A Supervised Modified Hebbian Learning Method On Feed-forward Neural Networks
This work addresses the need for more biologically plausible learning algorithms in neural networks, though it is incremental as it modifies an existing Hebbian approach.
The authors tackled the problem of developing a biologically plausible alternative to backpropagation by proposing a supervised modified Hebbian learning method for feed-forward neural networks, achieving an accuracy of 70.4% on the MNIST test set and 71.48% on the validation set.
In this paper, we present a new supervised learning algorithm that is based on the Hebbian learning algorithm in an attempt to offer a substitute for back propagation along with the gradient descent for a more biologically plausible method. The best performance for the algorithm was achieved when it was run on a feed-forward neural network with the MNIST handwritten digits data set reaching an accuracy of 70.4% on the test data set and 71.48% on the validation data set.