Approximations of Shannon Mutual Information for Discrete Variables with Applications to Neural Population Coding

Approximations of Shannon Mutual Information for Discrete Variables with Applications to Neural Population Coding

Although Shannon mutual information has been widely used, its effective calculation is often difficult for many practical problems, including those in neural population coding. Asymptotic formulas based on Fisher information sometimes provide accurate approximations to the mutual information but thi...

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Bibliographic Details
Main Authors: Wentao Huang, Kechen Zhang
Format: Article
Language:English
Published: MDPI AG 2019-03-01
Series:Entropy
Subjects:
neural population coding
mutual information
Kullback-Leibler divergence
Rényi divergence
Chernoff divergence
approximation
discrete variables
Online Access:http://www.mdpi.com/1099-4300/21/3/243

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http://www.mdpi.com/1099-4300/21/3/243

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