Proceedings of the Annual Meeting of the Cognitive Science Society

Synchrony of firing has recently become a popular technique for dynamic binding in neural networks, and has been applied to numerous problem domains. However, hierarchical structures are difficult to represent using synchrony for binding. This paper presents our progress toward a framework for representing hierarchies in a neural network using synchrony for dynamic binding. We illustrate the approach with a model of analogical mapping. The model (IMM2) uses synchrony to bind case roles to objects within propositions. Hierarchies are established by allowing units representing propositions to play a dual role, acting both as the argument of one proposition and as a pointer to another.