title: 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.
authors.affiliation: Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, Wako, Saitama, 351-0198, Japan. hiro@brain.riken.go.jp
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Nakahara
authors.forename: Shun-ichi
authors.initials: S
authors.lastname: Amari
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*