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Identifying type I excitability using dynamics of stochastic neural firing patterns

机译:使用随机神经激发模式的动力学识别I型兴奋性

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The stochastic firing patterns are simulated near saddle-node bifurcation on an invariant cycle corresponding to type I excitability in stochastic Morris–Lecar model. In absence of external periodic signal, the stochastic firing manifests continuous distribution in ISI histogram (ISIH), whose amplitude at first increases sharply and then decreases exponentially. In presence of the external periodic signal, stochastic firing patterns appear as two cases of integer multiple firing with multiple discrete peaks in ISIH. One manifests perfect exponential decay in all peaks and the other imperfect exponential decay except a lower first peak. These stochastic firing patterns simulated with or without external periodic signal can be demonstrated in the experiments on rat hippocampal CA1 pyramidal neurons. The exponential decay laws in the multiple peaks are also acquired using probability analysis method. The perfect decay law is determined by the independent characteristic within the firing while the imperfect decay law is from the inhibitory effect. In addition, the stochastic firing patterns corresponding to type I excitability are compared to those of type II excitability. The results not only reveal the dynamics of stochastic firing patterns with or without external signal corresponding to type I excitability, but also provide practical indicators to availably identify type I excitability.
机译:在与随机Morris-Lecar模型中的I型兴奋性相对应的不变周期上,在鞍节点分叉附近模拟了随机点火模式。在没有外部周期性信号的情况下,随机触发会在ISI直方图(ISIH)中表现出连续分布,其幅度首先会急剧增大,然后呈指数减小。在存在外部周期性信号的情况下,随机点火模式显示为两种情况:ISIH中具有多个离散峰值的整数多次点火。一个在所有峰中都表现出完美的指数衰减,而另一个不完美的指数衰减(除了较低的第一个峰)。在大鼠海马CA1锥体神经元的实验中,可以证明有或没有外部周期性信号模拟的这些随机放电模式。还使用概率分析方法获得了多个峰中的指数衰减定律。理想的衰减定律由点火过程中的独立特性决定,而不完美的衰减定律则来自抑制作用。另外,将与I型兴奋性对应的随机点火模式与II型兴奋性进行比较。结果不仅揭示了随机激发模式的动态变化,具有或不具有对应于I型兴奋性的外部信号,而且还提供了实用的指标来有效地识别I型兴奋性。

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