We clarify that the only essential difference between the two major "categories" of unsupervised learning rules discussed in theories of artificial neural networks-the competitive learning and the Hebbian learning rules-is that lateral inhibition is present in the former and is absent in the later. We demonstrate analytically that a competitive learning neural network, which has synaptically localized memory, shows better tolerance over noise in training patterns in comparison with the Hopfield neural network, which uses a Hebbian-type learning rule without any lateral inhibition and has synaptically distributed memory.
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