A balance equation determines a switch in neuronal excitability.
A balance equation determines a switch in neuronal excitability.
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DOI：
10.1371/journal.pcbi.1003040
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发表时间：
2013
影响因子：
4.3
通讯作者：
Sepulchre R
中科院分区：
文献类型：
--
作者：
Franci A;Drion G;Seutin V;Sepulchre R
我们使用定性的洞察力的平面神经元相图检测任意电导为基础的模型从一个简单的数学条件的兴奋性开关。该条件表示在静息电位下提供负反馈的离子通道（恢复通道）和在静息电位下提供正反馈的离子通道（再生通道）之间的平衡。几何上，该条件施加了一个跨临界分叉，通过单个生理参数的变化来控制兴奋性的切换。我们分析了六种不同的基于电导的模型，总是发现跨临界分叉和相关的兴奋性开关，这表明数学预测具有生理相关性，并且相同的调节机制可能涉及许多神经元的兴奋性和信号传导。了解神经元在不同生理和药理条件下变化的电生理特征是实验电生理学的中心焦点，因为它是神经系统中细胞信号的关键组成部分。计算建模可以通过识别核心机制并从模型的数学分析中提出药理学靶点来帮助实验者进行这一探索。但是，实验和数学预测之间的成功相互作用需要新的分析工具，以适应当今可用的高维计算模型的复杂性。我们使用分叉理论提出了一个数学条件，可以检测到任意电导为基础的神经元模型中的神经元兴奋性的重要开关，我们说明了其生理相关性在六个已发表的国家的最先进的模型不同的神经元。
We use the qualitative insight of a planar neuronal phase portrait to detect an excitability switch in arbitrary conductance-based models from a simple mathematical condition. The condition expresses a balance between ion channels that provide a negative feedback at resting potential (restorative channels) and those that provide a positive feedback at resting potential (regenerative channels). Geometrically, the condition imposes a transcritical bifurcation that rules the switch of excitability through the variation of a single physiological parameter. Our analysis of six different published conductance based models always finds the transcritical bifurcation and the associated switch in excitability, which suggests that the mathematical predictions have a physiological relevance and that a same regulatory mechanism is potentially involved in the excitability and signaling of many neurons. Understanding the changing electrophysiological signatures of neurons in different physiological and pharmacological conditions is a central focus of experimental electrophysiology because a key component of cell signaling in the nervous system. Computational modeling may assist experimentalists in this quest by identifying core mechanisms and suggesting pharmacological targets from a mathematical analysis of the model. But a successful interplay between experiments and mathematical predictions requires new analysis tools adapted to the complexity of high-dimensional computational models nowadays available. We use bifurcation theory to propose a mathematical condition that can detect an important switch of neuronal excitability in arbitrary conductance-based neuronal models and we illustrate its physiological relevance in six published state-of-the art models of different neurons.
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影响因子：
4.3
作者：
Drion G;Massotte L;Sepulchre R;Seutin V
通讯作者：
Seutin V
影响因子：
5.3
作者：
D'Angelo, E;Nieus, T;Naldi, G
通讯作者：
Naldi, G
影响因子：
1.2
作者：
Rubin, JE;Terman, D
通讯作者：
Terman, D
DOI：
10.1073/pnas.0712231105
发表时间：
2008-03-04
影响因子：
11.1
作者：
Izhikevich, Eugene M.;Edelman, Gerald M.
通讯作者：
Edelman, Gerald M.
影响因子：
5.5
作者：
HODGKIN, AL
通讯作者：
HODGKIN, AL