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

3 Citations (Scopus)


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
Issue number10
Publication statusPublished - 2013


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