A Kernel-Based Calculation of Information on a Metric Space

Entropy

R. Joshua Tobin¹ 

[1] School of Mathematics, Trinity College Dublin, Dublin 2, Ireland; E-Mail:

关键词: mutual information;    neuroscience;    electrophysiology;    metric spaces;    kernel density estimation;   

DOI  :  10.3390/e15104540

来源: mdpi

【 摘 要 】

Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space. This is motivated by the problem in neuroscience of calculating the mutual information between stimuli and spiking responses; the space of these responses is a metric space. It is shown that kernel density estimation on a metric space resembles the k-nearest-neighbor approach. This approach is applied to a toy dataset designed to mimic electrophysiological data.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.