- Poster presentation
- Open Access
Mutual information density of stochastic integrate-and-fire models
- Davide Bernardi1, 2Email author and
- Benjamin Lindner1, 3
https://doi.org/10.1186/1471-2202-14-S1-P245
© Bernardi and Lindner; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
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
References
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Copyright
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.
