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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Interactive Evolving Recurrent Neural Networks Are Super-Turing Universal
verfasst von : Jérémie Cabessa, Alessandro E. P. Villa
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2014
Verlag: Springer International Publishing
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Understanding the dynamical and computational capabilities of neural models represents an issue of central importance. In this context, recent results show that interactive evolving recurrent neural networks are super-Turing, irrespective of whether their synaptic weights are rational or real. We extend these results by showing that interactive evolving recurrent neural networks are not only super-Turing, but also capable of simulating any other possible interactive deterministic system. In this sense, interactive evolving recurrent neural networks represents a super-Turing universal model of computation, irrespective of whether their synaptic weights are rational or real.