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Time-resolved and parameter-free measures of spike train synchrony: properties and applications
BMC Neuroscience volume 16, Article number: P133 (2015)
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 , the SPIKE-distance  and the very recently proposed SPIKE-Synchronization . 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.
We analyze the mathematical properties of all three measures and discuss their advantages and disadvantages . 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  as well as experimental recordings .
The methods are implemented in both the Matlab-based graphical user interface SPIKY  and the Python library PySpike.
Tiesinga P, Fellous J-M, Sejnowski TJ: Regulation of spike timing in visual cortical circuits. Nature Reviews Neuroscience. 2008, 9: 97-107.
Shlens J, Rieke F, Chichilnisky EJ: Synchronized firing in the retina. Current opinion in Neurobiology. 2008, 18 (4): 396-402.
Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A: Measuring spike train synchrony. J Neurosci Methods. 2007, 165: 151-
Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F: Monitoring spike train synchrony. J Neurophysiol. 2013, 109: 1457-
Kreuz T, Mulansky M, Bozanic N: SPIKY: A graphical user interface for monitoring spike train synchrony. ArXiv. 2015, 1410.6910v2 (Submitted to JNeurophysiol)
Mulansky M, Bozanic N, Sburlea A, Kreuz T: A guide to time-resolved and parameter-free measures of spike train synchrony. Arxiv. 2015, 1502.02027. (Submitted to IEEE)
Chen Y, Zhang H, Wang H, Chen Y: The role of coincidence-detector neurons in the reliability and precision of subthreshold signal detection in noise. PloS one. 2013, 8 (2): e56822-
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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Mulansky, M., Bozanic, N. & Kreuz, T. Time-resolved and parameter-free measures of spike train synchrony: properties and applications. BMC Neurosci 16, P133 (2015). https://doi.org/10.1186/1471-2202-16-S1-P133
- Graphical User Interface
- Spike Train
- Statistical Relevance
- Mathematical Tool
- Popular Approach