On the Relationship between Symbolic and Neural Computation

Whitney Tabor and Dalia Terhesiu

There is a need to clarify the relationship between traditional symbolic computation and neural network computation. We suggest that traditional context-free grammars are best understood as a special case of neural network computation; the special case derives its power from the presence of certain kinds of symmetries in the weight values. We describe a simple class of stochastic neural networks, Stochastic Linear Dynamical Automata (SLDAs), define Lyapunov Exponents for these networks, and show that they exhibit a significant range of dynamical behaviors---contractive and chaotic, with context free grammars at the boundary between these regimes. Placing context-free languages in this more general context has allowed us, in previous work, to make headway on the challenging problem of designing neural mechanisms that can learn them.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.