  We study supervised learning and generalisation in coupled perceptrons
trained on-line using two learning scenarios. In the first scenario the teacher
and the student are independent networks and both are represented by an
Ashkin-Teller perceptron. In the second scenario the student and the teacher
are simple perceptrons but are coupled by an Ashkin-Teller type four-neuron
interaction term. Expressions for the generalisation error and the learning
curves are derived for various learning algorithms. The analytic results find
excellent confirmation in numerical simulations.
