记忆性莫里斯-勒卡神经元模型中低频和高频电流刺激的动态效应

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Chaos Solitons & Fractals Pub Date : 2024-10-17 DOI:10.1016/j.chaos.2024.115646
Quan Xu, Kai Wang, Chengtao Feng, Weiwei Fan, Ning Wang
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引用次数: 0

摘要

本文推导出二维(2D)Morris-Lecar 神经元模型中的钾离子和钙离子电流可分别用无源忆阻器和局部有源忆阻器来表征。然后,首先构建了具有等效电路方案的忆阻器莫里斯-勒卡神经元模型。分析了该模型的平衡轨迹及其稳定性,发现在模型参数适当的情况下,会出现折叠和霍普夫分岔。数值显示了在低频电流刺激下的爆发行为和带去极化后电位的混合模式振荡。此外,还从理论上推导出了猝发行为和混合模式振荡的分岔机制。此外,还从数值上发现了高频电流刺激下的点火模式和反单调现象。这项研究为从具有明确离子通道电流的神经元模型构建记忆仿生电路提供了一个新概念。
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Dynamical effects of low-frequency and high-frequency current stimuli in a memristive Morris–Lecar neuron model
This paper deduces that the potassium and calcium ion currents in the two-dimensional (2D) Morris–Lecar neuron model can be respectively characterized by a passive memristor and a locally active memristor. Then a memristive Morris–Lecar neuron model with an equivalent circuit scheme is first constructed. The equilibrium trajectory and its stability are analyzed, which displays fold and Hopf bifurcations with proper model parameters. Bursting behavior and mixed-mode oscillations with after-depolarizing potential for low-frequency current stimulus are numerically disclosed. Besides, the bifurcation mechanisms for bursting behavior and mixed-mode oscillations are theoretically deduced. Furthermore, the firing patterns and antimonotonicity phenomenon for high-frequency current stimulus are numerically discovered. This study provides a new concept for building memristive bionic circuits from neuron models with well-defined ion channel currents.
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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