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Volume 16 Supplement 1

24th Annual Computational Neuroscience Meeting: CNS*2015

Time-resolved and parameter-free measures of spike train synchrony: properties and applications

BMC Neuroscience volume 16, Article number: P133 (2015) Cite this article

The relevance of exact spike timings in neural coding was presumed since a long time and has now been experimentally established, see e.g. in [1, 2]. A popular approach to the analysis of spike timings is to measure the synchrony of spike trains. With the recent advancements of the experimental techniques, it is now possible to simultaneously record the activity of hundreds of neurons. The analysis of such collective responses requires new mathematical tools that are able to detect synchrony in groups of spike trains. Here, we present three methods to quantify spike train synchrony that are applicable in such multivariate situations. All of these methods are parameter-free and time-resolved which makes them easy to handle and able to detect temporal changes of synchrony.

Specifically, we discuss the ISI-distance [3], the SPIKE-distance [4] and the very recently proposed SPIKE-Synchronization [5]. The ISI-distance is based on the relative differences of interspike intervals, while the SPIKE-distance uses exact spike timings. SPIKE-Synchronization can be understood as a time-resolved, spike-wise coincidence detector. Figure 1 shows exemplarily the time-resolved profiles of all three methods for 50 artificially created spike trains.

Figure 1
figure 1

Multivariate ISI, SPIKE and SPIKE-Synchronization profiles for 50 artificially generated spike trains. The dashed lines represent the respective expectation values for random Poisson spike trains.

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We analyze the mathematical properties of all three measures and discuss their advantages and disadvantages [6]. Specific focus lies on the statistical relevance of the obtained values compared to random spike trains. By calculating the expectation values for Poisson spike trains we are able to provide an important point of reference for interpreting numerical and experimental results. Finally, we show exemplary applications of the methods to spike trains obtained from numerical simulations [7] as well as experimental recordings [4].

The methods are implemented in both the Matlab-based graphical user interface SPIKY [5] and the Python library PySpike.

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Acknowledgements

This work was supported by the European Commission through the Marie Curie Initial Training Network Neural Engineering Transformative Technologies (NETT), project number 289146 and through the European Joint Doctorate Complex oscillatory systems: Modeling and Analysis (COSMOS), project number 642563 (TK).

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Authors and Affiliations

  1. Institute for Complex Systems, CNR, Sesto Fiorentino, 50019, Italy

    Mario Mulansky, Nebojsa Bozanic & Thomas Kreuz

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  1. Mario Mulansky
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  2. Nebojsa Bozanic
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  3. Thomas Kreuz
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Correspondence to Mario Mulansky.

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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.

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Mulansky, M., Bozanic, N. & Kreuz, T. Time-resolved and parameter-free measures of spike train synchrony: properties and applications. BMC Neurosci 16 (Suppl 1), P133 (2015). https://doi.org/10.1186/1471-2202-16-S1-P133

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  • DOI: https://doi.org/10.1186/1471-2202-16-S1-P133

Keywords

  • Graphical User Interface
  • Spike Train
  • Statistical Relevance
  • Mathematical Tool
  • Popular Approach

BMC Neuroscience

ISSN: 1471-2202

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