Temporal correlations and neural spike train entropy

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  • 4 January 2000
Abstract
Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for extremely limited samples of data. This approach also yields insight upon the role of correlations between spikes in temporal coding mechanisms. The method is applied to recordings from the monkey visual cortex, yielding 1.5 and 0.5 bits per spike for simple and complex cells respectively.

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