Information-Geometric Measure for Neural Spikes
- 1 October 2002
- journal article
- Published by MIT Press in Neural Computation
- Vol. 14 (10), 2269-2316
- https://doi.org/10.1162/08997660260293238
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.Keywords
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