Notes on Generalized Linear Models of Neurons

TL;DR

This paper discusses the application of generalized linear models (GLMs) to neural data, highlighting their flexibility, dynamic behavior, and computational efficiency for analyzing neuron and network activity.

Contribution

It provides a summary of the primary equations for GLMs in neural spike train analysis and discusses extensions to model spatio-temporal receptive fields and network activity.

Findings

GLMs are effective for modeling neural spike trains.

Extensions enable modeling of complex neural interactions.

GLMs link neural data analysis to broader statistical methods.

Abstract

Experimental neuroscience increasingly requires tractable models for analyzing and predicting the behavior of neurons and networks. The generalized linear model (GLM) is an increasingly popular statistical framework for analyzing neural data that is flexible, exhibits rich dynamic behavior and is computationally tractable (Paninski, 2004; Pillow et al., 2008; Truccolo et al., 2005). What follows is a brief summary of the primary equations governing the application of GLM's to spike trains with a few sentences linking this work to the larger statistical literature. Latter sections include extensions of a basic GLM to model spatio-temporal receptive fields as well as network activity in an arbitrary numbers of neurons.