Automatic Parallelization in Neural Computers

Abstract

Neural Networks are more than just mathematical tools to achieve optimization and learning via subsymbolic computations. Neural networks can perform several other types of computation, namely symbolic and chaotic computations. The discrete time neural model presented here can perform those three types of computations in a modular way. This paper focuses on how neural networks within this model can be used to automatically parallelize computational processes.