Correlation and independence in the neural code

被引：13

作者：

Amari, Shun-ichi ^{[1
]}

Nakahara, Hiroyuki ^{[1
]}

机构：

[1] RIKEN, Brain Sci Inst, Wako, Saitama 35101, Japan

来源：

NEURAL COMPUTATION | 2006年 / 18卷 / 06期

关键词：

D O I：

10.1162/neco.2006.18.6.1259

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The decoding scheme of a stimulus can be different from the stochastic encoding scheme in the neural population coding. The stochastic fluctuations are not independent in general, but an independent version could be used for the ease of decoding. How much information is lost by using this unfaithful model for decoding? There are discussions concerning loss of information (Nirenberg & Latham, 2003; Schneidman, Bialek, & Berry 2003). We elucidate the Nirenberg-Latham loss from the point of view of information geometry.

引用

页码：1259 / 1267

页数：9

共 6 条

[1]

AMARI S, 2005, UNPUB GEN BAYES PRED

[2]

Amari SI, 2007, Methods of information geometry, V191

[3] Synergy, redundancy, and independence in population codes, revisited [J].