Entropy and information in neural spike trains: Progress on the sampling problem

• Type of Material: Article
• Authors: Ilya Nemenman, Emory University; William Bialek, Princeton University; Rob de Ruyter van Steveninck, Princeton University
• Language: English
• Date: 2004-05-24
• Publisher: American Physical Society
• Publication Version: Final Published Version
• Copyright Statement: ©2004 The American Physical Society
• Final Published Version (URL): http://link.aps.org/doi/10.1103/PhysRevE.69.056111
• Title of Journal or Parent Work: Physical Review E
• ISSN: 1539-3755
• Volume: 69
• Start Page: 056111
• End Page: 056111
• Grant/Funding Information: I.N. was supported by NSF Grant No. PHY99-07949 to the Kavli Institute for Theoretical Physics.
• Research Categories: Biophysics, General

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.