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Synchronization of multiple chaotic FitzHugh-Nagumo neurons with gap junctions under external electrical stimulation

机译:外部电刺激下具有间隙连接的多个混沌FitzHugh-Nagumo神经元的同步

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摘要

This paper discusses the synchronization of three coupled chaotic FitzHugh-Nagumo (FHN) neurons with different gap junctions under external electrical stimulation. A nonlinear control law that guarantees the asymptotic synchronization of coupled neurons (with reduced computations) is proposed. The developed control law incorporates the synchronization error between two slave neurons in addition to the conventionally considered synchronization errors between the master and the slave neurons, which make the proposed scheme computationally more efficient. Further, a novel L_2 gain reduction criterion has been developed for multi-input multi-output systems with non-zero initial conditions, and is applied to robust synchronization of FHN neurons under L_2 norm bounded disturbance and uncertainties. Furthermore, a robust adaptive nonlinear control law is developed, which is capable of handling variations in nonlinear part of synchronization error dynamics, without using any neural-network-based training-oriented adaptive scheme. The proposed control schemes ensure global synchronization with computational simplicity, easy way of design and implementation and avoiding extra measurements. The results obtained with the proposed control laws are verified through numerical simulations.
机译:本文讨论了在外部电刺激下具有不同间隙连接的三个耦合混沌FitzHugh-Nagumo(FHN)神经元的同步。提出了一种非线性控制律,可保证耦合神经元的渐近同步(减少了计算量)。除了通常认为的主神经元和从神经元之间的同步误差之外,发达的控制律还包括两个从神经元之间的同步误差,这使得所提出的方案在计算上更加有效。此外,已经针对具有非零初始条件的多输入多输出系统开发了一种新颖的L_2增益降低准则,并将其应用于L_2范数有界干扰和不确定性的FHN神经元的鲁棒同步。此外,开发了一种鲁棒的自适应非线性控制律,它能够处理同步误差动态的非线性部分的变化,而无需使用任何基于神经网络的面向训练的自适应方案。所提出的控制方案可确保全局同步,计算简单,设计和实现的简便方法以及避免额外的测量。通过数值模拟验证了所提出的控制律所获得的结果。

著录项

  • 来源
    《Neurocomputing》 |2011年第17期|p.3296-3304|共9页
  • 作者单位

    Department of Cogno-Mechatronics Engineering, Pusan National University, 30 Jangjeon-dong, Ceumjeong-gu, Busan 609-735, Republic of Korea;

    Department of Cogno-Mechatronics Engineering, Pusan National University, 30 Jangjeon-dong, Ceumjeong-gu, Busan 609-735, Republic of Korea,School of Mechanical Engineering, Pusan National University, 30 Jangjeon-dong, Ceumjeong-gu, Busan 609-735, Republic of Korea;

    School of Mechanical Engineering, Pusan National University, 30 Jangjeon-dong, Ceumjeong-gu, Busan 609-735, Republic of Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    chaos synchronization; L_2 gain reduction; nonlinear; robust and adaptive control; fitzhugh-nagumo neurons;

    机译:混沌同步L_2增益降低;非线性鲁棒且自适应的控制;fitzhugh-nagumo神经元;
  • 入库时间 2022-08-18 02:08:17

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