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Background: Measures of spike train synchrony are widely used in bothexperimental and computational neuroscience. Time-scale independent andparameter-free measures, such as the ISI-distance, the SPIKE-distance andSPIKE-synchronization, are preferable to time-scale parametric measures, sinceby adapting to the local firing rate they take into account all the time-scalesof a given dataset.  New Method: In data containing multiple time-scales (e.g. regular spiking andbursts) one is typically less interested in the smallest time-scales and a moreadaptive approach is needed. Here we propose the A-ISI-distance, theA-SPIKE-distance and A-SPIKE-synchronization, which generalize the originalmeasures by considering the local relative to the global time-scales. For theA-SPIKE-distance we also introduce a rate-independent extension called theRIA-SPIKE-distance, which focuses specifically on spike timing.  Results: The adaptive generalizations A-ISI-distance and A-SPIKE-distanceallow to disregard spike time differences that are not relevant on a moreglobal scale. A-SPIKE-synchronization does not any longer demand anunreasonably high accuracy for spike doublets and coinciding bursts. Finally,the RIA-SPIKE-distance proves to be independent of rate ratios between spiketrains.  Comparison with Existing Methods: We find that compared to the originalversions the A-ISI-distance and the A-SPIKE-distance yield improvements forspike trains containing different time-scales without exhibiting any unwantedside effects in other examples. A-SPIKE-synchronization matches spikes moreefficiently than SPIKE-Synchronization.  Conclusions: With these proposals we have completed the picture, since we nowprovide adaptive generalized measures that are sensitive to rate only(A-ISI-distance), to timing only (ARI-SPIKE-distance), and to both at the sametime (A-SPIKE-distance).

Measures of spike train synchrony for data with multiple time-scales