Assessment of synchrony in multiple neural spike trains using loglinear point process models

TL;DR

This paper develops a new class of time-varying loglinear models to analyze synchrony in neural spike trains, accounting for nonstationarity, history effects, and smooth temporal variation in dependencies.

Contribution

It introduces a flexible, nonstationary loglinear modeling framework for neural spike train synchrony analysis, extending previous methods that assumed stationarity.

Findings

Models accommodate time-varying intensities and history effects.

Assumes synchrony effects are independent of history.

All effects are modeled to vary smoothly over time.

Abstract

Neural spike trains, which are sequences of very brief jumps in voltage across the cell membrane, were one of the motivating applications for the development of point process methodology. Early work required the assumption of stationarity, but contemporary experiments often use time-varying stimuli and produce time-varying neural responses. More recently, many statistical methods have been developed for nonstationary neural point process data. There has also been much interest in identifying synchrony, meaning events across two or more neurons that are nearly simultaneous at the time scale of the recordings. A natural statistical approach is to discretize time, using short time bins, and to introduce loglinear models for dependency among neurons, but previous use of loglinear modeling technology has assumed stationarity. We introduce a succinct yet powerful class of time-varying loglinear models by (a) allowing individual-neuron effects (main effects) to involve time-varying intensities; (b) also allowing the individual-neuron effects to involve autocovariation effects (history effects) due to past spiking, (c) assuming excess synchrony effects (interaction effects) do not depend on history, and (d) assuming all effects vary smoothly across time.