Information-Geometric Measure for Neural Spikes
Abstract
This study introduces information-geometric measures to analyze neural firing patterns by taking not only the second-order but also higher-order interactions among neurons into account. Information geometry provides useful tools and concepts for this purpose, including the orthogonality of coordinate parameters and the Pythagoras relation in the Kullback-Leibler divergence. Based on this orthogonality, we show a novel method for analyzing spike firing 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 benefits of our proposal by using several examples.
Cite
Text
Nakahara and Amari. "Information-Geometric Measure for Neural Spikes." Neural Computation, 2002. doi:10.1162/08997660260293238Markdown
[Nakahara and Amari. "Information-Geometric Measure for Neural Spikes." Neural Computation, 2002.](https://mlanthology.org/neco/2002/nakahara2002neco-informationgeometric/) doi:10.1162/08997660260293238BibTeX
@article{nakahara2002neco-informationgeometric,
title = {{Information-Geometric Measure for Neural Spikes}},
author = {Nakahara, Hiroyuki and Amari, Shun-ichi},
journal = {Neural Computation},
year = {2002},
pages = {2269-2316},
doi = {10.1162/08997660260293238},
volume = {14},
url = {https://mlanthology.org/neco/2002/nakahara2002neco-informationgeometric/}
}