  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
limited samples of data. This approach also yields insight upon the role of
correlations between spikes in temporal coding mechanisms. The method, when
applied to recordings from complex cells of the monkey primary visual cortex,
results in lower RMS error information estimates in comparison to a `brute
force' approach.
