Turing Computation with Recurrent Artificial Neural Networks

Understand

We improve the results by Siegelmann & Sontag (1995) by providing a novel and parsimonious constructive mapping between Turing Machines and Recurrent Artificial Neural Networks, based on recent developments of Nonlinear Dynamical Automata.

• The architecture of the resulting R-ANNs is simple and elegant, stemming from its transparent relation with the underlying NDAs.
• These characteristics yield promise for developments in machine learning methods and symbolic computation with continuous time dynamical systems.
• A framework is provided to directly program the R-ANNs from Turing Machine descriptions, in absence of network training.