On the properties of the feedforward method: A simple training law for on-chip learning

This paper investigates the properties of the so-called feedforward method, which is a very simple training law suitable for on-chip learning. Its merit is conceptual and implementational simplicity. Its signals do not propagate in both directions and it works for various types of activation function, a feature that makes it particularly effective in the case of unmodeled activation functions. Extensive simulation has shown that this method is usually faster than backpropagation