Neural Comput - Impact of spike train autostructure on probability distribution of joint spike events.

Tópicos

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Resumo

The discussion whether temporally coordinated spiking activity really exists and whether it is relevant has been heated over the past few years. To investigate this issue, several approaches have been taken to determine whether synchronized events occur significantly above chance, that is, whether they occur more often than expected if the neurons fire independently. Most investigations ignore or destroy the autostructure of the spiking activity of individual cells or assume Poissonian spiking as a model. Such methods that ignore the autostructure can significantly bias the coincidence statistics. Here, we study the influence of the autostructure on the probability distribution of coincident spiking events between tuples of mutually independent non-Poisson renewal processes. In particular, we consider two types of renewal processes that were suggested as appropriate models of experimental spike trains: a gamma and a log-normal process. For a gamma process, we characterize the shape of the distribution analytically with the Fano factor (FFc). In addition, we perform Monte Carlo estimations to derive the full shape of the distribution and the probability for false positives if a different process type is assumed as was actually present. We also determine how manipulations of such spike trains, here dithering, used for the generation of surrogate data change the distribution of coincident events and influence the significance estimation. We find, first, that the width of the coincidence count distribution and its FFc depend critically and in a nontrivial way on the detailed properties of the structure of the spike trains as characterized by the coefficient of variation CV. Second, the dependence of the FFc on the CV is complex and mostly nonmonotonic. Third, spike dithering, even if as small as a fraction of the interspike interval, can falsify the inference on coordinated firing.

Resumo Limpo

discuss whether tempor coordin spike activ realli exist whether relev heat past year investig issu sever approach taken determin whether synchron event occur signific chanc whether occur often expect neuron fire independ investig ignor destroy autostructur spike activ individu cell assum poissonian spike model method ignor autostructur can signific bias coincid statist studi influenc autostructur probabl distribut coincid spike event tupl mutual independ nonpoisson renew process particular consid two type renew process suggest appropri model experiment spike train gamma lognorm process gamma process character shape distribut analyt fano factor ffc addit perform mont carlo estim deriv full shape distribut probabl fals posit differ process type assum actual present also determin manipul spike train dither use generat surrog data chang distribut coincid event influenc signific estim find first width coincid count distribut ffc depend critic nontrivi way detail properti structur spike train character coeffici variat cv second depend ffc cv complex most nonmonoton third spike dither even small fraction interspik interv can falsifi infer coordin fire

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