Generic constraints on underspecified target trajectories

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Abstract
Although general network learning rules are of undeniable interest, it is generally agreed that successful accounts of learning must incorporate domain-specific, a priori knowledge. Such knowledge might be used, for example, to determine the structure of a network or its initial weights. The author discusses a third possibility in which domain-specific knowledge is incorporated directly in a network learning rule via a set of constraints on activations. The approach uses the notion of a forward model to give constraints a domain-specific interpretation. This approach is demonstrated with several examples from the domain of motor learning.

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