Assembly Detection in Continuous Neural Spike Train Data

  • Christian Braune
  • Christian Borgelt
  • Sonja Grün
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)

Abstract

Since Hebb’s work on the organization of the brain [16] finding cell assemblies in neural spike trains has become a vivid field of research. As modern multi-electrode techniques allow to record the electrical potentials of many neurons in parallel, there is an increasing need for efficient and reliable algorithms to identify assemblies as expressed by synchronous spiking activity. We present a method that is able to cope with two core challenges of this complex task: temporal imprecision (spikes are not perfectly aligned across the spike trains) and selective participation (neurons in an ensemble do not all contribute a spike to all synchronous spiking events). Our approach is based on modeling spikes by influence regions of a user-specified width around the exact spike times and a clustering-like grouping of similar spike trains.

Keywords

spike train ensemble detection Hebbian learning continuous data multidimensional scaling 

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References

  1. 1.
    Allen, J.: Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843 (1983)MATHCrossRefGoogle Scholar
  2. 2.
    Berger, D., Borgelt, C., Diesmann, M., Gerstein, G., Grün, S.: An accretion based data mining algorithm for identification of sets of correlated neurons. In: 18th Annual Computational Neuroscience Meeting: CNS 2009, vol. 10(suppl. 1), pp. 18–23 (2009)Google Scholar
  3. 3.
    Berger, D., Borgelt, C., Louis, S., Morrison, A., Grün, S.: Efficient identification of assembly neurons within massively parallel spike trains. Computational Intelligence and Neuroscience, Article ID 439648 (2010), doi: 10.1155/2010/439648Google Scholar
  4. 4.
    Braune, C., Borgelt, C., Grün, S.: Finding Ensembles of Neurons in Spike Trains by Non-linear Mapping and Statistical Testing. In: Gama, J., Bradley, E., Hollmén, J. (eds.) IDA 2011. LNCS, vol. 7014, pp. 55–66. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Buzsáki, G.: Large-scale Recording of Neuronal Ensembles. Nature Neuroscience 7, 446–461 (2004)CrossRefGoogle Scholar
  6. 6.
    Cox, T.F., Cox, M.A.A.: Multidimensional Scaling, 2nd edn. Chapman and Hall, London (2000)CrossRefGoogle Scholar
  7. 7.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)CrossRefGoogle Scholar
  8. 8.
    Diekman, C.O., Sastry, P.S., Unnikrishnan, K.P.: Statistical significance of sequential firing patterns in multi-neuronal spike trains. Journal of Neuroscience Methods 182(2), 279–284 (2009)CrossRefGoogle Scholar
  9. 9.
    Edwards, A.: An introduction to linear regression and correlation. WH Freeman, New York (1984)MATHGoogle Scholar
  10. 10.
    Feldt, S., Waddell, J., Hetrick, V.L., Berke, J.D., Zochowski, M.: Functional clustering algorithm for the analysis of dynamic network data. Physical Review E 79(5), 056104-1–056104-9 (2009)Google Scholar
  11. 11.
    Gerstein, G., Perkel, D., Subramanian, K.: Identification of functionally related neural assemblies. Brain Research 140(1), 43–62 (1978)CrossRefGoogle Scholar
  12. 12.
    Grün, S., Diesmann, M., Grammont, F., Riehle, A., Aertsen, A.: Detecting unitary events without discretization of time. Journal of Neuroscience Methods 94, 67–79 (1999)CrossRefGoogle Scholar
  13. 13.
    Grün, S., Abeles, M., Diesmann, M.: Impact of Higher-Order Correlations on Coincidence Distributions of Massively Parallel Data. In: Marinaro, M., Scarpetta, S., Yamaguchi, Y. (eds.) Dynamic Brain - from Neural Spikes to Behaviors. LNCS, vol. 5286, pp. 96–114. Springer, Heidelberg (2008)Google Scholar
  14. 14.
    Grün, S., Diesmann, M., Aertsen, A.: Unitary events in multiple single-neuron activity. I. Detection and significance. Neural Computation 14(1), 43–80 (2002)MATHCrossRefGoogle Scholar
  15. 15.
    Hamming, R.: Error detecting and error correcting codes. Bell Systems Tech. Journal 29, 147–160 (1950)MathSciNetGoogle Scholar
  16. 16.
    Hebb, D.: The organization of behavior. J. Wiley & Sons, New York (1949)Google Scholar
  17. 17.
    Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)Google Scholar
  18. 18.
    Lewicki, M.: A review of methods for spike sorting: The detection and classification of neural action potentials. Network 9(4), R53–R78 (1998)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Masud, M.S., Borisyuk, R.: Statistical technique for analysing functional connectivity of multiple spike trains. Journal of Neuroscience Methods 196(1), 201–219 (2011)CrossRefGoogle Scholar
  20. 20.
    Pazienti, A., Maldonado, P., Diesmann, M., Grün, S.: Effectiveness of systematic spike dithering depends on the precision of cortical synchronization. Brain Research 1225, 39–46 (2008)CrossRefGoogle Scholar
  21. 21.
    Perkel, D.H., Gerstein, G.L., Moore, G.P.: Neuronal spike trains and stochastic point processes: II. Simultaneous spike trains. Biophysical Journal 7(4), 419–440 (1967)CrossRefGoogle Scholar
  22. 22.
    Picado-Muiño, D., Castro-León, I., Borgelt, C.: Fuzzy frequent pattern mining to identify frequent neuronal patterns in parallel spike trains. Submitted to IDA 2012Google Scholar
  23. 23.
    Rogers, D., Tanimoto, T.: A computer program for classifying plants. Science 132, 1115–1118 (1960)CrossRefGoogle Scholar
  24. 24.
    Sammon, J.: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 18(5), 401–409 (1969)CrossRefGoogle Scholar
  25. 25.
    Shmiel, T., Drori, R., Shmiel, O., Ben-Shaul, Y., Nadasdy, Z., Shemesh, M., Teicher, M., Abeles, M.: Temporally precise cortical firing patterns are associated with distinct action segments. Journal of Neurophysiology 96(5), 2645–2652 (2006)CrossRefGoogle Scholar
  26. 26.
    Tanimoto, T.: IBM Internal Report (November 17, 1957)Google Scholar
  27. 27.
    Yule, G.: On the association of attributes in statistics. Philosophical Transactions of the Royal Society of London, Series A 194, 257–319 (1900)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christian Braune
    • 1
  • Christian Borgelt
    • 2
  • Sonja Grün
    • 3
    • 4
  1. 1.Otto-von-Guericke-University of MagdeburgMagdeburgGermany
  2. 2.European Centre for Soft ComputingMieresSpain
  3. 3.Institute of Neuroscience and Medicine (INM-6)Research Center JülichGermany
  4. 4.Theoretical Systems NeurobiologyRWTH Aachen UniversityAachenGermany

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