m电流诱导神经元兴奋性类的Bogdanov-Takens分岔与切换。

IF 2.3 4区 医学 Q1 Neuroscience
Isam Al-Darabsah, Sue Ann Campbell
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引用次数: 3

摘要

在这项工作中,我们考虑了一个基于一般电导的神经元模型,其中包括乙酰胆碱敏感的m电流。我们研究了由施加电流[公式:见文]、m电流的最大电导[公式:见文]和漏电流的电导[公式:见文]组成的参数空间中的分岔。我们给出了保证波格丹诺夫- takens (BT)点存在的模型的精确条件,并表明这样的点可以通过改变[公式:见文]和[公式:见文]而出现。讨论了BT点成为波格丹诺夫-塔肯斯尖点(BTC)的情况,并证明了这种点在三维参数空间中可以出现。分岔分析的结果应用于不同的神经元模型,并通过使用MATCONT包生成的数值分岔图进行验证和补充。我们的结论是,随着m电流电导的增加,由BT点组织的神经元兴奋性类型发生了转变,神经元从i类转换到ii类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

M-current induced Bogdanov-Takens bifurcation and switching of neuron excitability class.

M-current induced Bogdanov-Takens bifurcation and switching of neuron excitability class.

M-current induced Bogdanov-Takens bifurcation and switching of neuron excitability class.

M-current induced Bogdanov-Takens bifurcation and switching of neuron excitability class.

In this work, we consider a general conductance-based neuron model with the inclusion of the acetycholine sensitive, M-current. We study bifurcations in the parameter space consisting of the applied current [Formula: see text], the maximal conductance of the M-current [Formula: see text] and the conductance of the leak current [Formula: see text]. We give precise conditions for the model that ensure the existence of a Bogdanov-Takens (BT) point and show that such a point can occur by varying [Formula: see text] and [Formula: see text]. We discuss the case when the BT point becomes a Bogdanov-Takens-cusp (BTC) point and show that such a point can occur in the three-dimensional parameter space. The results of the bifurcation analysis are applied to different neuronal models and are verified and supplemented by numerical bifurcation diagrams generated using the package MATCONT. We conclude that there is a transition in the neuronal excitability type organised by the BT point and the neuron switches from Class-I to Class-II as conductance of the M-current increases.

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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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