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

来源：

NEURAL COMPUTATION | 2002年 / 14卷 / 10期

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

页码：2269 / 2316

页数：48

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