A kernel-based calculation of information on a metric space.

R. Joshua Tobin, Conor J Houghton

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

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.
Original languageEnglish
Pages (from-to)4540-4552
JournalEntropy
Volume15
Issue number10
DOIs
Publication statusPublished - 2013

Fingerprint Dive into the research topics of 'A kernel-based calculation of information on a metric space.'. Together they form a unique fingerprint.

Cite this