https://nemenmanlab.org/~ilya/index.php?action=history&feed=atom&title=Nemenman_et_al.%2C_2004 2026-05-17T08:52:21Z Revision history for this page on the wiki MediaWiki 1.31.0 https://nemenmanlab.org/~ilya/index.php?title=Nemenman_et_al.,_2004&diff=51&oldid=prev 2018-07-04T16:28:38Z

<p>1 revision imported</p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr class="diff-title" lang="en"> <td colspan="1" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td> <td colspan="1" style="background-color: #fff; color: #222; text-align: center;">Revision as of 16:28, 4 July 2018</td> </tr><tr><td colspan="2" class="diff-notice" lang="en"><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

Ilya https://nemenmanlab.org/~ilya/index.php?title=Nemenman_et_al.,_2004&diff=50&oldid=prev 2006-11-13T01:42:08Z

<p></p> <p><b>New page</b></p><div>{{Back to publ}}<br /> I Nemenman, W Bialek, and R de Ruyter van Steveninck. Entropy and information in neural spike trains: Progress on the sampling problem. ''Phys. Rev. E'', '''69''':056111, 2004. [[media:nemenman-etal-04.pdf | PDF]], [http://arxiv.org/abs/physics/0306063 arXiv].<br /> ;Abstract: The major problem in information theoretic analysis of neural responses is the reliable estimation of entropy-like quantities from small samples. We review a Bayesian estimator of entropies introduced recently [Nemenman et al., 2002] to solve this problem, and study its performance on synthetic and experimental spike trains. The estimator performs admirably even very deep in the undersampled regime, where other techniques fail. This opens new possibilities for the information theoretic analysis of experiments, and may be of general interest as an example of learning from limited data.</div>

nemenman>Ilya