Mankin Romi, Rekker Astrid
School of Natural Sciences and Health, Tallinn University, 29 Narva Road, 10120 Tallinn, Estonia.
Phys Rev E. 2020 Jul;102(1-1):012103. doi: 10.1103/PhysRevE.102.012103.
The behavior of a stochastic perfect integrate-and-fire (PIF) model of neurons is considered. The effect of temporally correlated random activity of synaptic inputs is modeled as a combination of an asymmetric dichotomous noise and a random operation time in the form of an inverse strictly increasing Lévy-type subordinator. Using a first-passage-time formulation, we find exact expressions for the output interspike interval (ISI) statistics. Particularly, it is shown that at some parameter regimes the ISI density exhibits a multimodal structure. Moreover, it is demonstrated that the coefficient of variation, the serial correlation coefficient, and the Fano factor display a nonmonotonic dependence on the mean input current μ, i.e., the ISI's regularity is maximized at an intermediate value of μ. The features of spike statistics, analytically revealed in our study, are compared with previously obtained results for a perfect integrate-and-fire neuron model driven by dichotomous noise (without subordination).
考虑了神经元的随机完美积分发放（PIF）模型的行为。突触输入的时间相关随机活动的影响被建模为不对称二分噪声和以严格递增的逆 Lévy 型从属过程形式的随机操作时间的组合。使用首通时间公式，我们找到了输出脉冲间隔（ISI）统计量的精确表达式。特别地，表明在某些参数范围内，ISI 密度呈现多峰结构。此外，证明了变异系数、序列相关系数和 Fano 因子对平均输入电流μ呈现非单调依赖性，即 ISI 的规律性在μ的中间值处最大化。将我们研究中分析揭示的脉冲统计特征与先前获得的由二分噪声驱动（无从属关系）的完美积分发放神经元模型的结果进行了比较。
