TitleEntropy and information in neural spike trains: progress on the sampling problem.
Publication TypeJournal Article
Year of Publication2004
AuthorsNemenman, I, Bialek, W, van Steveninck, Rde Ruyter
JournalPhys Rev E Stat Nonlin Soft Matter Phys
Volume69
Issue5 Pt 2
Pagination056111
Date Published2004 May
Abstract

The major problem in information theoretic analysis of neural responses and other biological data is the reliable estimation of entropy-like quantities from small samples. We apply a recently introduced Bayesian entropy estimator to synthetic data inspired by experiments, and to real 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.

Alternate JournalPhys Rev E Stat Nonlin Soft Matter Phys