论文标题
使用多元鹰队累积物对神经峰尖峰列车统计的封闭形式建模
Closed-form modeling of neuronal spike train statistics using multivariate Hawkes cumulants
论文作者
论文摘要
我们得出了具有激发和抑制作用的多元霍克斯工艺模型中尖峰列车驱动的任何神经元膜电位累积物的精确分析表达式。这种表达式可用于对模型随时间的统计行为的预测和敏感性分析，并使用革兰氏阴性膨胀估算神经元膜电位的概率密度。我们的结果显示，通过随机模拟为蒙特卡洛估计提供了更好的替代方法，并且包括基于组合递归的计算机代码。
We derive exact analytical expressions for the cumulants of any orders of neuronal membrane potentials driven by spike trains in a multivariate Hawkes process model with excitation and inhibition. Such expressions can be used for the prediction and sensitivity analysis of the statistical behavior of the model over time, and to estimate the probability densities of neuronal membrane potentials using Gram-Charlier expansions. Our results are shown to provide a better alternative to Monte Carlo estimates via stochastic simulations, and computer codes based on combinatorial recursions are included.
