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

Entropy

Kechen Zhang¹  Wentao Huang² 

[1] Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;Key Laboratory of Cognition and Intelligence and Information Science Academy of China Electronics Technology Group Corporation, Beijing 100086, China;

关键词: neural population coding;    mutual information;    Kullback-Leibler divergence;    Rényi divergence;    Chernoff divergence;    approximation;    discrete variables;   

DOI  :  10.3390/e21030243

来源: DOAJ

【 摘 要 】

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 this approach is restricted to continuous variables because the calculation of Fisher information requires derivatives with respect to the encoded variables. In this paper, we consider information-theoretic bounds and approximations of the mutual information based on Kullback-Leibler divergence and Rényi divergence. We propose several information metrics to approximate Shannon mutual information in the context of neural population coding. While our asymptotic formulas all work for discrete variables, one of them has consistent performance and high accuracy regardless of whether the encoded variables are discrete or continuous. We performed numerical simulations and confirmed that our approximation formulas were highly accurate for approximating the mutual information between the stimuli and the responses of a large neural population. These approximation formulas may potentially bring convenience to the applications of information theory to many practical and theoretical problems.

【 授权许可】

Unknown