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Neuronal Data Analysis Based on the Empirical Cumulative Entropy

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Computer Aided Systems Theory – EUROCAST 2011 (EUROCAST 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6927))

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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.

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Author information

Authors and Affiliations

  1. Dipartimento di Matematica, Università di Salerno, I-84084, Fisciano, SA, Italy

    Antonio Di Crescenzo

  2. Dipartimento di Matematica e Applicazioni, Università di Napoli Federico II, I-80126, Napoli, Italy

    Maria Longobardi

Authors
  1. Antonio Di Crescenzo
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  2. Maria Longobardi
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Editor information

Editors and Affiliations

  1. Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Universidad de Las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz  & Alexis Quesada-Arencibia  & 

  2. Institute of Systems Science, Johannes Kepler University Linz, Altenbergerstrasse 69, 4040, Linz, Austria

    Franz Pichler

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Di Crescenzo, A., Longobardi, M. (2012). Neuronal Data Analysis Based on the Empirical Cumulative Entropy. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_10

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  • DOI: https://doi.org/10.1007/978-3-642-27549-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27548-7

  • Online ISBN: 978-3-642-27549-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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