Assembly Detection in Continuous Neural Spike Train Data

  • Christian Braune
  • Christian Borgelt
  • Sonja Grün
Conference paper

DOI: 10.1007/978-3-642-34156-4_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)
Cite this paper as:
Braune C., Borgelt C., Grün S. (2012) Assembly Detection in Continuous Neural Spike Train Data. In: Hollmén J., Klawonn F., Tucker A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg

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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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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