Detecting unitary events without discretization of time

In earlier studies we developed the 'Unitary Events' analysis (Grun S. Unit ary Joint-Events in Multiple-Neuron Spiking Activity: Detection, Significan ce and Interpretation. Reihe Physik, Band 60. Thun, Frankfurt/Main: Verlag Harri Deutsch, 1996.) to detect the presence of conspicuous spike coinciden ces in multiple single unit recordings and to evaluate their statistical si gnificance. The method enabled us to study the relation between spike synch ronization and behavioral events (Riehle A, Grun S, Diesmann M, Aertsen A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 1997;278:1950-1953.). There is recent experiment al evidence that the timing accuracy of coincident spiking events, which mi ght be relevant for higher brain function, may be in the range of 1-5 ms. T o detect coincidences on that time scale, we sectioned the observation inte rval into short disjunct time slices ('bins'). Unitary Events analysis of t his discretized process demonstrated that coincident events can indeed be r eliably detected. However, the method looses sensitivity for higher tempora l jitter of the events constituting the coincidences (Grun S. Unitary Joint -Events in Multiple-Neuron Spiking Activity: Detection, Significance and In terpretation. Reihe Physik, Band 60. Thun, Frankfurt/Main: Verlag Harri Deu tsch, 1996.). Here we present a new approach, the 'multiple shift' method ( MS), which overcomes the need for binning and treats the data in their (ori ginal) high time resolution (typically 1 ms, or better). Technically, coinc idences are detected by shifting the spike trains against each other over t he range of allowed coincidence width and integrating the number of exact c oincidences (on the time resolution of the data) over all shifts. We found that the new method enhances the sensitivity for coincidences with temporal jitter. Both methods are outlined and compared on the basis of their analy tical description and their application on simulated data. The performance on experimental data is illustrated. (C) 1999 Elsevier Science B.V. All rig hts reserved.