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Neuronal Data Analysis Based on the Empirical Cumulative Entropy

  • Antonio Di Crescenzo
  • Maria Longobardi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)

Abstract

We propose the empirical cumulative entropy as a variability measure suitable to describe the information content in neuronal firing data. Some useful characteristics and an application to a real dataset are also discussed.

Keywords

Spike Train Neural Code Neuronal Code Partition Entropy Cumulative Residual Entropy 
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 2012

Authors and Affiliations

  • Antonio Di Crescenzo
    • 1
  • Maria Longobardi
    • 2
  1. 1.Dipartimento di MatematicaUniversità di SalernoFiscianoItaly
  2. 2.Dipartimento di Matematica e ApplicazioniUniversità di Napoli Federico IINapoliItaly

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