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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.
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-
Shannon C: A Mathematical Theory of Communication. The Bell System Technical Journal. 1948, 27: 379-423. 623-656
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.
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.
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, P245 (2013) doi:10.1186/1471-2202-14-S1-P245
- Mutual Information
- Numerical Procedure
- Full Information
- Information Transfer
- Neuronal Response