IDEAS home Printed from https://ideas.repec.org/a/wsi/fracta/v30y2022i10ns0218348x22401946.html
   My bibliography  Save this article

Interlayer And Intralayer Synchronization In Multiplex Fractional-Order Neuronal Networks

Author

Listed:
  • BO YAN

    (School of Information Engineering, Shaoyang University, Shaoyang 422000, P. R. China)

  • FATEMEH PARASTESH

    (Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran)

  • SHAOBO HE

    (School of Physics and Electronics, Central South University, Changsha 410083, P. R. China)

  • KARTHIKEYAN RAJAGOPAL

    (Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, India)

  • SAJAD JAFARI

    (Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran5Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran)

  • MATJAŽ PERC

    (Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia7Complexity Science Hub Vienna, Josefstädterstraße 39, 1080 Vienna, Austria8Department of Medical Research, China Medical University Hospital, China Medical University, 404332, Taichung, Taiwan9Alma Mater Europaea, Slovenska ulica 17, 2000 Maribor, Slovenia)

Abstract

Fractional-order models describing neuronal dynamics often exhibit better compatibility with diverse neuronal firing patterns that can be observed experimentally. Due to the overarching significance of synchronization in neuronal dynamics, we here study synchronization in multiplex neuronal networks that are composed of fractional-order Hindmarsh–Rose neurons. We compute the average synchronization error numerically for different derivative orders in dependence on the strength of the links within and between network layers. We find that, in general, fractional-order models synchronize better than integer-order models. In particular, we show that the required interlayer and intralayer coupling strengths for interlayer or intralayer synchronization can be weaker if we reduce the derivative order of the model describing the neuronal dynamics. Furthermore, the dependence of the interlayer or intralayer synchronization on the intralayer or interlayer coupling strength vanishes with decreasing derivative order. To support these results analytically, we use the master stability function approach for the considered multiplex fractional-order neuronal networks, by means of which we obtain sufficient conditions for the interlayer and intralayer synchronizations that are in agreement with numerical results.

Suggested Citation

  • Bo Yan & Fatemeh Parastesh & Shaobo He & Karthikeyan Rajagopal & Sajad Jafari & Matjaå½ Perc, 2022. "Interlayer And Intralayer Synchronization In Multiplex Fractional-Order Neuronal Networks," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(10), pages 1-11, December.
    • Handle: RePEc:wsi:fracta:v:30:y:2022:i:10:n:s0218348x22401946
    DOI: 10.1142/S0218348X22401946
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0218348X22401946
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0218348X22401946?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hu, Dongpo & Ma, Linyi & Song, Zigen & Zheng, Zhaowen & Cheng, Lifang & Liu, Ming, 2024. "Multiple bifurcations of a time-delayed coupled FitzHugh–Rinzel neuron system with chemical and electrical couplings," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    2. Fateev, I. & Polezhaev, A., 2024. "Chimera states in a lattice of superdiffusively coupled neurons," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:fracta:v:30:y:2022:i:10:n:s0218348x22401946. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: https://www.worldscientific.com/worldscinet/fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.