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 ...
