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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]

This work addresses the problem of finding the adjustable parameters of a learning algorithm using Genetic Algorithms. This problem is also known as the model selection problem. In this paper, some model selection techniques (e.g., crossvalidation and bootstrap) are used as objective functions of a Genetic Algorithm. The Genetic Algorithm is modified in order to allow the efficient use of these objective functions by means of occam's razor, growing, and other heuristics. Some modifications explore intrinsic features of Genetic Algorithms, such as their ability to handle multiple and noise objective functions. The proposed techniques are very general and may be applied to a large range of learning algorithms. [ABSTRACT FROM AUTHOR]

Presents information on the papers selected from the VIIth Brazilian Symposium on Neural Networks proceedings, held in 2002. Presentation of a novel class of recurrent neural fuzzy networks; Proposed constructive and pruning methods; Investigation of artificial neural networks.