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BMC Neuroscience

, 12:P255 | Cite as

Measures of statistical dispersion based on Entropy and Fisher information

  • Lubomir Kostal
  • Petr Lansky
  • Ondrej Pokora
Open Access
Poster presentation
  • 1.1k Downloads

Keywords

Standard Deviation Experimental Data Animal Model Fisher Information Statistical Dispersion 
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.

We propose and discuss two information-based measures of statistical dispersion suitable to description of interspike interval data. The measures are compared with the standard deviation. Although the standard deviation is used routinely, we show that it is not well suited to quantify some aspects of dispersion which are often expected intuitively, such as the degree of randomness. The proposed dispersion measures are not mutually independent, however, each describes the firing regularity from a different point of view. We discuss relationships between the measures and describe their extreme values. We also apply the method to real experimental data from spontaneously active olfactory neurons of rats. Our results and conclusions are applicable to a wide range of situations where the distribution of a continuous positive random variable is of interest.

Notes

Acknowledgements

This work was supported by AV0Z50110509 and Centre for Neuroscience LC554.

References

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    Kostal L, Lansky P, Rospars JP: Review: Neuronal coding and spiking randomness. Eur J Neurosci. 2007, 26: 2693-2701. 10.1111/j.1460-9568.2007.05880.x.CrossRefPubMedGoogle Scholar
  2. 2.
    Kostal L, Marsalek P: Neuronal jitter: can we measure the spike timing dispersion differently?. Chin J Physiol. 2010, 53: 454-464.CrossRefPubMedGoogle Scholar

Copyright information

© Kostal et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Lubomir Kostal
    • 1
  • Petr Lansky
    • 1
  • Ondrej Pokora
    • 1
  1. 1.Department of Computational NeuroscienceInstitute of PhysiologyPrahaCzech Republic

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