In multistate neural associative memories, some neurons have small noise and the others have large noise. If we know which neurons have small noise, the noise tolerance could be improved. In this brief, we provide a novel method to reinforce neurons with small noise and apply our new method to images with the Gaussian noise. A complex-valued multistate neuron is decomposed to two neurons, referred to as high and low neurons. For the Gaussian noise, the high neurons are expected to have small noise. The noise tolerance is improved by reinforcement of high neurons. The computer simulations support the efficiency of reinforced neurons.

中文翻译：

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两级复值 Hopfield 神经网络。

在多态神经联想记忆中，一些神经元噪声小，而另一些神经元噪声大。如果我们知道哪些神经元的噪声较小，则可以提高噪声耐受性。在这个简介中，我们提供了一种新方法来增强具有小噪声的神经元，并将我们的新方法应用于具有高斯噪声的图像。一个复值多态神经元被分解为两个神经元，称为高神经元和低神经元。对于高斯噪声，期望高的神经元具有小的噪声。通过增强高神经元来提高噪声耐受性。计算机模拟支持增强神经元的效率。