Dynamical response of Autaptic Izhikevich Neuron disturbed by Gaussian white noise.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2023-02-01 DOI:10.1007/s10827-022-00832-w
Mohammad Saeed Feali, Abdolsamad Hamidi
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引用次数: 2

Abstract

Using the improved memristive Izhikevich neuron model, the effects of autaptic connection as well as electromagnetic induction are studied on the dynamical behavior of neuronal spiking. Using bifurcation analysis for membrane potentials, the effects of autaptic and electromagnetic parameters on the mode transition in electrical activities of the neuron model are investigated. Furthermore, white Gaussian noise is considered in the neuron model, to evaluate the effect of electromagnetic disturbance on the firing pattern of the neuron using the coefficient of variation. The bifurcation diagram versus autaptic conductance and time delay has been extensively studied. The results show that the effects of autaptic connection as well as electromagnetic induction on the spiking behavior of neurons can be well demonstrated by using the Izhikevich model. The electrical activities of the Izhikevich neuron model become more complex when the effects of autaptic connection and electromagnetic induction are considered in the neuron model. Using the Izhikevich neuron model, the high variety of spiking/bursting patterns is represented in the bifurcation diagram of inter-spike interval versus autaptic or electromagnetic parameters. Noise can have distinct effects on the spiking activity of the neuron, for the subthreshold input current, increasing the intensity of the electromagnetic noise increases the regularity of the neuron spiking, but for the suprathreshold input current, the regularity of spiking decreases with noise.

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高斯白噪声扰动下自适应Izhikevich神经元的动态响应。
利用改进的记忆性Izhikevich神经元模型,研究了自适应连接和电磁感应对神经元尖峰动态行为的影响。利用膜电位分岔分析,研究了自适应参数和电磁参数对神经元电活动模式转换的影响。此外,在神经元模型中考虑高斯白噪声,利用变异系数来评价电磁干扰对神经元放电模式的影响。分岔图与自适应电导和时间延迟的关系已被广泛研究。结果表明,自适应连接和电磁感应对神经元尖峰行为的影响可以用Izhikevich模型很好地证明。当考虑自适应连接和电磁感应的影响时,Izhikevich神经元模型的电活动变得更加复杂。利用Izhikevich神经元模型,在脉冲间隔与自适应参数或电磁参数的分岔图中表示了脉冲/破裂模式的高度多样性。噪声对神经元的尖峰活动有明显的影响,对于阈下输入电流,增加电磁噪声的强度增加神经元尖峰的规律性,但对于阈上输入电流,尖峰的规律性随着噪声的增加而降低。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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