  In this study, we propose a novel deep neural network and its supervised
learning method that uses a feedforward supervisory signal. The method is
inspired by the human visual system and performs human-like association-based
learning without any backward error propagation. The feedforward supervisory
signal that produces the correct result is preceded by the target signal and
associates its confirmed label with the classification result of the target
signal. It effectively uses a large amount of information from the feedforward
signal, and forms a continuous and rich learning representation. The method is
validated using visual recognition tasks on the MNIST handwritten dataset.
