Implementation of universal computation via small recurrent finite precision neural networks
J. Nicholas Hobbs, Hava T. Siegelmann · 2015
We design and implement a small neural network, comprised of 52 fixed precision neurons - computationally equivalent to a bounded memory Universal Turing Machine; this design is an order of magnitude smaller than the smallest known universal neural nets. The network is the core of a practical universal neural computer; all neurons have fixed precision and a small set of simple weights. External memory will be used, or additional neurons dynamically recruited for more memory intensive calculations or input.