Logical computation on a fractal neural substrate
Simon D. Levy, Jordan B. Pollack · 2002
Attempts to use neural networks to model recursive symbolic processes like logic 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 recursive auto-associative memory, a new connectionist unification model based on a fusion of recurrent neural networks with fractal geometry. Using a logical programming language as our modeling domain, we show how this approach solves many of the problems faced by earlier connectionist models, supporting arbitrarily large sets of logical expressions.