Real and Modeled Spike Trains: Where Do They Meet?

  • Vasile V. Moca
  • Danko Nikolić
  • Raul C. Mureşan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)


Spike train models are important for the development and calibration of data analysis methods and for the quantification of certain properties of the data. We study here the properties of a spike train model that can produce both oscillatory and non-oscillatory spike trains, faithfully reproducing the firing statistics of the original spiking data being modeled. Furthermore, using data recorded from cat visual cortex, we show that despite the fact that firing statistics are reproduced, the dynamics of the modeled spike trains are significantly different from their biological counterparts. We conclude that spike train models are difficult to use when studying collective dynamics of neurons and that there is no universal ’recipe’ for modeling cortical firing, as the latter can be both very complex and highly variable.


Oscillation Strength Spike Train Spike Count Spike Burst Tonic Spike 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vasile V. Moca
    • 1
  • Danko Nikolić
    • 2
    • 3
  • Raul C. Mureşan
    • 1
    • 2
  1. 1.Center for Cognitive and Neural Studies (Coneural)Cluj-NapocaRomania
  2. 2.Max Planck Institute for Brain ResearchFrankfurt am MainGermany
  3. 3.Frankfurt Institute for Advanced StudiesFrankfurt am MainGermany

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