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AbstractWe 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 integrarive formalism to compare and combine various techniques of learning. (We consider gradient, EM, reinforcement, and unsupervised learn- ing.) Its uniform representation aims at a simple netic 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 perspec- tives.