Backpropagation is perhaps the most commonly used neural network learning algorithm. Several different "flavors" of backpropagation have been developed over the years, several of which have been implemented in the PDP++ software, including the use of different error functions such as cross-entropy, and recurrent backprop, from the simple recurrent network to the Almeida-Pineda algorithm up to the real-time continuous recurrent backprop. The implementation allows the user to extend the unit types to use different activation and error functions in a straightforward manner.
Note that the simple recurrent networks (SRN, a.k.a. Elman networks) are described in the feedforward backprop section, as they are more like feedforward networks than the fully recurrent ones.