  An Ashkin-Teller neural network, allowing for two types of neurons is
considered in the case of low loading as a function of the strength of the
respective couplings between these neurons. The storage and retrieval of
embedded patterns built from the two types of neurons, with different degrees
of (in)dependence is studied. In particular, thermodynamic properties including
the existence and stability of Mattis states are discussed. Furthermore, the
dynamic behaviour is examined by deriving flow equations for the macroscopic
overlap. It is found that for linked patterns the model shows better retrieval
properties than a corresponding Hopfield model.
