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 language | English |
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Pages (from-to) | 4540-4552 |
Journal | Entropy |
Volume | 15 |
Issue number | 10 |
DOIs | |
Publication status | Published - 2013 |