Literature DB >> 24206385

A new class of metrics for spike trains.

Cătălin V Rusu1, Răzvan V Florian.   

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 from those of classical metrics like the van Rossum distance or the Victor and 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 do not carry information. The modulus-metric does not depend on any parameters and can be computed using a fast algorithm whose time 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.

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Year:  2013        PMID: 24206385     DOI: 10.1162/NECO_a_00545

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  3 in total

1.  SPIKY: a graphical user interface for monitoring spike train synchrony.

Authors:  Thomas Kreuz; Mario Mulansky; Nebojsa Bozanic
Journal:  J Neurophysiol       Date:  2015-03-04       Impact factor: 2.714

2.  Measuring spike timing distance in the Hindmarsh-Rose neurons.

Authors:  Jinjie Zhu; Xianbin Liu
Journal:  Cogn Neurodyn       Date:  2017-12-27       Impact factor: 5.082

3.  Measures of spike train synchrony for data with multiple time scales.

Authors:  Eero Satuvuori; Mario Mulansky; Nebojsa Bozanic; Irene Malvestio; Fleur Zeldenrust; Kerstin Lenk; Thomas Kreuz
Journal:  J Neurosci Methods       Date:  2017-06-03       Impact factor: 2.390

  3 in total

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