  Recently, the SPIKE-distance has been proposed as a parameter-free and
time-scale independent measure of spike train synchrony. This measure is
time-resolved since it relies on instantaneous estimates of spike train
dissimilarity. However, its original definition led to spuriously high
instantaneous values for event-like firing patterns. Here we present a
substantial improvement of this measure which eliminates this shortcoming. The
reliability gained allows us to track changes in instantaneous clustering,
i.e., time-localized patterns of (dis)similarity among multiple spike trains.
Additional new features include selective and triggered temporal averaging as
well as the instantaneous comparison of spike train groups. In a second step, a
causal SPIKE-distance is defined such that the instantaneous values of
dissimilarity rely on past information only so that time-resolved spike train
synchrony can be estimated in real-time. We demonstrate that these methods are
capable of extracting valuable information from field data by monitoring the
synchrony between neuronal spike trains during an epileptic seizure. Finally,
the applicability of both the regular and the real-time SPIKE-distance to
continuous data is illustrated on model electroencephalographic (EEG)
recordings.
