Singular-continuous nowhere-differentiable attractors in neural systems
Ichiro Tsuda, Akihiro Yamaguchi · Institutional Repositories DataBase (IRDB) · 1996
We present a neural model for a singular-continuous nowhere-differentiable (SCND) attractors. This model shows various characteristics originated in attractor's nowheredifferentiability, 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. vVith 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. vVe also present possible information processings with these attractors.