A Kernel-Based Calculation of Information on a Metric Space

作者：Tobin R Joshua; Houghton Conor J^*

来源：Entropy, 2013, 15(10): 4540-4552.

DOI：10.3390/e15104540

• mutual information
• neuroscience
• electrophysiology
• metric spaces
• kernel density estimation

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.

• 出版日期2013-10