HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Kennel, M. B. Articles by Chichilnisky, E. J. Search for Related Content PubMed PubMed Citation Articles by Kennel, M. B. Articles by Chichilnisky, E. J.

Estimating Entropy Rates with Bayesian Confidence Intervals

mkennel{at}ucsd.edu, Institute for Nonlinear Science, University of California, San Diego, La Jolla, CA 92093-0402, U.S.A.

Jonathon Shlens

shlens{at}salk.edu, Systems Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037
Institute for Nonlinear Science, University of California, San Diego, La Jolla, CA 92093-0402, U.S.A.

Henry D. I. Abarbanel

habarbanel{at}ucsd.edu, Department of Physics and Marine Physical Laboratory, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093-0402, U.S.A.

E. J. Chichilnisky

ej{at}salk.edu, Systems Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A.

The entropy rate quantifies the amount of uncertainty or disorder produced by any dynamical system. In a spiking neuron, this uncertainty translates into the amount of information potentially encoded and thus the subject of intense theoretical and experimental investigation. Estimating this quantity in observed, experimental data is difficult and requires a judicious selection of probabilistic models, balancing between two opposing biases. We use a model weighting principle originally developed for lossless data compression, following the minimum description length principle. This weighting yields a direct estimator of the entropy rate, which, compared to existing methods, exhibits significantly less bias and converges faster in simulation. With Monte Carlo techinques, we estimate a Bayesian confidence interval for the entropy rate. In related work, we apply these ideas to estimate the information rates between sensory stimuli and neural responses in experimental data (Shlens, Kennel, Abarbanel, & Chichilnisky, 2004).

This article has been cited by other articles:

				G. A. Jacobs, J. P. Miller, and Z. Aldworth Computational mechanisms of mechanosensory processing in the cricket J. Exp. Biol., June 1, 2008; 211(11): 1819 - 1828. [Abstract] [Full Text] [PDF]

				M. A. Montemurro, R. Senatore, and S. Panzeri Tight Data-Robust Bounds to Mutual Information Combining Shuffling and Model Selection Techniques Neural Comput., November 1, 2007; 19(11): 2913 - 2957. [Abstract] [Full Text] [PDF]

				S. Panzeri, R. Senatore, M. A. Montemurro, and R. S. Petersen Correcting for the Sampling Bias Problem in Spike Train Information Measures J Neurophysiol, September 1, 2007; 98(3): 1064 - 1072. [Abstract] [Full Text] [PDF]