Information geometric measure for neural spikes

Information geometric measure for neural spikes (2002)

by Hiroyuki Nakahara , Shun-ichi Amari

Venue:	Neural Computation
Citations:	4 - 1 self

Datum	Value	Source
TITLE	Information geometric measure for neural spikes	SVM HeaderParse 0.2
AUTHOR NAME	Hiroyuki Nakahara	SVM HeaderParse 0.2
AUTHOR AFFIL	Lab. for Mathematical Neuroscience, RIKEN Brain Science Institute	SVM HeaderParse 0.2
AUTHOR ADDR	Wako, Saitama, 351-0198, Japan, fhiro,	SVM HeaderParse 0.2
AUTHOR NAME	Shun-ichi Amari	SVM HeaderParse 0.2
AUTHOR AFFIL	Lab. for Mathematical Neuroscience, RIKEN Brain Science Institute	SVM HeaderParse 0.2
AUTHOR ADDR	Wako, Saitama, 351-0198, Japan, fhiro,	SVM HeaderParse 0.2
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	SVM HeaderParse 0.2
YEAR	2002	INFERENCE
VENUE	Neural Computation	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	2269--2316	INFERENCE
VOLUME	14	INFERENCE
CITATIONS	94 found	ParsCit 1.0