Modeling and analyzing higher-order correlations in non-Poissonian spike trains

Journal of Neuroscience Methods
Imke C G ReimerStefan Rotter

Abstract

Measuring pairwise and higher-order spike correlations is crucial for studying their potential impact on neuronal information processing. In order to avoid misinterpretation of results, the tools used for data analysis need to be carefully calibrated with respect to their sensitivity and robustness. This, in turn, requires surrogate data with statistical properties common to experimental spike trains. Here, we present a novel method to generate correlated non-Poissonian spike trains and study the impact of single-neuron spike statistics on the inference of higher-order correlations. Our method to mimic cooperative neuronal spike activity allows the realization of a large variety of renewal processes with controlled higher-order correlation structure. Based on surrogate data obtained by this procedure we investigate the robustness of the recently proposed method empirical de-Poissonization (Ehm et al., 2007). It assumes Poissonian spiking, which is common also for many other estimation techniques. We observe that some degree of deviation from this assumption can generally be tolerated, that the results are more reliable for small analysis bins, and that the degree of misestimation depends on the detailed spike statistics. As a c...Continue Reading

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Citations

Mar 12, 2013·Journal of Computational Neuroscience·Imke C G ReimerStefan Rotter
Apr 9, 2014·Neural Computation·Christine Tuleau-MalotPatricia Reynaud-Bouret
Aug 25, 2015·Biometrical Journal. Biometrische Zeitschrift·Julien Chevallier, Thomas Laloë
May 13, 2017·Journal of Neuroscience Methods·Matthias GärtnerGaby Schneider
Dec 19, 2016·Journal of the American Statistical Association·Bo ZhouBabak Shahbaba
Apr 14, 2018·Biological cybernetics·Pietro QuaglioSonja Grün

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