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Mutual information density of stochastic integrate-and-fire models

BMC Neuroscience201314 (Suppl 1) :P245

https://doi.org/10.1186/1471-2202-14-S1-P245

  • Published:

Keywords

  • Mutual Information
  • Numerical Procedure
  • Full Information
  • Information Transfer
  • Neuronal Response

The coherence function of integrate-and-fire neurons shows low-pass properties in the most diverse firing regimes [1]. While the coherence function provides a good approximation to the full information transfer properties in the case of a weak input, for a strong input non-linear encoding could play an important role. The complete information transfer is quantified by Shannon's mutual information rate [2] which has been estimated in certain biological model systems [3]. In general, the exact analytical calculation of the mutual information rate is unfeasible and even the numerical estimation is demanding [4].

Numerical calculation of the mutual information rate is now a commonly adopted practice, but it does not indicate what aspects of the stimulus are best represented by the neuronal response. We developed a numerical procedure to directly calculate a frequency-selective version of the mutual information rate. This can be used to study how different frequency components of a Gaussian stimulus are encoded in neural models without invoking a weak-signal paradigm.

Declarations

Acknowledgements

This work was funded by the BMBF (FKZ: 01GQ1001A).

Authors’ Affiliations

(1)
Bernstein Center for Computational Neuroscience, Berlin, 10115, Germany
(2)
Department of Physics, Freie Universität Berlin, Berlin, Berlin, 14195, Germany
(3)
Department of Physics, Humboldt-Universität zu Berlin, Berlin, Berlin, 12489, Germany

References

  1. Vilela RD, Lindner B: A comparative study of different integrate fire neurons: spontaneous activity, dynamical response, and stimulus-induced correlation. Phys Rev E. 2009, 80: 031909-View ArticleGoogle Scholar
  2. Shannon C: A Mathematical Theory of Communication. The Bell System Technical Journal. 1948, 27: 379-423. 623-656View ArticleGoogle Scholar
  3. Strong SP, Koberle R, de Ruyter van Steveninck R, Bialek W: Entropy and Information in Neural Spike Trains. Phys Rev Lett. 1998, 80 (1): 197-200. 10.1103/PhysRevLett.80.197.View ArticleGoogle Scholar
  4. Panzeri S, Senatore R, Montemurro MA, Petersen RS: Correcting for the sampling bias problem in spike train information measures. J Neurophysiol. 2007, 98 (3): 1064-1072. 10.1152/jn.00559.2007.View ArticlePubMedGoogle Scholar

Copyright

© Bernardi and Lindner; licensee BioMed Central Ltd. 2013

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.

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