title: Multi-scale detection of rate changes in spike trains with weak dependencies.
abstract: The statistical analysis of neuronal spike trains by models of point processes often relies on the assumption of constant process parameters. However, it is a well-known problem that the parameters of empirical spike trains can be highly variable, such as for example the firing rate. In order to test the null hypothesis of a constant rate and to estimate the change points, a Multiple Filter Test (MFT) and a corresponding algorithm (MFA) have been proposed that can be applied under the assumption of independent inter spike intervals (ISIs). As empirical spike trains often show weak dependencies in the correlation structure of ISIs, we extend the MFT here to point processes associated with short range dependencies. By specifically estimating serial dependencies in the test statistic, we show that the new MFT can be applied to a variety of empirical firing patterns, including positive and negative serial correlations as well as tonic and bursty firing. The new MFT is applied to a data set of empirical spike trains with serial correlations, and simulations show improved performance against methods that assume independence. In case of positive correlations, our new MFT is necessary to reduce the number of false positives, which can be highly enhanced when falsely assuming independence. For the frequent case of negative correlations, the new MFT shows an improved detection probability of change points and thus, also a higher potential of signal extraction from noisy spike trains.
authors.affiliation: Institute of Mathematics, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Messer
authors.affiliation: Institute of Neurophysiology, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany.
authors.forename: Kauê M
authors.initials: KM
authors.lastname: Costa
authors.affiliation: Institute of Neurophysiology, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany.
authors.forename: Jochen
authors.initials: J
authors.lastname: Roeper
authors.affiliation: Institute of Mathematics, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany. schneider@math.uni-frankfurt.de.
authors.forename: Gaby
authors.identifier: http://orcid.org/0000-0001-5791-6405
authors.initials: G
authors.lastname: Schneider
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D011336:Probability
keywords: Change point detection; Multi scale; Non-stationarity; Point processes; Serial correlation; Spike train analysis