Handling Selective Participation in Neuron Assembly Detection

  • Salatiel Ezennaya-Gomez
  • Christian Borgelt
  • Christian Braune
  • Kristian Loewe
  • Rudolf Kruse
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 669)

Abstract

With the objective to detect neuron assemblies in recorded parallel spike trains, we develop methods to find frequent parallel episodes in parallel point processes (or event sequences) that allow for imprecise synchrony of the events constituting occurrences (temporal imprecision) as well as incomplete occurrences (selective participation). The temporal imprecision problem is tackled by frequent pattern mining using two different notions of synchrony: a binary notion that captures only the number of instances of a pattern and a graded notion that captures both the number of instances as well as the precision of synchrony of its events. To cope with selective participation, which is the main focus of this paper, a reduction sequence of items (or event types) is formed based on found frequent patterns and guided by pattern overlap, for which we explore different concept. We demonstrate the performance of our methods on a large number of (artificially generated) data sets with injected parallel episodes, which mimic actually recorded parallel spike trains.

Notes

Acknowledgements

The work presented in this paper was partially supported by the Spanish Ministry for Economy and Competitiveness (MINECO Grant TIN2012-31372) and by the Principality of Asturias, through the 2013-2017 Science Technology and Innovation Plan (Programa Asturias, CT1405206), and the European Union, through FEDER funds.

References

  1. 1.
    Abeles, M.: Role of the cortical neuron: integrator or coincidence detector? Isr. J. Med. Sci. 18(1), 83–92 (1982). Israel Medical Association, Ramat Gan, Israel (1982)MathSciNetGoogle Scholar
  2. 2.
    Borgelt, C.: Frequent item set mining. In: Wiley Interdisciplinary Reviews (WIREs): Data Mining and Knowledge Discovery, pp. 437–456. Wiley, Chichester (2012)Google Scholar
  3. 3.
    Borgelt, C., Braune, C., Loewe, K., Kruse, R.: Mining frequent parallel episodes with selective participation. In: Proceedings of 16th World Congress of the International Fuzzy Systems Association (IFSA) and 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), IFSA-EUSFLAT2015, Gijon, Spain. Atlantis Press, Amsterdam, Netherlands (2015)Google Scholar
  4. 4.
    Borgelt, C., Picado-Muiño, D.: Finding frequent synchronous events in parallel point processes. In: Proceedings of 12th International Symposium on Intelligent Data Analysis, IDA, London, UK, pp. 116–126. Springer, Heidelberg (2013)Google Scholar
  5. 5.
    Dayan, P., Abbott, L.: Theoretical neuroscience: computational and mathematical modeling of neural systems. J. Cogn. Neurosci. 15(1), 154–155 (2003). MIT Press, CambridgeCrossRefGoogle Scholar
  6. 6.
    Dudoit, S., van der Laan, M.J.: Multiple Testing Procedures with Application to Genomics. Springer, New York (2008)CrossRefMATHGoogle Scholar
  7. 7.
    Ezennaya-Gómez, S., Borgelt, C.: Mining frequent synchronous patterns with a graded notion of synchrony. In: Proceedings of 16th World Congress Int. Fuzzy Systems Association (IFSA) and 9th Conference European Society for Fuzzy Logic and Technology (EUSFLAT), IFSA-EUSFLAT, Gijón, Spain. Atlantis Press, Amsterdam (2015)Google Scholar
  8. 8.
    Fiedler, M., Borgelt, C.: Subgraph support in a single graph. In: Proceedings of IEEE International Workshop on Mining Graphs and Complex Data, pp. 399–404. IEEE Press, Piscataway (2007)Google Scholar
  9. 9.
    Høastad, J.: Clique is hard to approximate within \(n^{1-e}\). Acta Mathematica 182, 105–142 (1999). Mittag-Leffler Institute, StockholmMathSciNetCrossRefGoogle Scholar
  10. 10.
    Hebb, D.O.: The Organization of Behavior. Wiley, New York (1949)Google Scholar
  11. 11.
    Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press, New York (1972)CrossRefGoogle Scholar
  12. 12.
    Kernighan, B.W., Ritchie, D.M.: The C Programming Language. Prentice Hall, Upper Saddle River (1978)MATHGoogle Scholar
  13. 13.
    König, P., Engel, A.K., Singer, W.: Integrator or coincidence detector? the role of the cortical neuron revisited. Trends Neurosci. 19(4), 130–137 (1996). Cell Press, Maryland HeightsCrossRefGoogle Scholar
  14. 14.
    Laxman, S., Sastry, P.S., Unnikrishnan, K.: Discovering frequent episodes and learning hidden Markov models: a formal connection. IEEE Trans. Knowl. Data Eng. 17(11), 1505–1517 (2005). IEEE Press, PiscatawayCrossRefGoogle Scholar
  15. 15.
    Louis, S., Borgelt, C., Grün, S.: Generation and selection of surrogate methods for correlation analysis. In: Grün, S., Rotter, S. (eds.) Analysis of Parallel Spike Trains, pp. 359–382. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Mannila, H., Toivonen, H., Verkamo, A.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discovery 1(3), 259–289 (1997). Springer, New YorkCrossRefGoogle Scholar
  17. 17.
    Picado-Muiño, D., Castro-León, I., Borgelt, C.: Fuzzy characterization of spike synchrony in parallel spike trains. Soft Comput. 18(1), 71–83 (2013). Springer, Heidelberg 2013 (online)/2014 (print)CrossRefGoogle Scholar
  18. 18.
    Picado-Muiño, D., Borgelt, C., Berger, D., Gerstein, G.L., Grün, S.: finding neural assemblies with frequent item set mining. Front. Neuroinf.7(9). Frontiers Media, Lausanne, Switzerland (2013). doi: 10.3389/fninf.2013.00009
  19. 19.
    Picado-Muiño, D., Borgelt, C.: Frequent itemset mining for sequential data: synchrony in neuronal spike trains. Intell. Data Anal. 18(6), 997–1012 (2014). IOS Press, AmsterdamGoogle Scholar
  20. 20.
    van Rossum, G.: An Introduction to Python for Unix/C programmers. In: Proceedings of the NLUUG najaarsconferentie (Dutch UNIX users group) (1993)Google Scholar
  21. 21.
    Tatti, N.: Significance of episodes based on minimal windows. In: Proceedings of 9th IEEE International Conference on Data Mining (ICDM 2009, Miami, FL, USA), 513–522. IEEE Press, Piscataway (2009)Google Scholar
  22. 22.
    Torre, E., Picado-Muiño, D., Denker, M., Borgelt, C., Grün, S.: Statistical evaluation of synchronous spike patterns extracted by frequent itemset mining. Front. Comput. Neurosci. 7, 132. Frontiers Media, Lausanne (2013)Google Scholar
  23. 23.
    Tsourakakis, C., Bonchi, F., Gionis, A., Gullo, F., Tsiarli, M.: Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees. In: Proceedings of 19th ACM SIGMOD International Conference on Knowledge Discovery and Data Mining (KDD, Chicago, IL), pp. 104–112. ACM, New York (2013)Google Scholar
  24. 24.
    Vanetik, N., Gudes, E., Shimony, S.E.: Computing frequent graph patterns from semistructured data. In: Proceedings of IEEE International Conference on Data Mining, 458–465. IEEE Press, Piscataway (2002)Google Scholar
  25. 25.
    Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Proceedings 3rd International Conference on Knowledge Discovery and Data Mining (KDD, Newport Beach, CA), pp. 283–296. AAAI Press, Menlo Park, CA, USA (1997)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Salatiel Ezennaya-Gomez
    • 1
    • 2
  • Christian Borgelt
    • 1
  • Christian Braune
    • 2
  • Kristian Loewe
    • 2
    • 3
  • Rudolf Kruse
    • 2
  1. 1.Intelligent Data Analysis Research UnitEuropean Centre for Soft ComputingMieres (Asturias)Spain
  2. 2.Department of Knowledge and Language ProcessingOtto-von-Guericke-UniversityMagdeburgGermany
  3. 3.Department of Neurology, Experimental NeurologyOtto-von-Guericke-UniversityMagdeburgGermany

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