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  • Poster presentation
  • Open Access

Mutual information density of stochastic integrate-and-fire models

BMC Neuroscience201314 (Suppl 1) :P245

  • Published:


  • 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.



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

Authors’ Affiliations

Bernstein Center for Computational Neuroscience, Berlin, 10115, Germany
Department of Physics, Freie Universität Berlin, Berlin, Berlin, 14195, Germany
Department of Physics, Humboldt-Universität zu Berlin, Berlin, Berlin, 12489, Germany


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