Abstract
Measures of spike train synchrony have become important tools in both experimental and theoretical neuroscience. Three time-resolved measures called the ISI-distance, the SPIKE-distance, and SPIKE-synchronization have already been successfully applied in many different contexts. These measures are time scale independent, since they consider all time scales as equally important. However, in real data one is typically less interested in the smallest time scales and a more adaptive approach is needed. Therefore, in the first part of this Chapter we describe recently introduced generalizations of the three measures, that gradually disregard differences in smaller time-scales. Besides similarity, another very relevant property of spike trains is the temporal order of spikes. In the second part of this chapter we address this property and describe a very recently proposed algorithm, which quantifies the directionality within a set of spike train. This multivariate approach sorts multiple spike trains from leader to follower and quantifies the consistency of the propagation patterns. Finally, all measures described in this chapter are freely available for download.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
http://www.fi.isc.cnr.it/users/thomas.kreuz/Source-Code/SPIKY.html.
- 2.
http://www.fi.isc.cnr.it/users/thomas.kreuz/Source-Code/cSPIKE.html.
- 3.
http://mariomulansky.github.io/PySpike.
References
Andrzejak, R.G., Kreuz, T.: Characterizing unidirectional couplings between point processes and flows. Europhys. Lett. 96, 50012 (2011)
Applegate, D.L., Bixby, R.E., Chvatal, V., Cook, W.J.: The Traveling Salesman Problem: A Computational Study. Princeton University Press, Princeton (2011)
Boers, N., Bookhagen, B., Barbosa, H.M.J., Marwan, N., Kurths, J., Marengo, J.A.: Prediction of extreme floods in the eastern central andes based on a complex networks approach. Nat. Commun. 5, 5199 (2014)
Bower, M.R., Stead, M., Meyer, F.B., Marsh, W.R., Worrell, G.A.: Spatiotemporal neuronal correlates of seizure generation in focal epilepsy. Epilepsia 53, 807 (2012)
Chicharro, D., Andrzejak, R.G.: Reliable detection of directional couplings using rank statistics. Phys. Rev. E 80(2), 026217 (2009)
Chicharro, D., Kreuz, T., Andrzejak, R.G.: What can spike train distances tell us about the neural code? J. Neurosci. Methods 199, 146–165 (2011)
Dowsland, K.A., Thompson, J.M.: Simulated annealing. In: Handbook of Natural Computing, pp. 1623–1655. Springer, Berlin (2012)
Dura-Bernal, S., Li, K., Neymotin, S.A., Francis, J.T., Principe, J.C., Lytton, W.W.: Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm. Front. Neurosci. 10, 28 (2016)
Espinal, A., Rostro-Gonzalez, H., Carpio, M., Guerra-Hernandez, E.I., Ornelas-Rodriguez, M., Puga-Soberanes, H.J., Sotelo-Figuero, M.A., Melin, P.: Quadrupedal robot locomotion: a biologically inspired approach and its hardware implementation. Comput. Intell. Neurosci. 2016, 5615618 (2016)
Jolivet, R., Kobayashi, R., Rauch, A., Naud, R., Shinomoto, S., Gerstner, W.: A benchmark test for a quantitative assessment of simple neuron models. J. Neurosci. Methods 169, 417 (2008)
Kreuz, T.: Synchronization measures. In: Quian Quiroga, R., Panzeri, S. (eds.) Principles of Neural Coding, p. 97. CRC Taylor and Francis, Boca Raton, FL (2013)
Kreuz, T., Haas, J.S., Morelli, A., Abarbanel, H.D.I., Politi, A.: Measuring spike train synchrony. J. Neurosci. Methods 165, 151 (2007)
Kreuz, T., Mormann, F., Andrzejak, R.G., Kraskov, A., Lehnertz, K., Grassberger, P.: Measuring synchronization in coupled model systems: a comparison of different approaches. Phys. D 225, 29 (2007)
Kreuz, T., Chicharro, D., Greschner, M., Andrzejak, R.G.: Time-resolved and time-scale adaptive measures of spike train synchrony. J. Neurosci. Methods 195, 92 (2011)
Kreuz, T., Chicharro, D., Houghton, C., Andrzejak, R.G., Mormann, F.: Monitoring spike train synchrony. J. Neurophysiol. 109, 1457 (2013)
Kreuz, T., Mulansky, M., Bozanic, N.: SPIKY: a graphical user interface for monitoring spike train synchrony. J. Neurophysiol. 113, 3432 (2015)
Kreuz, T., Satuvuori, E., Pofahl, M., Mulansky, M.: Leaders and followers: quantifying consistency in spatio-temporal propagation patterns. New J. Phys. 19, 043028 (2017)
Mainen, Z., Sejnowski, T.J.: Reliability of spike timing in neocortical neurons. Science 268, 1503 (1995)
Malvestio, I., Kreuz, T., Andrzejak, RG: Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains. Phys. Rev. E 96, 022203 (2017)
Mulansky, M., Kreuz, T.: Pyspike - a python library for analyzing spike train synchrony. Softw. X 5, 183–189 (2016)
Pereda, E., Quian Quiroga, R., Bhattacharya, J.: Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol. 77, 1 (2005)
Quian Quiroga, R., Kreuz, T., Grassberger, P.: Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. Phys. Rev. E 66, 041904 (2002)
Rahbar, F., Anzalone, S., Varni, G., Zibetti, E., Ivaldi, S., Chetouani, M.: Predicting extraversion from non-verbal features during a face-to-face human-robot interaction. In: International Conference on Social Robotics, p. 10 (2015)
Satuvuori, E., Mulansky, M., Bozanic, N., Malvestio, I., Zeldenrust, F., Lenk, K., Kreuz, T.: Measures of spike train synchrony for data with multiple time-scales. J. Neurosci. Methods 287, 25 (2017)
Truccolo, W., Donoghue, J.A., Hochberg, L.R., Eskandar, E.N., Madsen, J.R., Anderson, W.S., Brown, E.N., Halgren, E., Cash, S.S.: Single-neuron dynamics in human focal epilepsy. Nat. Neurosci. 14, 635 (2011)
van Rossum, M.C.W.: A novel spike distance. Neural Comput. 13, 751 (2001)
Varni, G., Volpe, G., Camurri, A.: A system for real-time multimodal analysis of nonverbal affective social interaction in user-centric media. IEEE Trans. Multimedia 12, 576 (2010)
Victor, J.D.: Spike train metrics. Curr. Opin. Neurobiol. 15, 585 (2005)
Victor, J.D., Purpura, K.P.: Nature and precision of temporal coding in visual cortex: a metric-space analysis. J. Neurophysiol. 76, 1310 (1996)
Acknowledgements
We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. #642563 ‘Complex Oscillatory Systems: Modeling and Analysis’ (COSMOS). T.K. also acknowledges support from the European Commission through Marie Curie Initial Training Network ‘Neural Engineering Transformative Technologies’ (NETT), project 289146. We thank Ralph G. Andrzejak, Nebojsa Bozanic, Kerstin Lenk, Mario Mulansky, and Martin Pofahl for useful discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Satuvuori, E., Malvestio, I., Kreuz, T. (2017). Measures of Spike Train Synchrony and Directionality. In: Naldi, G., Nieus, T. (eds) Mathematical and Theoretical Neuroscience. Springer INdAM Series, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-68297-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-68297-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68296-9
Online ISBN: 978-3-319-68297-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)