  We present a generalization of conventional artificial neural networks that
allows for a functional equivalence to multi-expert systems. The new model
provides an architectural freedom going beyond existing multi-expert models and
an integrative formalism to compare and combine various techniques of learning.
(We consider gradient, EM, reinforcement, and unsupervised learning.) Its
uniform representation aims at a simple genetic encoding and evolutionary
structure optimization of multi-expert systems. This paper contains a detailed
description of the model and learning rules, empirically validates its
functionality, and discusses future perspectives.
