Neural Comput - A new class of metrics for spike trains.

Tópicos

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Resumo

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.

Resumo Limpo

distanc pair spike train quantifi differ can measur use various metric introduc new class spike train metric inspir pompeiuhausdorff distanc compar exist metric new metric modulusmetr maxmetr characterist qualit differ classic metric like van rossum distanc victor purpura distanc modulusmetr maxmetr particular suitabl measur distanc spike train inform encod burst number time spike insid burst carri inform modulusmetr depend paramet can comput use fast algorithm whose time depend linear number spike two spike train also introduc local version new metric biolog relev interpret measur differ spike train perceiv particular moment time neuron receiv spike train

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