Information-geometric measure for neural spikes

被引:97
作者
Nakahara, H [1 ]
Amari, S [1 ]
机构
[1] RIKEN, Brain Sci Inst, Lab Math Neurosci, Wako, Saitama 3510198, Japan
关键词
D O I
10.1162/08997660260293238
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:2269 / 2316
页数:48
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