A New Method for Multiple Spike Train Analysis Based on Information Discrepancy

  • Guang-Li Wang
  • Xue Liu
  • Pu-Ming Zhang
  • Pei-Ji Liang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


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.


Ganglion Cell Discrepancy Ratio Spike Train Information Discrepancy Spike Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nirenberg, S., Latham, P.E.: Population Coding in the Retina. Current. Opinion in Neurobiology 8(4), 488–493 (1998)CrossRefGoogle Scholar
  2. 2.
    Petersen, R.S., Panzeri, S., Diamond, M.E.: Population Coding in Somatosensory Cortex. Current. Opinion in Neurobiology 12(4), 441–447 (2002)CrossRefGoogle Scholar
  3. 3.
    Meister, M., Pine, J., Baylor, D.A.: Multi-neuronal Signals from the Retina: Acquisition and Analysis. J. Neurosci. Methods 51, 95–106 (1994)CrossRefGoogle Scholar
  4. 4.
    Brown, E.N., Kass, R.E., Mitra, P.P.: Multiple Neural Spike Train Data Analysis: State-of-the-Art and Future Challenges. Nature Neuroscience 7(5), 456–461 (2004)CrossRefGoogle Scholar
  5. 5.
    Mastronarde, D.N.: Interactions Between Ganglion Cells in Cat Retina. J. Neurophysiol. 49(2), 350–365 (1983)Google Scholar
  6. 6.
    Aertsen, A.M., Gerstein, G.L., Habib, M.K., Palm, G.: Dynamics of Neuronal Firing Correlation: Modulation of “Effective Connectivity”. J. Neurophysiol. 61(5), 900–917 (1989)Google Scholar
  7. 7.
    Konig, P.: A Method for the Quantification of Synchrony and Oscillatory Properties of Neuronal Activity. J. Neurosci. Methods 54, 31–37 (1994)CrossRefGoogle Scholar
  8. 8.
    Gerstein, G.L., Aertsen, A.M.: Representation of Cooperative Firing Activity among Simultaneously Recorded Neurons. J. Neurophysiol. 54(6), 1513–1528 (1985)Google Scholar
  9. 9.
    Schnitzer, M.J., Meister, M.: Multineuronal Firing Patterns in the Signal from Eye to Brain. Neuron 37, 499–511 (2003)CrossRefGoogle Scholar
  10. 10.
    Fang, W.W.: Disagreement Degree of Multi-person Judgements in an Additive Structure. Mathemetical Social Sciences 28, 85–111 (1994)MATHCrossRefGoogle Scholar
  11. 11.
    Fang, W.W., Roberts, F.S., Ma, Z.R.: A Measure of Discrepancy of Multiple Sequences. Information Science 137, 75–102 (2001)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Chen, A.H., Zhou, Y., Gong, H.Q., Liang, P.J.: Firing Rates and Dynamic Correlated Activities of Ganglion Cells Both Contribute to Retinal Information Processing. Brain Res. 1017, 13–20 (2004)CrossRefGoogle Scholar
  13. 13.
    Zhang, P.M., Wu, J.Y., Zhou, Y., Liang, P.J., Yuan, J.Q.: Spike Sorting Based on Automatic Template Reconstruction with A Partial Solution to the Overlapping Problem. J. Neurosci. Methods 135(1-2), 55–65 (2004)CrossRefGoogle Scholar
  14. 14.
    Wang, G.L., Zhou, Y., Chen, A.H., Zhang, P.M., Liang, P.J.: A RobustMethod for Spike Sorting with Automatic Overlap Decomposition. IEEE Trans. Biomed. Eng. 53(6), 1195–1198 (2006)CrossRefGoogle Scholar
  15. 15.
    Szczepanski, J., Amigo, J.M., Wajnryb, E., Sanchez-Vives, M.V.: Application of Lempel-ziv Complexity to the Analysis of Neural Discharges. Network: Comput. Neural Syst. 14, 335–350 (2003)CrossRefGoogle Scholar
  16. 16.
    Li, W., Fang, W.W., Ling, L.J., Wang, J.H., Xuan, Z., Chen, R.S.: Phylogeny Based on Whole Genome as Inferred from Complete Information Set Analysis. Journal of Biological Physics 28, 439–447 (2002)CrossRefGoogle Scholar
  17. 17.
    Fang, W.W.: The Characterization of A Measure of Information Discrepancy. Information Sciences 125, 207–232 (2000)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guang-Li Wang
    • 1
  • Xue Liu
    • 1
  • Pu-Ming Zhang
    • 1
  • Pei-Ji Liang
    • 1
  1. 1.Department of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations