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Mutual information density of stochastic integrate-and-fire models

BMC Neuroscience
Springer (Biomed Central Ltd.)
Publication Date
DOI: 10.1186/1471-2202-14-s1-p245
  • Poster Presentation
  • Biology
  • Mathematics


Mutual information density of stochastic integrate-and-fire models POSTER PRESENTATION Open Access Mutual information density of stochastic integrate-and-fire models Davide Bernardi1,2*, Benjamin Lindner1,3 From Twenty Second Annual Computational Neuroscience Meeting: CNS*2013 Paris, France. 13-18 July 2013 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 repre- sented 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 compo- nents of a Gaussian stimulus are encoded in neural models without invoking a weak-signal paradigm. Acknowledgements This work was funded by the BMBF (FKZ: 01GQ1001A). Author details 1Bernstein Center for Computational Neuroscience, Berlin 10115, Germany. 2Department of Physics, Freie Universität Berlin, Berlin, Berlin 14195, Germany. 3Department of Physics, Humboldt-Universität zu Berlin, Berlin, Berlin 12489, Germany. Published: 8 July 2013 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. 2. Shannon C: A Mathematical Theory of Communication. The Bel

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