  The dynamics of interacting perceptrons is solved analytically. For a
directed flow of information the system runs into a state which has a higher
symmetry than the topology of the model. A symmetry breaking phase transition
is found with increasing learning rate. In addition it is shown that a system
of interacting perceptrons which is trained on the history of its minority
decisions develops a good strategy for the problem of adaptive competition
known as the Bar Problem or Minority Game.
