A new class of metrics for spike trains

TL;DR

This paper introduces a new class of spike train metrics inspired by the Pompeiu-Hausdorff distance, focusing on burst-based information encoding, with efficient computation and localized versions for temporal analysis.

Contribution

The authors propose novel spike train metrics, the modulus-metric and max-metric, that are parameter-free, computationally efficient, and suitable for burst-based neural coding.

Findings

Modulus-metric and max-metric differ qualitatively from classical metrics.

New metrics are parameter-free and computationally fast.

Localized versions provide temporal perception insights.

Abstract

The distance between a pair of spike trains, quantifying the differences between them, can be measured using various metrics. Here we introduce a new class of spike train metrics, inspired by the Pompeiu-Hausdorff distance, and compare them with existing metrics. Some of our new metrics (the modulus-metric and the max-metric) have characteristics that are qualitatively different than those of classical metrics like the van Rossum distance or the Victor & Purpura distance. The modulus-metric and the max-metric are particularly suitable for measuring distances between spike trains where information is encoded in bursts, but the number and the timing of spikes inside a burst does not carry information. The modulus-metric does not depend on any parameters and can be computed using a fast algorithm, in a time that depends linearly on the number of spikes in the two spike trains. We also introduce localized versions of the new metrics, which could have the biologically-relevant interpretation of measuring the differences between spike trains as they are perceived at a particular moment in time by a neuron receiving these spike trains.