Information geometric measure for neural spikes (2002)
| Venue: | Neural Computation |
| Citations: | 4 - 1 self |
BibTeX
@ARTICLE{Nakahara02informationgeometric,
author = {Hiroyuki Nakahara and Shun-ichi Amari},
title = {Information geometric measure for neural spikes},
journal = {Neural Computation},
year = {2002},
volume = {14},
pages = {2269--2316}
}
OpenURL
Abstract
The present study introduces information-geometric measures to analyze neural ring patterns by taking not only the second-order but also higher-order interactions among neurons into ac-count. Information geometry provides useful tools and concepts for this purpose, including the orthogonality of coordinate pa-rameters and the Pythagoras relation in the Kullback-Leibler di-vergence. Based on this orthogonality, we show anovel method to analyze spike ring patterns by decomposing the interactions of neurons of various orders. As a result, purely pairwise, triple-wise, and higher-order interactions are singled out. We also demonstrate the bene ts of our proposal by using real neural data, recorded in the prefrontal and parietal cortices of mon-keys. 1







