论文标题
同时记录的神经尖峰火车的多元点过程模型
A Multivariate Point Process Model for Simultaneously Recorded Neural Spike Trains
论文作者
论文摘要
神经生理数据收集的当前最新技术允许同时记录数十个至数百个神经元的神经元，在此过程中，过程是一个适当的统计建模框架。但是，现有的点过程模型缺乏灵活且可在计算上易于处理的多元概括。本文介绍了Skellam过程的多元概括（SPR）（SPR），这是针对单个神经尖峰火车建模的点过程。多元SPR（MSPR）在模拟神经整合过程时在生物学上是有道理的。它的灵活依赖性结构和快速参数估计方法使其非常适合分析来自多个神经元的同时记录的尖峰列。通过模拟和分析实验数据来证明MSPR的优势和劣势。
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.
