Neural Computation
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We show how a layered neural adaptation process comes to learn a simple default mapping from inputs to outputs. Then, as a succeeding step, it learns to use context for detection and handling of exceptions from this default mapping. Thereby tasks can be learnt in a step-wise manner, and the system exhibits graceful degradation if higher structures in the system are damaged.