This paper presents autoassociator neural networks. A first section reviews the architecture of these models, common learning rules, and presents sample simulations to illustrate their abilities. In a second section, the ability of these models to account for learning phenomena such as habituation is reviewed. The contribution of these networks to discussions about infant cognition is highlighted. A new, modular approach is presented in a third section. In the discussion, a role for these learning models in a broader developmental framework is proposed.