Measuring spike train correlation with non-parametric statistics coefficient

Soletta, Jorge Humberto
Farfan, Fernando Daniel
Felice, Carmelo Jose

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Publication date

December 2015

DOI

10.1109/TLA.2015.7404902

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

ISSN

1548-0992

Journal

IEEE Latin America Transactions

Citation count (estimate)

Abstract

Measure correlation between spike trains is a fundamental step for the study of neural systems. There are many alternatives to measure correlation, but not all possess the required properties. In this paper we propose to use non-parametric coefficients of correlation, coefficients Spearman and Kendall. To analyze their properties were generated computationally trains of spikes that simulate different experimental conditions, then the proposed coefficients were calculated and compared with the Pearson coefficient. The results show that under certain experimental conditions Kendall coefficient is more appropriate to quantify correlations between spikes trains.Fil: Soletta, Jorge Humberto. Consejo Nacional de Investigaciones Científicas y Técn...

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Measuring spike train correlation with non-parametric statistics coefficient

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Measuring spike train correlation with non-parametric statistics coefficient

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