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Mutual information density of stochastic integrate-and-fire models

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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.

References

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    Shannon C: A Mathematical Theory of Communication. The Bell System Technical Journal. 1948, 27: 379-423. 623-656

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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.

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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.

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Acknowledgements

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

Author information

Correspondence to Davide Bernardi.

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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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Bernardi, D., Lindner, B. Mutual information density of stochastic integrate-and-fire models. BMC Neurosci 14, P245 (2013) doi:10.1186/1471-2202-14-S1-P245

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Keywords

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