Submitted to Computational Neuroscience 2000
Information theory has become popular for illucidating cortical function, both in experimental and theoretical studies. however, these studies make unrealistic assumptions about the noise. Generally experimental studies assume Poisson statistics, the theoretical models use rate-based neurons with rate-independent noise. These simplifications qualitatively affect the results. Generalizing, from Poisson, to arbitrary rate-dependent renewal neurons I shown that: 1) For constant rate, one loses information if one only the total spike count is used, unlike in Poisson processes. 2) An optimal encoder has a sparse representation of the input, unlike the whitening obtained in models with rate-based neurons. Thus, when using information theory, a sufficiently good description of the noise is vital.
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