The current state-of-the-art in neurophysiological data collection allows for simultaneous recording of tens to hundreds of neurons, for which point processes are an appropriate statistical modelling framework. However, existing point process models lack multivariate generalizations which are both flexible and computationally tractable. This paper introduces a multivariate generalization of the Skellam process with resetting (SPR), a point process tailored to model individual neural spike trains. The multivariate SPR (MSPR) is biologically justified as it mimics the process of neural integration. Its flexible dependence structure and a fast parameter estimation method make it well-suited for the analysis of simultaneously recorded spike trains from multiple neurons. The strengths and weaknesses of the MSPR are demonstrated through simulation and analysis of experimental data.
翻译:目前的神经生理数据收集最新技术允许同时记录数十至数百个神经元,对于这些神经元来说,点点进程是一个适当的统计建模框架;然而,现有的点进程模型缺乏灵活和可计算可动的多变量概括性,本文对Skellam进程作了多变量的概括化,并进行重新设置(SPR),这是一个适合个人神经峰值火车模型的点进程;多变量 SPR(MSPR)在生物上是有道理的,因为它模仿神经整合过程;其灵活的依赖性结构和快速参数估计方法使其适合于对多个神经元同时记录的峰值列进行分析;MSPR的长处和短处是通过模拟和分析实验数据来证明的。