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Detecting Multineuronal Temporal Patterns in Parallel Spike Trains
2012
Frontiers in Neuroinformatics
We present a non-parametric and computationally efficient method that detects spatiotemporal firing patterns and pattern sequences in parallel spike trains and tests whether the observed numbers of repeating patterns and sequences on a given timescale are significantly different from those expected by chance. The method is generally applicable and uncovers coordinated activity with arbitrary precision by comparing it to appropriate surrogate data. The analysis of coherent patterns of spatially
doi:10.3389/fninf.2012.00018
pmid:22661942
pmcid:PMC3357495
fatcat:frbmsmufwjcohd3aydqmajy3by