کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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6269234 | 1295128 | 2012 | 16 صفحه PDF | دانلود رایگان |
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 consequence of these findings we finally propose a strategy to assess the reliability of results for experimental data.
⺠Generation of non-Poissonian spike trains with defined higher-order correlations. ⺠Non-Poissonian spiking can impair the estimation of higher-order correlations. ⺠Correlation measures can be calibrated by the use of this method.
Journal: Journal of Neuroscience Methods - Volume 208, Issue 1, 30 June 2012, Pages 18-33