IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i13p2811-d1177088.html
   My bibliography  Save this article

The Impact of Higher-Order Interactions on the Synchronization of Hindmarsh–Rose Neuron Maps under Different Coupling Functions

Author

Listed:
  • Mahtab Mehrabbeik

    (Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran P.O. Box 15875-4413, Iran)

  • Atefeh Ahmadi

    (Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran P.O. Box 15875-4413, Iran)

  • Fatemeh Bakouie

    (Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran P.O. Box 14155-6354, Iran)

  • Amir Homayoun Jafari

    (Biomedical Engineering and Medical Physics Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran P.O. Box 14155-6559, Iran
    Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran P.O. Box 14185-615, Iran)

  • Sajad Jafari

    (Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran P.O. Box 15875-4413, Iran
    Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Tehran P.O. Box 15875-4413, Iran)

  • Dibakar Ghosh

    (Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India)

Abstract

In network analysis, links depict the connections between each pair of network nodes. However, such pairwise connections fail to consider the interactions among more agents, which may be indirectly connected. Such non-pairwise or higher-order connections can be signified by involving simplicial complexes. The higher-order connections become even more noteworthy when it comes to neuronal network synchronization, an emerging phenomenon responsible for the many biological processes in real-world phenomena. However, involving higher-order interactions may considerably increase the computational costs. To confound this issue, map-based models are more suitable since they are faster, simpler, more flexible, and computationally more optimal. Therefore, this paper addresses the impact of pairwise and non-pairwise neuronal interactions on the synchronization state of 10 coupled memristive Hindmarsh–Rose neuron maps. To this aim, electrical, inner linking, and chemical synaptic functions are considered as two- and three-body interactions in three homogeneous and two heterogeneous cases. The results show that through chemical pairwise and non-pairwise synapses, the neurons achieve synchrony with the weakest coupling strengths.

Suggested Citation

  • Mahtab Mehrabbeik & Atefeh Ahmadi & Fatemeh Bakouie & Amir Homayoun Jafari & Sajad Jafari & Dibakar Ghosh, 2023. "The Impact of Higher-Order Interactions on the Synchronization of Hindmarsh–Rose Neuron Maps under Different Coupling Functions," Mathematics, MDPI, vol. 11(13), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2811-:d:1177088
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/13/2811/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/13/2811/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xu, Ying & Jia, Ya & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir, 2017. "Synchronization between neurons coupled by memristor," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 435-442.
    2. Ruofeng Rao & Zhi Lin & Xiaoquan Ai & Jiarui Wu, 2022. "Synchronization of Epidemic Systems with Neumann Boundary Value under Delayed Impulse," Mathematics, MDPI, vol. 10(12), pages 1-10, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sergey Kashchenko, 2023. "Van der Pol Equation with a Large Feedback Delay," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    2. Mingli Xia & Linna Liu & Jianyin Fang & Yicheng Zhang, 2023. "Stability Analysis for a Class of Stochastic Differential Equations with Impulses," Mathematics, MDPI, vol. 11(6), pages 1-10, March.
    3. Wang, Zhen & Parastesh, Fatemeh & Rajagopal, Karthikeyan & Hamarash, Ibrahim Ismael & Hussain, Iqtadar, 2020. "Delay-induced synchronization in two coupled chaotic memristive Hopfield neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    4. Jahanshahi, Hadi & Yousefpour, Amin & Munoz-Pacheco, Jesus M. & Kacar, Sezgin & Pham, Viet-Thanh & Alsaadi, Fawaz E., 2020. "A new fractional-order hyperchaotic memristor oscillator: Dynamic analysis, robust adaptive synchronization, and its application to voice encryption," Applied Mathematics and Computation, Elsevier, vol. 383(C).
    5. Li, Xing & Zou, Jianxun & Feng, Zhe & Wu, Zuheng & Xu, Zuyu & Yang, Fei & Zhu, Yunlai & Dai, Yuehua, 2023. "Thermal design engineering for improving the variation of memristor threshold," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    6. Rhaima, Mohamed, 2023. "Ulam–Hyers stability for an impulsive Caputo–Hadamard fractional neutral stochastic differential equations with infinite delay," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 210(C), pages 281-295.
    7. Guo, Yeye & Wang, Chunni & Yao, Zhao & Xu, Ying, 2022. "Desynchronization of thermosensitive neurons by using energy pumping," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    8. Bodo, B. & Armand Eyebe Fouda, J.S. & Mvogo, A. & Tagne, S., 2018. "Experimental hysteresis in memristor based Duffing oscillator," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 190-195.
    9. Kaijun Wu & Jiawei Li, 2023. "Effects of high–low-frequency electromagnetic radiation on vibrational resonance in FitzHugh–Nagumo neuronal systems," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(9), pages 1-19, September.
    10. Liu, Dan & Zhao, Song & Luo, Xiaoyuan & Yuan, Yi, 2021. "Synchronization for fractional-order extended Hindmarsh-Rose neuronal models with magneto-acoustical stimulation input," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    11. Natalya O. Sedova & Olga V. Druzhinina, 2023. "Exponential Stability of Nonlinear Time-Varying Delay Differential Equations via Lyapunov–Razumikhin Technique," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    12. Ge, Mengyan & Lu, Lulu & Xu, Ying & Mamatimin, Rozihajim & Pei, Qiming & Jia, Ya, 2020. "Vibrational mono-/bi-resonance and wave propagation in FitzHugh–Nagumo neural systems under electromagnetic induction," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    13. Chunsheng Wang & Xiangdong Liu & Feng Jiao & Hong Mai & Han Chen & Runpeng Lin, 2023. "Generalized Halanay Inequalities and Relative Application to Time-Delay Dynamical Systems," Mathematics, MDPI, vol. 11(8), pages 1-11, April.
    14. Branislav Rehák & Volodymyr Lynnyk, 2021. "Synchronization of a Network Composed of Stochastic Hindmarsh–Rose Neurons," Mathematics, MDPI, vol. 9(20), pages 1-16, October.
    15. Dutta, Maitreyee & Roy, Binoy Krishna, 2021. "A new memductance-based fractional-order chaotic system and its fixed-time synchronisation," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    16. Li, Jing & Zhu, Quanxin, 2023. "Event-triggered impulsive control of stochastic functional differential systems," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    17. Li, Zhijun & Chen, Kaijie, 2023. "Neuromorphic behaviors in a neuron circuit based on current-controlled Chua Corsage Memristor," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    18. Quanxin Zhu, 2022. "Nonlinear Systems: Dynamics, Control, Optimization and Applications to the Science and Engineering," Mathematics, MDPI, vol. 10(24), pages 1-2, December.
    19. Yong Tang, 2023. "Traveling Wave Optical Solutions for the Generalized Fractional Kundu–Mukherjee–Naskar (gFKMN) Model," Mathematics, MDPI, vol. 11(11), pages 1-12, June.
    20. Bao, Han & Yu, Xihong & Zhang, Yunzhen & Liu, Xiaofeng & Chen, Mo, 2023. "Initial condition-offset regulating synchronous dynamics and energy diversity in a memristor-coupled network of memristive HR neurons," Chaos, Solitons & Fractals, Elsevier, vol. 177(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:gam:jmathe:v:11:y:2023:i:13:p:2811-:d:1177088. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.