一个简单的受生物学启发的主成分分析仪- modh神经元模型

M. Jankovic
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引用次数: 0

摘要

单层神经网络无监督学习的新方法。讨论了网络。提出了一种基于Hebbian学习规则的无监督学习算法。分析了一个简单的神经元模型。所采用的神经元模型表示动态神经模型,该模型包含输入和输出之间的前馈和反馈连接。实际上,我们提出的学习算法可以更准确地命名为自监督学习算法,而不是无监督学习算法。这里提出的解决方案是一个改进的Hebbian规则,其中突触强度的变化不是与突触前和突触后的活动成比例,而是与突触前和突触后活动的平均值成比例。结果表明,模型神经元倾向于从平稳输入向量序列中提取主成分。避免了通常接受的用于稳定原始Hebb规则的附加衰减项。由于采用的网络结构,基本Hebb方案的实现不会导致突触强度不切实际的增长。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A simple biologically inspired principal component analyzer-ModH neuron model
A new approach to unsupervised learning in a single-layer neural. network is discussed. An algorithm for unsupervised learning based on Hebbian learning rule is presented. A simple neuron model is analyzed. Adopted neuron model represents dynamic neural model which contains both feed forward and feedback connections between input and output. Actually, proposed learning algorithm could be more correctly named self-supervised rather than unsupervised. The solution proposed here is a modified Hebbian rule in which the modification of the synaptic strength is proportional not to pre- and post-synaptic activity, but instead to the pre-synaptic and averaged value of post-synaptic activity. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence. Usually accepted additional decaying terms for the stabilization of original Hebb rule are avoided. Implementation of the basic Hebb scheme would not lead to unrealistic growth of the synaptic strengths, thanks to the adopted network structure.
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