Data-driven significance estimation for precise spike correlation.

Journal of Neurophysiology
Sonja Grün

Abstract

The mechanisms underlying neuronal coding and, in particular, the role of temporal spike coordination are hotly debated. However, this debate is often confounded by an implicit discussion about the use of appropriate analysis methods. To avoid incorrect interpretation of data, the analysis of simultaneous spike trains for precise spike correlation needs to be properly adjusted to the features of the experimental spike trains. In particular, nonstationarity of the firing of individual neurons in time or across trials, a spike train structure deviating from Poisson, or a co-occurrence of such features in parallel spike trains are potent generators of false positives. Problems can be avoided by including these features in the null hypothesis of the significance test. In this context, the use of surrogate data becomes increasingly important, because the complexity of the data typically prevents analytical solutions. This review provides an overview of the potential obstacles in the correlation analysis of parallel spike data and possible routes to overcome them. The discussion is illustrated at every stage of the argument by referring to a specific analysis tool (the Unitary Events method). The conclusions, however, are of a genera...Continue Reading

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Citations

May 19, 2010·Journal of Computational Neuroscience·Michael DenkerSonja Grün
Sep 13, 2012·Journal of Computational Neuroscience·Nahal SharafiBenjamin Lindner
Apr 25, 2012·Proceedings of the National Academy of Sciences of the United States of America·Valérie Ventura, Richard C Gerkin
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Mar 14, 2012·PLoS Computational Biology·Hideaki ShimazakiSonja Grün
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