Measuring multiple spike train synchrony

TL;DR

This paper introduces two extensions of the ISI-distance for measuring multiple spike train synchrony, demonstrating their advantages in real and simulated neural data over existing methods.

Contribution

The paper proposes two new parameter-free, time scale independent measures of spike train synchrony, extending the ISI-distance to improve performance and visualization.

Findings

Averaged bivariate measures outperform multivariate ones.

Multivariate ISI-diversity is the best among multivariate methods.

Instantaneous measures outperform window-based methods in real data.

Abstract

Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals. In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.