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Singular-continuous nowhere-differentiable attractors in neural systems

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Please use this identifier to cite or link to this item:http://doi.org/10.14943/83506

Title: Singular-continuous nowhere-differentiable attractors in neural systems
Authors: Tsuda, I. Browse this author
Yamaguchi, A. Browse this author
Keywords: Singular-continuous nowhere-differentiable attractors
Chaos-driven contraction dynamics
Information processings on Cantor set
Dimension gap
Issue Date: 1-Nov-1996
Journal Title: Hokkaido University Preprint Series in Mathematics
Volume: 360
Start Page: 1
End Page: 24
Abstract: 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.
Type: bulletin (article)
URI: http://hdl.handle.net/2115/69110
Appears in Collections:理学院・理学研究院 (Graduate School of Science / Faculty of Science) > Hokkaido University Preprint Series in Mathematics

Submitter: 数学紀要登録作業用

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