  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.
