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

Ilya Nemenman, William Bialek, Rob De Ruyter Van Steveninck

Research output: Contribution to journalArticlepeer-review

155 Scopus citations

Abstract

The Bayesian entropy estimator was used for reliable estimation of entropy-like quantities in information theoretic analysis of neural responses and other biological data. The estimator was applied to synthetic data inspired by experiments, and to real experimental spike trains. It was found that the estimator performed well even in the undersampled regime. This shows that the estimator can increase the possibilities for the information theoretic analysis of experiments.

Original languageEnglish (US)
Article number056111
Pages (from-to)056111-1-056111-6
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume69
Issue number5 1
StatePublished - May 1 2004

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

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