研究者業績

西村 治彦

ニシムラ ハルヒコ  (Haruhiko Nishimura)

基本情報

所属
大手前大学 現代社会学部 教授
兵庫県立大学 応用情報科学研究科 (名誉教授)
学位
学術博士(神戸大学)

ORCID ID
 https://orcid.org/0000-0003-1572-6747
J-GLOBAL ID
200901043372803974
researchmap会員ID
5000099933

外部リンク

主要な委員歴

 27

論文

 247
  • Sou Nobukawa, Aya Shirama, Yusuke Sakemi, Eiji Watanabe, Teijiro Isokawa, Haruhiko Nishimura, Kazuyuki Aihara
    2026年3月13日  
  • Anh Tu Tran, Sou Nobukawa, Nobuhiko Wagatsuma, Keiichiro Inagaki, Hirotaka Doho, Teruya Yamanishi, Haruhiko Nishimura
    IEEE Access 2026年  
  • Anh Tu Tran, Sou Nobukawa, Nobuhiko Wagatsuma, Keiichiro Inagaki, Hirotaka Doho, Teruya Yamanishi, Haruhiko Nishimura
    Nonlinear Theory and Its Applications, IEICE 2025年  
  • Anh Tu Tran, Sou Nobukawa, Nobuhiko Wagatsuma, Keiichiro Inagaki, Hirotaka Doho, Teruya Yamanishi, Haruhiko Nishimura
    Frontiers in Applied Mathematics and Statistics 10 2024年8月8日  
    Introduction 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, synchronizes 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. 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 behavior. Results and discussion 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.
  • Takahiro IINUMA, Yudai EBATO, Sou NOBUKAWA, Nobuhiko WAGATSUMA, Keiichiro INAGAKI, Hirotaka DOHO, Teruya YAMANISHI, Haruhiko NISHIMURA
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E107.A(8) 1106-1114 2024年8月1日  

MISC

 629

主要な共同研究・競争的資金等の研究課題

 33

主要な産業財産権

 3