Cross nearest-spike interval based method to measure synchrony dynamics
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Department of Mathematics, Facultad de Informática, Campus de Elviña s/n, 15071, Universidade da Coruña, A Coruña
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2.
Interuniversity Institute for Biostatistics and statistical Bionformatics, Hasselt University and KULeuven, Hasselt
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3.
Neuroscience and Motor Control Group (NEUROcom), Department of Medicine, Facultad de Ciencias de la Salud, Campus de Oza s/n, 15006, Universidade da Coruña, A Coruña
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Received:
01 December 2012
Accepted:
29 June 2018
Published:
01 September 2013
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MSC :
Primary: 58F15, 58F17; Secondary: 53C35.
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A new synchrony index for neural activity is defined in this paper. The method is able to measure synchrony dynamics in low firing rate scenarios. It is based on the computation of the time intervals between nearest spikes of two given spike trains. Generalized additive models are proposed for the synchrony profiles obtained by this method. Two hypothesis tests are proposed to assess for differences in the level of synchronization in a real data example. Bootstrap methods are used to calibrate the distribution of the tests. Also, the expected synchrony due to chance is computed analytically and by simulation to assess for actual synchronization.
Citation: Aldana M. González Montoro, Ricardo Cao, Christel Faes, Geert Molenberghs, Nelson Espinosa, Javier Cudeiro, Jorge Mariño. Cross nearest-spike interval based method to measure synchrony dynamics[J]. Mathematical Biosciences and Engineering, 2014, 11(1): 27-48. doi: 10.3934/mbe.2014.11.27
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Abstract
A new synchrony index for neural activity is defined in this paper. The method is able to measure synchrony dynamics in low firing rate scenarios. It is based on the computation of the time intervals between nearest spikes of two given spike trains. Generalized additive models are proposed for the synchrony profiles obtained by this method. Two hypothesis tests are proposed to assess for differences in the level of synchronization in a real data example. Bootstrap methods are used to calibrate the distribution of the tests. Also, the expected synchrony due to chance is computed analytically and by simulation to assess for actual synchronization.
References
|
[1]
|
The Journal of Neuroscience, 22 (2002), 8691-8704.
|
|
[2]
|
Studi in Onore del Professore Salvatore Ortu Carboni, Rome, (1935), 13-60.
|
|
[3]
|
Nature Neuroscience, 7 (2004), 456-461.
|
|
[4]
|
BMC Bioinformatics, 11 (2010), 77.
|
|
[5]
|
J. Amer. Statist. Assoc., 103 (2008), 149-161.
|
|
[6]
|
Science, 164 (1969), 828-830.
|
|
[7]
|
Reihe Physik, Band 60, Verlag Harri Deutsch, Thun, Frankfurt/Main, 1996.
|
|
[8]
|
Neural Computation, 14 (2002), 43-80.
|
|
[9]
|
Monographs on Statistics and Applied Probability, 43, Chapman & Hall, Ltd., London, 1990.
|
|
[10]
|
Journal of Neurophysiology, 94 (2005), 8-25.
|
|
[11]
|
The Journal of Neuroscince, 23 (2003), 4299-4307.
|
|
[12]
|
Journal of Neuroscience Methods, 94 (1999), 81-92.
|
|
[13]
|
Physical Review E (3), 66 (2002), 041904, 9 pp.
|
|
[14]
|
Science, 262 (1993), 679-685.
|
|
[15]
|
Journal of Psychiatry and Neuroscience, 19 (1994), 354-358.
|
|
[16]
|
Monographs on Statistics and Applied Probability, 60, Chapman & Hall, London, 1995.
|
|
[17]
|
Texts in Statistical Science Series, Chapman & Hall/CRC, Boca Raton, FL, 2006.
|
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-
This article has been cited by:
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Aldana M. González-Montoro, Ricardo Cao, Nelson Espinosa, Javier Cudeiro, Jorge Mariño,
Bootstrap testing for cross-correlation under low firing activity,
2015,
38,
0929-5313,
577,
10.1007/s10827-015-0557-5
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