A general assembly as implementation of a Hebbian rule in a Boolean neural network

被引：0

作者：

Lauria, FE ^{[1
]}

Milo, M ^{[1
]}

Prevete, R ^{[1
]}

Visco, S ^{[1
]}

机构：

[1] Univ Naples, INFM, Dept Sci Fis, I-80125 Naples, Italy

来源：

NEURAL NETS - WIRN VIETRI-99 | 1999年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Usually the Hebbian learning spontaneously seems to produce associative memory behavior in the network where they are applied. The unsupervised learning performed by the Hebbian rule, automatically creates associations into the network as soon as the responses to the inputs are computed. The paradigm we are discussing here is different from the classical unsupervised learning paradigm, and it is a quite general solution for the implementation of a a Hebbian rule in a Boolean neural network. Our system may not be seen as an associative memory only, it is both a controller and a classifier.

引用

页码：266 / 271

页数：6

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