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NeuroXidence : A Non-parametric Test on Excess or Deficiency of Joint-spike Events

Acknowledgements: The authors wish to thank to Sonja Grün and Emery Brown for the fruitful discussions on this project. Also, Gordon Pipa would like to thank to his wife Gabriela Pipa and his family for the great support. ABSTRACT We present a non-parametric and computationally-efficient method named NeuroXidence that detects coordinated firing of two or more neurons and tests whether the observed level of coordinated firing is significantly different from that expected by chance. The method considers the full auto-structure of the data, including the changes in the rate responses and the history dependencies in the spiking activity. Also, the method accounts for trial-by-trial variability in the dataset, such as the variability of the rate responses and their latencies. NeuroXidence can be applied to short data windows lasting only tens of milliseconds, which enables the tracking of transient neuronal states correlated to information processing. We demonstrate, on both toy-data and single-unit activity recorded in cat cortex, that NeuroXidence discriminates reliably between significant and spurious events that occur by chance.