Monitoring spike train synchrony

TL;DR

This paper improves the SPIKE-distance measure for spike train synchrony by eliminating artifacts, enabling real-time analysis, and demonstrating its effectiveness on neural and EEG data during seizures.

Contribution

It introduces an enhanced, artifact-free SPIKE-distance measure with real-time capabilities and new features for analyzing spike train synchrony.

Findings

Improved measure reduces spurious high values.

Effective in real-time monitoring during epileptic seizures.

Applicable to continuous EEG data.

Abstract

Recently, the SPIKE-distance has been proposed as a parameter-free and time-scale independent measure of spike train synchrony. This measure is time-resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for event-like firing patterns. Here we present a substantial improvement of this measure which eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real-time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.