4.7 Article

Chimera states in coupled Hindmarsh-Rose neurons with α-stable noise

期刊

CHAOS SOLITONS & FRACTALS
卷 148, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.chaos.2021.110976

关键词

Chimera state; Hindmarsh-Rose system; alpha-stable noise

资金

  1. National Natural Science Foundation of China [12072264, 11902118]
  2. Fundamental Research Funds for the Central Universities
  3. Research Funds for Interdisciplinary Subject of Northwestern Polytechnical University
  4. Shaanxi Project for Distinguished Young Scholars, Shaanxi Provincial Key RD Program [2020KW-013, 2019TD010]
  5. National Key Research and Development Program of China [2018AAA0102201]
  6. National Science Centre, Poland, OPUS Programme [2018/29/B/ST8/00457]
  7. Ministry of Science and Higher Education of the Russian Federation [075152020926]

向作者/读者索取更多资源

This paper investigates alpha-stable noise-induced chimera states in a small-world Hindmarsh-Rose neuronal network, showing that changes in network and noise parameters can affect the occurrence and disappearance of chimera states. The concept of coherence strength based on the local order parameter is proposed for identifying both the occurrence of chimera states and the proportion of coherent neurons in the entire network.
In this paper, we study alpha-stable noise-induced chimera states in a small-world Hindmarsh-Rose neuronal network. Chimera states are the coexistence of coherence and incoherence. The alpha-stable noise is a general non-Gaussian noise, and can be used to describe more complex and changeable noisy environments. We focus on the effect of the parameters of the small-world network (the rewiring probability) and alpha-stable noise (the stability parameter and noise intensity) on the chimera state. We find that the changes of the rewiring probability, the stability parameter and noise intensity can make the location and range of the incoherence domain for the chimera state shift and change, and changes of the stability parameter and noise intensity even make chimera state disappear. Moreover, we propose the strength of coherence based on the local order parameter, and it can be used to identify not only the occurrence of chimera states but also the proportion of coherent neurons in the entire network. (C) 2021 Elsevier Ltd. All rights reserved.

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