The flow of the signals in neural networks can be either in only one direction or in recurrence. In the first case, we call the neural network architecture feed-forward, since the input signals are fed into the input layer, then, after being processed, they are forwarded to the next layer, just as shown in the following figure. MLPs and radial basis functions are also good examples of feed-forward networks. In the following figure is shown an MLPs architecture:

When the neural network has some kind of internal recurrence, meaning that the signals are fed back to a neuron or layer that has already received and processed that signal, the network is of the type feedback, as shown in the following image:

The special reason to add recurrence in a network is the production of a dynamic behavior, particularly when the network addresses problems involving time series or pattern recognition, that require an internal memory to reinforce the learning process. However, such networks are particularly difficult to train, eventually failing to learn. Most of the feedback networks are single layer, such as the Elman and Hopfield networks, but it is possible to build a recurrent multilayer network, such as echo and recurrent MLP networks.