Abstract: A digital neural network architecture including a forward cascade of layers of neurons, having one input channel and one output channel, for forward processing of data examples that include many data packets. Backward cascade of layers of neurons, having one input channel and one output channel, for backward propagation learning of errors of the processed data examples. Each packet being of a given size. The forward cascade is adapted to be fed, through the input channel, with a succession of data examples and to deliver a succession of partially and fully processed data examples each consisting of a plurality of packets. The fully processed data examples are delivered through the one output channel. Each one of the layers is adapted to receive as input in its input channel a first number of data packets per time unit and to deliver as output in its output channel a second number of data packets per time unit.