Capturing the dynamical repertoire of single neurons with generalized linear models

TL;DR

This paper demonstrates that Poisson generalized linear models can replicate a wide array of neural response behaviors, making them effective tools for studying single-neuron dynamics and variability.

Contribution

The study systematically compares the dynamical capabilities of Poisson GLMs to real neurons, showing they can reproduce diverse neural response behaviors.

Findings

GLMs can reproduce tonic and phasic spiking behaviors.

GLMs capture spike rate adaptation and bistability.

GLMs exhibit stimulus-dependent spike timing variability.

Abstract

A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a set of linear filters, a point nonlinearity, and conditionally Poisson spiking. They have desirable statistical properties for fitting and have been widely used to analyze spike trains from electrophysiological recordings. However, the dynamical repertoire of GLMs has not been systematically compared to that of real neurons. Here we show that GLMs can reproduce a comprehensive suite of canonical neural response behaviors, including tonic and phasic spiking, bursting, spike rate adaptation, type I and type II excitation, and two forms of bistability. GLMs can also capture stimulus-dependent changes in spike timing precision and reliability that mimic those observed in real neurons, and can exhibit varying degrees of stochasticity, from virtually deterministic responses to greater-than-Poisson variability. These results show that Poisson GLMs can exhibit a wide range of dynamic spiking behaviors found in real neurons, making them well suited for qualitative dynamical as well as quantitative statistical studies of single-neuron and population response properties.