Abstract
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 and temporally distributed spiking activity on various timescales enables the immediate tracking of diverse qualities of coordinated firing related to neuronal state changes and information processing. We apply the method to simulated data and multineuronal recordings from rat visual cortex and show that it reliably discriminates between data sets with random pattern occurrences and with additional exactly repeating spatiotemporal patterns and pattern sequences. Multineuronal cortical spiking activity appears to be precisely coordinated and exhibits a sequential organization beyond the cell assembly concept.
Highlights
One of the most fundamental issues in neuroscience is the nature of the neural representation of information
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
We apply the method to simulated data and multineuronal recordings from rat visual cortex and show that it reliably discriminates between data sets with random pattern occurrences and with additional exactly repeating spatiotemporal patterns and pattern sequences
Summary
One of the most fundamental issues in neuroscience is the nature of the neural representation of information. To achieve a comprehensive and conceptually unrestricted description of multineuronal spiking, we present a new method for analyzing consistent relations between discharges of simultaneously recorded neurons on arbitrary timescales that are referred to as spatiotemporal firing patterns and pattern sequences (Gansel and Singer, 2006). A new type of surrogate data is introduced that allows for variability and sparseness of spiking events and is superior to common resampling methods in terms of statistical test performance. Another difficulty when searching for recurring spatiotemporal patterns in massively parallel recordings arises from the mutual masking of unrelated patterns that are arranged in the same window. To avoid the combinatorial explosion that results from testing every single possible subpattern, we propose an algorithm that separates coincident events based on the preferences with
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