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Observed Brain Dynamics$
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Partha Mitra and Hemant Bokil

Print publication date: 2007

Print ISBN-13: 9780195178081

Published to Oxford Scholarship Online: May 2009

DOI: 10.1093/acprof:oso/9780195178081.001.0001

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Entropy and Mutual Information

Entropy and Mutual Information

(p.333) 14 Entropy and Mutual Information
Observed Brain Dynamics

Partha P. Mitra

Hemant Bokil

Oxford University Press

In the neuroscience literature, significant use has been made of entropy as a measure of variability, and mutual information as a measure of association. Part of the attraction of these measures arises from their use in statistical physics and in communication theory. The idea is that they are free of distributional assumptions and have elevated theoretical status compared with second-moment measures that may be associated with Gaussian distributions. However, while these measures are theoretically elegant and have desirable invariance properties, apart from difficulties of estimation, they are by construction not informative about the shape of the distributions or the nature of the functional relationships between variables. This chapter presents a brief review of the relevant information theoretic approaches, including links to Gaussian processes and inhomogeneous Poisson processes.

Keywords:   continuous random variables, discrete time stochastic processes, point processes, estimation methods, communication theory, Gaussian distributions

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