Quantifying Statistical Interdependence PART III: n > 2 Multi-Dimensional Point Processes

Stochastic event synchrony (SES) is a technique to quantify the similarity of pairs of signals. In this paper (Part III), SES is extended from pairs of signals to collections of signals. As in Part I and II, first “events” are extracted from the given time series. Next, one tries to align events from one time series with events from the other. The better the alignment, the more similar the collection of time series is considered to be. As in Part II, this paper deals with multi-dimensional events. Although the basic idea is similar to the pairwise case, the extension to collection of point processes involves an NP-hard combinatorial problem, and therefore, it is far from trivial.

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