Abstract
Simultaneous recording of multiple spike trains from population of neurons provides the possibility for understanding how neurons work together in response to various stimulations. But currently method is still lacking for researchers to perform multiple spike train data analysis and those existing techniques either allow people to analyze pair-wise neuronal activities only or are seriously subject to the selection of parameters. In this paper, a new measurement of information discrepancy, which is based on the comparisons of subsequence distributions, is applied to deal with a group of spike trains (n > 2) and analyze the synchronization pattern among the neurons, where the analytical result mostly depends on the experimental data and is affected little by subjective interference.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, GL., Liu, X., Zhang, PM., Liang, PJ. (2006). A New Method for Multiple Spike Train Analysis Based on Information Discrepancy. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_4
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DOI: https://doi.org/10.1007/11893028_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
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