title: Detecting unitary events without discretization of time.
abstract: In earlier studies we developed the 'Unitary Events' analysis (Grün S. Unitary Joint-Events in Multiple-Neuron Spiking Activity: Detection, Significance and Interpretation. Reihe Physik, Band 60. Thun, Frankfurt/Main: Verlag Harri Deutsch, 1996.) to detect the presence of conspicuous spike coincidences in multiple single unit recordings and to evaluate their statistical significance. The method enabled us to study the relation between spike synchronization and behavioral events (Riehle A, Grün S, Diesmann M, Aertsen A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 1997;278:1950-1953.). There is recent experimental evidence that the timing accuracy of coincident spiking events, which might be relevant for higher brain function, may be in the range of 1-5 ms. To detect coincidences on that time scale, we sectioned the observation interval into short disjunct time slices ('bins'). Unitary Events analysis of this discretized process demonstrated that coincident events can indeed be reliably detected. However, the method looses sensitivity for higher temporal jitter of the events constituting the coincidences (Grün S. Unitary Joint-Events in Multiple-Neuron Spiking Activity: Detection, Significance and Interpretation. Reihe Physik, Band 60. Thun, Frankfurt/Main: Verlag Harri Deutsch, 1996.). Here we present a new approach, the 'multiple shift' method (MS), which overcomes the need for binning and treats the data in their (original) high time resolution (typically 1 ms, or better). Technically, coincidences are detected by shifting the spike trains against each other over the range of allowed coincidence width and integrating the number of exact coincidences (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 analytical description and their application on simulated data. The performance on experimental data is illustrated.
authors.affiliation: Department of Neurophysiology, Max-Planck-Institute for Brain Research, Frankfurt/Main, Germany. gruen@mpih-frankfurt.mpg.de
authors.forename: S
authors.initials: S
authors.lastname: Grün
authors.forename: M
authors.initials: M
authors.lastname: Diesmann
authors.forename: F
authors.initials: F
authors.lastname: Grammont
authors.forename: A
authors.initials: A
authors.lastname: Riehle
authors.forename: A
authors.initials: A
authors.lastname: Aertsen
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D003198:Computer Simulation; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013997:Time Factors