Dynamics and synchronization of the Morris-Lecar model with field coupling subject to electromagnetic excitation

IF 3.4 2区 数学 Q1 MATHEMATICS, APPLIED
Lixiang Wei , Dong Li , Jiangang Zhang
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引用次数: 0

Abstract

In this study, we extend the Morris-Lecar (M-L) neuron model to design a neuronal network model with field effects. Using Lyapunov theory and the master stability function to evaluate the stability of synchronous manifolds. The Hamilton energy function of a single neuron is derived using Helmholtz's theorem to equivalently describe its internal field energy, and bioelectric activities are analyzed in conjunction with synchronization error functions. A comprehensive analysis was conducted using various numerical methods, including time series diagrams, bifurcation diagrams, two-parameter plane synchronization error diagrams, and similarity function diagrams. The observations indicate that variations in conductivity and equilibrium potential directly impact the firing patterns and synchronization states of neurons. In various coupling methods, an increase in coupling strength induces different degrees of oscillation within the coupled system. The effects of three coupling methods on the synchronization of the neuronal network are examined using spatiotemporal evolution diagrams and energy evolution diagrams. Global synchronization error and synchronization factors are introduced to quantify the level of synchrony. Numerical results indicate that an appropriate coupling strength and ionic conductance enhance network synchrony. Numerical results indicate that appropriate coupling strength promotes network synchrony. The insights gained from this study contribute to providing a more coherent framework for constructing neuronal network models under field-coupled conditions, thereby enhancing the understanding of fundamental principles in neuroscience and offering new perspectives for the treatment and rehabilitation of neurological disorders.
莫里斯-勒卡模型在电磁激励下的动力学和同步性
在这项研究中,我们扩展了莫里斯-勒卡(M-L)神经元模型,设计了一个具有场效应的神经元网络模型。利用李亚普诺夫理论和主稳定函数评估同步流形的稳定性。利用亥姆霍兹定理推导出单个神经元的汉密尔顿能量函数,等效描述其内部场能,并结合同步误差函数分析生物电活动。利用各种数值方法进行了综合分析,包括时间序列图、分岔图、双参数平面同步误差图和相似函数图。观察结果表明,电导率和平衡电位的变化直接影响神经元的发射模式和同步状态。在各种耦合方法中,耦合强度的增加会引起耦合系统内不同程度的振荡。我们使用时空演化图和能量演化图研究了三种耦合方法对神经元网络同步的影响。引入了全局同步误差和同步因子来量化同步水平。数值结果表明,适当的耦合强度和离子传导能增强网络同步性。数值结果表明,适当的耦合强度可促进网络同步。本研究获得的见解有助于为构建场耦合条件下的神经元网络模型提供一个更加连贯的框架,从而加深对神经科学基本原理的理解,并为神经系统疾病的治疗和康复提供新的视角。
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来源期刊
Communications in Nonlinear Science and Numerical Simulation
Communications in Nonlinear Science and Numerical Simulation MATHEMATICS, APPLIED-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
6.80
自引率
7.70%
发文量
378
审稿时长
78 days
期刊介绍: The journal publishes original research findings on experimental observation, mathematical modeling, theoretical analysis and numerical simulation, for more accurate description, better prediction or novel application, of nonlinear phenomena in science and engineering. It offers a venue for researchers to make rapid exchange of ideas and techniques in nonlinear science and complexity. The submission of manuscripts with cross-disciplinary approaches in nonlinear science and complexity is particularly encouraged. Topics of interest: Nonlinear differential or delay equations, Lie group analysis and asymptotic methods, Discontinuous systems, Fractals, Fractional calculus and dynamics, Nonlinear effects in quantum mechanics, Nonlinear stochastic processes, Experimental nonlinear science, Time-series and signal analysis, Computational methods and simulations in nonlinear science and engineering, Control of dynamical systems, Synchronization, Lyapunov analysis, High-dimensional chaos and turbulence, Chaos in Hamiltonian systems, Integrable systems and solitons, Collective behavior in many-body systems, Biological physics and networks, Nonlinear mechanical systems, Complex systems and complexity. No length limitation for contributions is set, but only concisely written manuscripts are published. Brief papers are published on the basis of Rapid Communications. Discussions of previously published papers are welcome.
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