Work

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

Year: 2004

Type: article

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.

Source: Physical Review E

DOI: 10.1103/physreve.69.056111

Authors: Ilya Nemenman, William Bialek, Rob de Ruyter van Steveninck

Institutions: University of California, Santa Barbara, Kavli Institute for Theoretical Physics, Princeton University

Language: English

Topic: Statistical Mechanics and Entropy

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2097636360

FWCI: 13.13

Cites: 39

Cited by: 268

Related to: 10