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A Kernel-Based Calculation of Information on a Metric Space
School of Mathematics, Trinity College Dublin, Dublin 2, Ireland
Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol BS8 1UB, UK
* Author to whom correspondence should be addressed.
Received: 24 July 2013; in revised form: 14 October 2013 / Accepted: 14 October 2013 / Published: 22 October 2013
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.
Tobin RJ, Houghton CJ. A Kernel-Based Calculation of Information on a Metric Space. Entropy. 2013; 15(10):4540-4552.
Tobin, R. J.; Houghton, Conor J. 2013. "A Kernel-Based Calculation of Information on a Metric Space." Entropy 15, no. 10: 4540-4552.