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

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

2003-06-07

arxiv.org/abs/physics/0306063v3

Phys. Rev. E 69, 056111 (2004)

Physics

Data Analysis, Statistics and Probability

7 pages, 4 figures; referee suggested changes, accepted version

Scientific paper

10.1103/PhysRevE.69.056111

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.

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