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Suppression of Multimodality in Inter-Spike Interval Distribution: Role of External Damped Oscillatory Input

机译:峰间间隔分布中的多模态抑制:外部阻尼振荡输入的作用

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摘要

A theoretical model based on the generalized neuronal model with distributed delay (GNMDD) is proposed to generate multimodal/bimodal inter-spike interval (ISI) distribution. The distributed delay is assumed to be general in the sense that it is a linear combination of gamma distributed weak and strong memory kernels. It is found that the expected membrane potential in the subthreshold regime exhibits damped oscillatory behavior. This causes the ISI pattern to become bimodal/multimodal. Furthermore, the effect of external damped oscillatory current in the GNMDD model is investigated. Based on extensive simulation study of a set of coupled stochastic differential equations in the presence of a threshold, a vast amount of spiking data are generated. Using the non-parametric modeling approach, the optimal bin width of the histogram is obtained by using the Freedman-Diaconis rule. It is to be noted that, with the increasing amplitude of the damped oscillatory current, the multimodal ISI distribution changes to unimodal ISI distribution when the magnitude of external current reaches a critical value. It is found that the entropy also shows a sudden transition around the critical point. This phenomenon is akin to a phase transition.
机译:提出了一种基于广义神经元分布式时延模型(GNMDD)的理论模型,以生成多峰/双峰钉间间隔(ISI)分布。从某种意义上说,分布式延迟是通用的,它是伽马分布的弱和强内存内核的线性组合。发现在亚阈值范围内预期的膜电位表现出阻尼的振荡行为。这导致ISI模式变为双峰/多峰。此外,还研究了GNMDD模型中外部阻尼振荡电流的影响。基于在存在阈值的情况下对一组耦合的随机微分方程的广泛仿真研究,生成了大量的尖峰数据。使用非参数建模方法,可以使用Freedman-Diaconis规则获得直方图的最佳bin宽度。应当注意,随着阻尼振荡电流的幅度增加,当外部电流的大小达到临界值时,多峰ISI分布变为单峰ISI分布。发现熵也显示出在临界点附近的突然转变。这种现象类似于相变。

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