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Nonstationarity of the event rate is a persistent problem in modeling timeseries of events, such as neuronal spike trains. Motivated by a variety ofpatterns in neurophysiological spike train recordings, we define a generalclass of renewal processes. This class is used to test the null hypothesis ofstationary rate versus a wide alternative of renewal processes with finitelymany rate changes (change points). Our test extends ideas from the filteredderivative approach by using multiple moving windows simultaneously. To adjustthe rejection threshold of the test, we use a Gaussian process, which emergesas the limit of the filtered derivative process. We also develop a multiplefilter algorithm, which can be used when the null hypothesis is rejected inorder to estimate the number and location of change points. We analyze thebenefits of multiple filtering and its increased detection probability ascompared to a single window approach. Application to spike trains recorded fromdopamine midbrain neurons in anesthetized mice illustrates the relevance of theproposed techniques as preprocessing steps for methods that assume ratestationarity. In over 70% of all analyzed spike trains classified as ratenonstationary, different change points were detected by different window sizes.

A multiple filter test for the detection of rate changes in renewal processes with varying variance