Authors:
Jérémie Cabessa
and
Alessandro E. P. Villa
Affiliation:
University of Lausanne, Switzerland
Keyword(s):
Neural Computation, Analog Computation, Interactive Computation, Recurrent Neural Networks, Super-Turing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neuroinformatics and Bioinformatics
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
According to the Church-Turing Thesis, the classical Turing machine model is capable of capturing all possible aspects of algorithmic computation. However, in neural computation, several basic neural models were proven to be capable of computational capabilities located beyond the Turing limits. In this context, we present an overview of recent results concerning the super-Turing computational power of recurrent neural networks, and show that recurrent neural networks provide a suitable and natural model of computation beyond the Turing limits. We nevertheless don’t draw any hasty conclusion about the controversial issue of a possible predominance of biological intelligence over the potentialities of artificial intelligence.