This type of Backpropagation network has been successfully used in predicting financial markets because recurrent networks can learn sequences, so they are excellent for time series data.
A Backpropagation network with standard connections responds to a given input pattern with exactly the same output pattern every time the input pattern is presented. A recurrent network may respond to the same input pattern differently at different times, depending upon the patterns that have been presented as inputs just previously. Thus, the sequence of the patterns is as important as the input pattern itself.
Recurrent networks are trained the same as standard Backpropagation networks except that patterns must always be presented in the same order; random selection is not allowed. The one difference in structure is that there is one extra slab in the input layer that is connected to the hidden layer just like the other input slab. This extra slab holds the contents of one of the layers as it existed when the previous pattern was trained. In this way the network sees previous knowledge it had about previous inputs. This extra slab is sometimes called the network's "long term" memory.