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Feedforward neural network models of cognitive development are reviewed within the framework of a functional distinction between learning and development. This analysis suggests that static architecture networks implement a learning theory, whereas generative architecture networks combine learning and development. Both types of networks are then evaluated in terms of genetic costs. Within a levels-of-innateness framework, generative architectures are viewed as more plausible than static ones. Static architecture networks appear to implement a form of nativistic elicitation.