INTERACTIVE EVOLVING RECURRENT NEURAL NETWORKS ARE SUPER-TURING
Jérémie Cabessa · 2012
We consider a model of evolving recurrent neural networks where the synaptic weights can change over time, and we study the computational power of such networks in a basic context of interactive computation. In this framework, we prove that both models of rationaland real-weighted interactive evolving neural networks are computationally equivalent to interactive Turing machines with advice, and hence capable of super-Turing capabilities. These results support the idea that some intrinsic feature of biological intelligence might be beyond the scope of the current state of artificial intelligence, and that the concept of evolution might be strongly involved in the computational capabilities of biological neural networks. It also shows that the computational power of interactive evolving neural networks is by no means influenced by nature of their synaptic weights.