Self-organizing maps - Multivariate learning algorithms and applications to auditory spike train analysis

被引：0

作者：

Si, J ^{[1
]}

Kipke, DR ^{[1
]}

Witte, R ^{[1
]}

Lan, J ^{[1
]}

Lin, SM ^{[1
]}

机构：

[1] Arizona State Univ, Dept Elect Engn, Tempe, AZ 85287 USA

来源：

BIOCOMPUTING | 2002年 / 1卷

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Self-organizing map (SOM) has been applied in many different fields of science and engineering. In this chapter it is shown how the SOM can be used to decode neural spike trains of an awake animal and associate external auditors, stimuli with spike patterns of the brain. This chapter begins with an introduction of the SOM and highlights advantages of the SOM when compared to other multivariate statistical data analysis tools commonly used to analyze similar data sets. In this study, simultaneous multichannel recording from guinea pig auditory cortex is examined using the SOM to assess the effectiveness and potential of the SOM in neurophysiological studies.

引用

页码：85 / 106

页数：22

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