Infinite RAAM: A Principled Connectionist Substrate for Cognitive Modeling

Unification-based approaches have come to play an important role in both theoretical and applied modeling of cognitive processes, most notably natural language. Attempts to model such processes using neural networks have met with some success, but have faced serious hurdles caused by the limitations of standard connectionist coding schemes. As a contribution to this effort, this paper presents recent work in Infinite RAAM (IRAAM), a new connectionist unification model. Based on a fusion of recurrent neural networks with fractal geometry, IRAAM allows us to understand the behavior of these networks as dynamical systems. Using a logical programming language as our modeling domain, we show how this dynamical-systems approach solves many of the problems faced by earlier connectionist models, supporting unification over arbitrarily large sets of recursive expressions. We conclude that IRAAM can provide a principled connectionist substrate for unification in a variety of cognitive modeling domains. Language and Connectionism: Three