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A General Assembly as implementation of a Hebbian rule in a Boolean Neural Network

机译:大会作为布尔神经网络中的Hebbian规则的实施

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Usually the Hebbian learning spontaneously seems to produce associative memory behavior in the netowrk 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 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.
机译:通常,Hebbian学习自发地似乎在应用的Netowrk中产生关联的内存行为。一旦计算到输入的响应,由Hebbian规则执行的无监督学习会自动在网络中创建关联。我们在这里讨论的范式不同于古典无监督的学习范式,它是一个非常一般的解决方案,用于在布尔神经网络中实施Hebbian规则。我们的系统可能不被视为仅作为关联内存,它既是一个控制器和分类器。

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