuda, I ^{[1
]}

Yamaguchi, A

机构：

[1] Hokkaido Univ, Grad Sch Sci, Dept Math, Appl Math & Complex Syst Res Grp, Sapporo, Hokkaido 060, Japan

[2] Hokkaido Univ, Fac Engn, Res Grp Complex Syst Engn, Sapporo, Hokkaido 060, Japan

来源：

NEURAL NETWORKS | 1998年 / 11卷 / 05期

关键词：

singular-continuous nowhere-differentiable attractors; chaos-driven contraction dynamics; information processings on Canter set; dimension gap;

D O I：

10.1016/S0893-6080(98)00028-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a neural model for a singular-continuous nowhere-differentiable (SCND) attractors. This model shows various characteristics originated in attractor's nowhere-differentiability, in spite of a differentiable dynamical system. SCND attractors are still unfamiliar in the neural network studies and have not yet been observed in both artificial and biological neural systems. With numerical calculations of various kinds of statistical quantities in artificial neural network, dynamical characters of SCND attractors are strongly suggested to be observed also in neural systems experiments. We also present possible information processings with these attractors. (C) 1998 Elsevier Science Ltd. All rights reserved.

引用

页码：927 / 937

页数：11

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