Lag Synchronization of Noisy and Nonnoisy Multiple Neurobiological Coupled FitzHugh-Nagumo Networks with and without Delayed Coupling.

Journal: Computational intelligence and neuroscience
Published Date: Jun 2, 2022

Abstract

This paper presents a methodology for synchronizing noisy and nonnoisy multiple coupled neurobiological FitzHugh-Nagumo (FHN) drive and slave neural networks with and without delayed coupling, under external electrical stimulation (EES), external disturbance, and variable parameters for each state of both FHN networks. Each network of neurons was configured by considering all aspects of real neurons communications in the brain, i.e., synapse and gap junctions. Novel adaptive control laws were developed and proposed that guarantee the synchronization of FHN neural networks in different configurations. The Lyapunov stability theory was utilized to analytically derive the sufficient conditions that ensure the synchronization of the FHN networks. The effectiveness and robustness of the proposed control laws were shown through different numerical simulations.

Authors

  • Malik Muhammad Ibrahim
    Department of Mathematics, Pusan National University, Busan 46241, Republic of Korea.
  • Shazia Iram
    Department of Mathematics, Air University, Islamabad 44000, Pakistan.
  • Muhammad Ahmad Kamran
    Department of Cogno-Mechatronics, Pusan National University, Busan 46241, Republic of Korea.
  • Malik Muhammad Naeem Mannan
    School of Health Sciences and Social Work, Griffith University, Gold Coast, Australia.
  • Muhammad Umair Ali
    Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea.
  • Il Hyo Jung
    Department of Mathematics, College of Natural Sciences, Pusan National University, Busan, South Korea.
  • Sangil Kim
    Department of Mathematics, Pusan National University, Busan 46241, Republic of Korea.

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