The above architectures compose much of the research that has been done in the field, but certainly there are many other possible NN architectures. In passing, the following architectures are different from those previously presented. Often the choice of learning algorithm is left up to the user. This is very useful in character recognition, for example. For an Autoassociative net, the training input and target output vectors are identical.
Connections are bidirectional, so that the net can recall one pattern of a pair given the other.
Recurrent nets are those in which some or all of the neurons are connected to themselves. The Hopfield net is an example.