Hebbian学习和LMS算法

B. Widrow
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引用次数: 5

摘要

Hebbian学习是神经科学的基本前提之一。Widrow和Hoff的LMS(最小均方)算法是目前世界上应用最广泛的学习算法。Hebbian学习是无监督的。LMS学习是有监督的。然而,可以构造一种形式的LMS来执行无监督学习和实现Hebbian学习。结合这两种范式,创造了一种新的无监督学习算法,具有实际的工程应用,并为活体神经网络的学习提供了见解。一个基本的问题是,学习是如何在活的神经网络中发生的?自然界在神经元和突触水平上的学习算法很可能是Hebbian-LMS算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hebbian learning and the LMS algorithm
Hebbian learning is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used learning algorithm. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm that has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is, how does learning take place in living neural networks? The learning algorithm practiced by nature at the neuron and synapse level may well be the Hebbian-LMS algorithm.
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