Volume 16 Supplement 1

24th Annual Computational Neuroscience Meeting: CNS*2015

Open Access

Joint pausiness in parallel spike trains

  • Matthias Gärtner1Email author,
  • Sevil Duvarci2,
  • Jochen Roeper2 and
  • Gaby Schneider1
BMC Neuroscience201516(Suppl 1):P218


Published: 18 December 2015

So-called 'pauses', i.e., periods with surprisingly few spikes, have recently gained increasing attention in the analysis of parallel spike trains of dopaminergic (DA) and Purkinje cells, in particular concerning simultaneity of pausing activity. The analysis of simultaneous pauses is usually based on the pauses identified in the separate spike trains. As a consequence, such techniques can suffer from the local definition of a pause within one spike train and can thus fail to identify joint pauses across spike trains that are easily detectable by eye. In addition, they crucially depend on the algorithm used for pause detection.

In order to tackle this problem, we present a new statistical method for the detection of synchronous pauses that focuses on typical characteristics of time periods showing synchronous pauses in parallel spike trains, and introduce a new measure for synchronous pausiness in parallel spike trains. We apply the technique to a data set of parallel DA neurons recorded from the VTA in freely moving mice. Interestingly, pausiness can be significantly increased in parallel spike trains as compared to individual processes or processes shifted by small time lags. This observation is robust and practically independent from the algorithm used for pause detection.



This work was supported by the Priority Program 1665 of the DFG (SD, MG, JR, GS) and by the German Federal Ministry of Education and Research (BMBF, Funding number: 01ZX1404B; GS).

Authors’ Affiliations

Institute for Mathematics, Goethe-University
Neuroscience Center, Institute of Neurophysiology, Goethe-University


© Gärtner et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.