Mutual information density of stochastic integrate-and-fire models
BMC Neuroscience volume 14, Article number: P245 (2013)
The coherence function of integrate-and-fire neurons shows low-pass properties in the most diverse firing regimes . 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  which has been estimated in certain biological model systems . In general, the exact analytical calculation of the mutual information rate is unfeasible and even the numerical estimation is demanding .
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.
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This work was funded by the BMBF (FKZ: 01GQ1001A).
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Bernardi, D., Lindner, B. Mutual information density of stochastic integrate-and-fire models. BMC Neurosci 14 (Suppl 1), P245 (2013). https://doi.org/10.1186/1471-2202-14-S1-P245