Abstract
Currently there are many methods to detect synchronization, each of them trying to extract some specific aspects or oriented to specific number or type of signals. In this paper, we present a new method to detect synchronization for multivariate signals, computationally light and not requiring a combinatorial number of operations on signals differences.The method is based on the Hilbert transform of the signals, which provides their instantaneous phases. The distribution of phases for all signals at a specific time is assimilated to a probability distribution. In this way, we obtain a sequence of probability distributions (one per time unit). Computing the entropy of the probability distributions we get finally a function of entropies along time. The average value of this final function provides a good estimate of the synchronization level of the multivariate signals ensemble, and the function itself can be used as a signature (descriptive function) of the whole multidimensional ensemble dynamics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dauwels, J., Vialatte, F., Cichocki, A.: A Comparative Study of Synchrony Measures for the Early Detection of Alzheimers Disease Based on EEG. Elsevier, NeuroImage 49, 668–693 (2010)
Granger, C.W.J.: Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control 2, 329–352 (1980)
Sellers, P.H.: On the theory and computation of evolutionary distances. SIAM J. Appl. Math. 26, 787–793 (1974)
Plaut, G., Vautard, R.: Spells of Low-Frequency Oscillations and Weather Regimes in the Northern Hemisphere. Journal of Atmospheric Sciences 51(2), 210–236 (1993)
Gillian, N., Knapp, R.B., O’Modhrain, S.: Recognition of Multivariate Temporal Musical Gestures Using N-Dimensional Dynamic Time Warping. In: Proceedings of NIME 2011, Oslo, Norway (May 2011)
Dong, Y., Mihalas, S., Qiu, F., von der Heydt, R., Niebur, E.: Synchrony and the binding problem in macaque visual cortex. Journal of Vision 8(7), 1–16 (2008)
Borisyuk, R., Borisyuk, G.: Information coding on the basis of synchronization of neural activity. BioSystems 40, 3–10 (1997)
Liu, X.F., Tse, C.K.: A complex network perspective of world stock markets: synchronization and volatility. International Journal of Bifurcation and Chaos 22(6) (2012)
Müller, M.: New Developments in Music Information Retrieval. In: Proceedings of the 42nd AES Conference (2011)
Dexter, E., Perez, P., Laptev, I., Junejo, I.N.: Multi-view Synchronization of Human Actions and Dynamic Scenes. In: VISAPP 2009: Proceedings 4th International Conference on Computer Vision Theory and Applications, vol. 2, pp. 383–391 (2009)
Pereda, E., Quiroga, R.Q., Bhattacharya, J.: Nonlinear multivariate analysis of neurophysiological signals. Progress in Neurobiology 77, 1–37 (2005)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)
Izhikevich, E.M.: Simple Model of Spiking Neurons. IEEE Trans. Neural Networks 14(6), 1569–1572 (2003)
Izhikevic, E.M.: Which Model to Use for Cortical Spiking Neurons? IEEE Transactions on Neural Networks 15, 1063–1070 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lopez, M., Rodríguez, F.B. (2013). Detection Method for Phase Synchronization in a Population of Spiking Neurons. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38637-4_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-38637-4_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38636-7
Online ISBN: 978-3-642-38637-4
eBook Packages: Computer ScienceComputer Science (R0)