Sonja Grün

The mechanisms underlying neuronal information processing and in particular the role of temporal spike coordination are hotly debated. However, the debate is often confounded by an implicit discussion on the use of appropriate analysis methods. To avoid false interpretation of data the analysis of simultaneous spike trains for cooperative neuronal interactions needs to be properly adjusted to the features of experimental data. In particular non-stationarity 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 those features in the null-hypothesis of significance tests. In this context the usage of surrogate data becomes increasingly important, since the complexity of the data typically prevents analytical solutions [1,2]. Thorough testing and calibration of analysis tools is emphasized, also with respect to the impact of potentially erroneous preprocessing stages. The lecture provides an overview of the potential obstacles in the correlation analysis of parallel, also massively parallel, spike data and routes to overcome them [3].

[1] Grün S. Data-driven significance estimation of precise spike correlation. Journal of Neurophysiology, 101, 1126-1140, (2009). (invited review)
[2] Louis S, Gerstein GL, Grün S, Diesmann M. Surrogate spike train generation through dithering in operational time. Frontiers in Computational Neuroscience 4: 127, doi: 10.3389/ fncom.2010.00127 (2010)
[3] Analysis of parallel spike trains. S. Grün and S. Rotter, editors. Springer Series on Computational Neuroscience 106, ISBN 978-1-4419-0377-8 (2010)