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| Hauptseite > Publikationsdatenbank > Indications of Higher-Order Correlations in a Pairwise Population Measure |
| Hauptseite > Publikationsdatenbank > Indications of Higher-Order Correlations in a Pairwise Population Measure |
| Poster (Other) | FZJ-2015-02379 |
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2015
Abstract: The Unitary Event (UE) analysis method was designed to detect excess spike synchrony in parallel spike trains as an indicator of assembly activity [1]. The application of the method to simultaneous recordings from cortical neurons provided evidence for occurrence of excess synchrony related to behavior [2]. However, the UE analysis does not scale to arbitrary cell assemblies in massively parallel spike trains (MPST, e.g. 100 or more neurons) due to the combinatorial explosion of occurring spike patterns, and thus the consequent massive multiple testing problem.Here we present an extended UE analysis that is applicable to MPST with acceptable computational effort. It provides indications of the presence of higher-order synchrony (HOS) in a time dependent manner. The extension is based on a population measure derived from pairwise measures, namely the sum of the empirical pairwise coincidences from all neuron pairs nemppop and the expected number of coincidences nexppop. The latter is calculated for all neuron pairs on the basis of their firing rates under assumption of mutual independence. The significance of the nemppop given nexppop is derived by the p-value ppop, and accordingly the surprise Spop=log(1-ppop)/ppop. Given recordings from multiple trials, we define the Fano factor FFpop as the variance of Spop across trials divided by its mean. For calibration of the measures, we use a compound Poisson process (CPP) [3] to model parallel spike trains that possess a) an arbitrary order of HOS, b) a specific average pairwise correlation, and c) a given firing rate distribution across the neurons. We find that the average pairwise correlation is reflected by the mean of Spop across trials, whereas the order of the synchrony by FFpop. As the measures provide reliable statistics on a short time scale (~100 ms), we use them to track the temporal changes in correlation order and average pairwise correlation (Figure). Furthermore, our measures show robustness against the heterogeneity of experimental data, in particular non-homogeneous firing rates across neurons and trials, and non-stationary firing rates in time.Our method thus suggests a way to detect temporal variation of the correlation order and the average pairwise correlation in MPST. We currently work on a comparison of our analysis to existing methods such as CuBIC [3], which infers the lower bound of the order of synchrony, in terms of reliability with respect to the amount of available data.
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