A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects
A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects
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DOI：
10.1152/jn.00697.2004
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发表时间：
2005-02-01
影响因子：
2.5
通讯作者：
Brown, EN
中科院分区：
文献类型：
--
作者：
Truccolo, W;Eden, UT;Brown, EN
多种因素同时影响单个神经元的尖峰活动。确定这些因素的影响和相对重要性是神经生理学中的一个具有挑战性的问题。我们提出了一个基于点过程似然函数的统计框架，将神经元的尖峰概率与三个典型的协变量相关联：神经元自己的尖峰历史，并发的集成活动和外部协变量，如刺激或行为。该框架使用条件强度函数的参数模型来定义神经元的尖峰概率的协变量。利用点过程的离散时间似然函数进行模型拟合和模型分析。我们表明，通过建模的对数的条件强度函数的协变量的函数的线性组合，离散时间点过程似然函数很容易分析的广义线性模型（GLM）框架。我们说明了我们的GLM和非GLM似然函数的方法，使用模拟数据和多变量的单单位活动数据同时记录从运动皮层的猴子执行视觉追踪跟踪任务。点过程框架为最大似然估计、拟合优度评估、残差分析、模型选择和神经解码提供了一种灵活的、计算效率高的方法。因此，该框架允许制定和分析的点过程模型的神经尖峰活动，很容易捕捉多个协变量的同时影响，并使其相对重要性的评估。
Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.