Entropy 2013, 15(10), 4540-4552; doi:10.3390/e15104540

A Kernel-Based Calculation of Information on a Metric Space

1email and 2,* email
Received: 24 July 2013; in revised form: 14 October 2013 / Accepted: 14 October 2013 / Published: 22 October 2013
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: mutual information; neuroscience; electrophysiology; metric spaces; kernel density estimation
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MDPI and ACS Style

Tobin, R.J.; Houghton, C.J. A Kernel-Based Calculation of Information on a Metric Space. Entropy 2013, 15, 4540-4552.

AMA Style

Tobin RJ, Houghton CJ. A Kernel-Based Calculation of Information on a Metric Space. Entropy. 2013; 15(10):4540-4552.

Chicago/Turabian Style

Tobin, R. J.; Houghton, Conor J. 2013. "A Kernel-Based Calculation of Information on a Metric Space." Entropy 15, no. 10: 4540-4552.

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