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BMC Neuroscience

, 14:P245 | Cite as

Mutual information density of stochastic integrate-and-fire models

Open Access
Poster presentation
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Keywords

Mutual Information Numerical Procedure Full Information Information Transfer Neuronal Response 
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.

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.

Notes

Acknowledgements

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

References

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    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.CrossRefGoogle Scholar
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    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.CrossRefPubMedGoogle Scholar

Copyright information

© 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.

Authors and Affiliations

  1. 1.Bernstein Center for Computational NeuroscienceBerlinGermany
  2. 2.Department of PhysicsFreie Universität Berlin, BerlinBerlinGermany
  3. 3.Department of PhysicsHumboldt-Universität zu Berlin, BerlinBerlinGermany

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