In connectionism and its offshoots, models acquire functionality through externally controlled learning schedules. This undermines the claim of these models to autonomy. Providing these models with intrinsic biases is not a solution, as it makes their function dependent on design assumptions. Between these two alternatives, there is room for approaches based on spontaneous self-organization. Structural reorganization in adaptation to spontaneous activity is a well-known phenomenon in neural development. It is proposed here as a way to prepare connectionist models for learning and enhance the autonomy of these models.