We report a learning rule for neural networks that computes how much each neuron should contribute to minimize a giving cost function via the estimation of its target value. By theoretical analysis, we show that this learning rule contains backpropagation, Hebian learning, and additional terms. We also give a general technique for weights initialization. Our results are at least as good as those obtained with backpropagation. The neural networks are trained and tested in three problems: MNIST, MNIST-Fashion, and CIFAR-10 datasets. The associated code is available at this https URL
A novel neural network architecture is proposed that provides a unified framework for Hebbian and backpropagation-based learning. The learning rule for this architecture, called the hybrid learning rule, combines the features of the Hebbian learning rule, which is a good feature extractor, and the backpropagation algorithm, which is an excellent classifier. By combining these two learning rules into the hybrid learning rule, the hybrid learning rule should have the strengths of both without any of the weaknesses. The hybrid learning rule was applied to the problem of isolated character recognition. While the hybrid learning rule failed to perform better than the backpropagation algorithm, it did generate receptive fields similar to those found by R. Linsker (1986), T.D. Sanger (1989), and D.H. Hubel and T.N. Wiesel (1962). >