Turing's analysis of computation and artificial neural networks.

A novel way to simulate Turing Machines (TMs) by Artificial Neural Networks (ANNs) is proposed. We claim that the proposed simulation is in agreement with the correct interpretation of Turing's analysis of computation; compatible with the current approaches to analyze cognition as an interactive agent-environment process; and physically realizable since it does not use connection weights with unbounded precision. A full description of an implementation of a universal TM into a recurrent sigmoid ANN focusing on the TM finite state control is given, leaving the tape, an infinite resource, as an external non-intrinsic feature. Also, motivated by the results on the limit of what can actually be computed by ANNs when noise is taken into account, we introduce the notion of Definite Turing Machine and investigate some of its properties. [ABSTRACT FROM AUTHOR]