Skip to main content

ORIGINAL RESEARCH article

Front. Appl. Math. Stat.
Sec. Mathematical Biology
Volume 10 - 2024 | doi: 10.3389/fams.2024.1434119
This article is part of the Research Topic Recent Developments in Mathematical Biology and Medicine View all articles

Emergence of Chaotic Resonance Controlled by Extremely Weak Feedback Signals in Neural Systems

Provisionally accepted
Anh T. Tran Anh T. Tran 1Sou Nobukawa Sou Nobukawa 1*Nobuhiko Wagatsuma Nobuhiko Wagatsuma 2Keiichiro Inagaki Keiichiro Inagaki 3Hirotaka Doho Hirotaka Doho 4Teruya Yamanishi Teruya Yamanishi 5Haruhiko Nishimura Haruhiko Nishimura 6
  • 1 Chiba Institute of Technology, Narashino, Japan
  • 2 Faculty of Science, Toho University, Funabashi, Chiba, Japan
  • 3 Chubu University, Kasugai, Aichi, Japan
  • 4 Kōchi University, Kochi, Kōchi, Japan
  • 5 Osaka Seikei University, Osaka, Japan
  • 6 Yamato University, Suita, Miyagi, Japan

The final, formatted version of the article will be published soon.

    Chaotic resonance is similar to stochastic resonance, which emerges from chaos as an internal dynamical fluctuation. In chaotic resonance, chaos-chaos intermittency (CCI), in which the chaotic orbits shift between the separated attractor regions, synchronises with a weak input signal. Chaotic resonance exhibits higher sensitivity than stochastic resonance. However, engineering applications are difficult because adjusting the internal system parameters, especially of biological systems, to induce chaotic resonance from the outside environment is challenging. Moreover, several studies reported abnormal neural activity caused by CCI. Recently, our study proposed that the double-Gaussian-filtered reduced region of orbit (RRO) method (abbreviated as DG-RRO), using external feedback signals to generate chaotic resonance, could control CCI with a lower perturbation strength than the conventional RRO method. This study applied the DG-RRO method to a model which includes excitatory and inhibitory neuron populations in the frontal cortex as typical neural systems with CCI behaviour. Our results reveal that DG-RRO can be applied to neural systems with extremely low perturbation but still maintain robust effectiveness compared to conventional RRO, even in noisy environments.

    Keywords: chaotic resonance, feedback control, neural system, Nonlinear Dynamics, Synchronisation

    Received: 17 May 2024; Accepted: 15 Jul 2024.

    Copyright: © 2024 Tran, Nobukawa, Wagatsuma, Inagaki, Doho, Yamanishi and Nishimura. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sou Nobukawa, Chiba Institute of Technology, Narashino, Japan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.