A Kernel-Based Calculation of Information on a Metric Space
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 spac...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2013-10-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/15/10/4540 |
| _version_ | 1817996580217683968 |
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| author | Conor J. Houghton R. Joshua Tobin |
| author_facet | Conor J. Houghton R. Joshua Tobin |
| author_sort | Conor J. Houghton |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T02:24:36Z |
| format | Article |
| id | doaj.art-7f0d18fd5c86429ea17c665a95272763 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T02:24:36Z |
| publishDate | 2013-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-7f0d18fd5c86429ea17c665a952727632022-12-22T02:17:56ZengMDPI AGEntropy1099-43002013-10-0115104540455210.3390/e15104540A Kernel-Based Calculation of Information on a Metric SpaceConor J. HoughtonR. Joshua TobinKernel 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.http://www.mdpi.com/1099-4300/15/10/4540mutual informationneuroscienceelectrophysiologymetric spaceskernel density estimation |
| spellingShingle | Conor J. Houghton R. Joshua Tobin A Kernel-Based Calculation of Information on a Metric Space Entropy mutual information neuroscience electrophysiology metric spaces kernel density estimation |
| title | A Kernel-Based Calculation of Information on a Metric Space |
| title_full | A Kernel-Based Calculation of Information on a Metric Space |
| title_fullStr | A Kernel-Based Calculation of Information on a Metric Space |
| title_full_unstemmed | A Kernel-Based Calculation of Information on a Metric Space |
| title_short | A Kernel-Based Calculation of Information on a Metric Space |
| title_sort | kernel based calculation of information on a metric space |
| topic | mutual information neuroscience electrophysiology metric spaces kernel density estimation |
| url | http://www.mdpi.com/1099-4300/15/10/4540 |
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