Detection and localization of multiple rate changes in Poisson spike trains
© Messer et al; licensee BioMed Central Ltd. 2011
Published: 18 July 2011
In statistical spike train analysis, stochastic point process models usually assume stationarity, in particular that the underlying spike train shows a constant firing rate (e.g. ). However, such models can lead to misinterpretation of the associated tests if the assumption of rate stationarity is not met (e.g. ). Therefore, the analysis of nonstationary data requires that rate changes can be located as precisely as possible. However, present statistical methods focus on rejecting the null hypothesis of stationarity without explicitly locating the change point(s) (e.g. ).
We propose a test for stationarity of a given spike train that can also be used to estimate the change points in the firing rate. Assuming a Poisson process with piecewise constant firing rate, we propose a Step-Filter-Test (SFT) which can work simultaneously in different time scales, accounting for the high variety of firing patterns in experimental spike trains. Formally, we compare the numbers N1=N1(t,h) and N2=N2(t,h) of spikes in the time intervals (t-h,t] and (h,t+h]. By varying t within a fine time lattice and simultaneously varying the interval length h, we obtain a multivariate statistic D(h,t):=(N1-N2)/√(N1+N2), for which we prove asymptotic multivariate normality under homogeneity. From this a practical, graphical device to spot changes of the firing rate is constructed.
We thank Brooks Ferebee for stimulating discussions. This work was supported by the LOEWE-Schwerpunkt “Neuronale Koordination Forschungsschwerpunkt Frankfurt” (MM, JR), by the BMBF Project Bernstein Fokus: Neurotechnologie Frankfurt, FKZ 01GQ0841 (MB) and by the Deutsche Forschungsgemeinschaft SFB 815 (JR).
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