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噪声和非噪声多发性神经生物耦合 FitzHugh-Nagumo 网络的滞后同步，具有和不具有延迟耦合。

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

机构信息

Department of Mathematics, Pusan National University, Busan 46241, Republic of Korea.

Department of Mathematics, Air University, Islamabad 44000, Pakistan.

出版信息

Comput Intell Neurosci. 2022 Jun 2;2022:5644875. doi: 10.1155/2022/5644875. eCollection 2022.

DOI:10.1155/2022/5644875
PMID:35694576
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9184196/
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.

摘要

本文提出了一种在外部电刺激（EES）、外部干扰和两个 FHN 网络的每个状态的参数变化的情况下，对有噪和无噪的多个耦合神经生物 FitzHugh-Nagumo（FHN）驱动和从网络进行同步的方法。每个神经元网络的配置都考虑了大脑中真实神经元通信的各个方面，即突触和间隙连接。提出了新颖的自适应控制律，保证了不同配置下 FHN 神经网络的同步。利用李雅普诺夫稳定性理论分析推导了保证 FHN 网络同步的充分条件。通过不同的数值模拟，验证了所提出的控制律的有效性和鲁棒性。

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