A Point Process Framework for Relating Neural Spiking Activity to Spiking History, Neural Ensemble, and Extrinsic Covariate Effects

TLDR

Multiple factors simultaneously affect the spiking activity of individual neurons, and determining their effects and relative importance is challenging. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to its own spiking history, concurrent ensemble activity, and extrinsic covariates. The framework uses parametric models of the conditional intensity function and discrete‑time point process likelihood to fit and analyze models, illustrated with GLM and non‑GLM approaches on simulated data and motor‑cortex recordings. The framework offers a flexible, computationally efficient approach for maximum likelihood estimation, goodness‑of‑fit assessment, residual analysis, model selection, and neural decoding, enabling assessment of multiple covariates’ relative importance.

Abstract

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.