Analysis of sinusoidal noisy leaky integrate-and-fire models and comparison with experimental data are important to understand the neural code and neural synchronization and rhythms. In this paper, we propose two methods to estimate input parameters using interspike interval data only. One is based on numerical solutions of the Fokker-Planck equation, and the other is based on an integral equation, which is fulfilled by the interspike interval probability density. This generalizes previous methods tailored to stationary data to the case of time-dependent input. The main contribution is a binning method to circumvent the problems of nonstationarity, and an easy-to-implement initializer for the numerical procedures. The methods are compared on simulated data. LIST OF ABBREVIATIONS LIF Leaky integrate-and-fireISI: Interspike intervalSDE: Stochastic differential equationPDE: Partial differential equation.

中文翻译：

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基于 Fokker-Planck 和 Fortet 方程的具有正弦和随机强迫的泄漏积分火模型的参数估计。

分析正弦噪声泄漏积分和点火模型并与实验数据进行比较对于理解神经代码和神经同步和节律非常重要。在本文中，我们提出了两种仅使用峰值间隔数据来估计输入参数的方法。一种是基于 Fokker-Planck 方程的数值解，另一种是基于积分方程，它由尖峰间隔概率密度来满足。这将针对固定数据定制的先前方法推广到时间相关输入的情况。主要贡献是一种避免非平稳性问题的分箱方法，以及一种易于实现的数值过程初始化器。这些方法在模拟数据上进行了比较。缩略语列表 LIF Leaky integration-and-fireISI：Interspike intervalSDE：