Circuits of the mind
On the computational power of neural nets
Journal of Computer and System Sciences
Complexity and real computation
Complexity and real computation
Journal of the ACM (JACM)
Simulating the mind: a gaunlet thrown to computer science
ACM Computing Surveys (CSUR)
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Formal languages and their relation to automata
Formal languages and their relation to automata
Emergence of a Super-Turing Computational Potential in Artificial Living Systems
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Computational Power of Neuroidal Nets
SOFSEM '99 Proceedings of the 26th Conference on Current Trends in Theory and Practice of Informatics on Theory and Practice of Informatics
Beyond the Turing Limit: Evolving Interactive Systems
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
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The neuroidal tabula rasa (NTR) as a hypothetical device which is capable of performing tasks related to cognitive processes in the brain was introduced by L. G. Valiant in 1994. Neuroidal nets represent a computational model of the NTR. Their basic computational element is a kind of a programmable neuron called neuroid. Essentially it is a combination of a standard threshold element with a mechanism that allows modification of the neuroid's computational behaviour. This is done by changing its state and the settings of its weights and of threshold in the course of computation. The computational power of an NTR crucially depends both on the functional properties of the underlying update mechanism that allows changing of neuroidal parameters and on the universe of allowable weights. We will define instances of neuroids for which the computational power of the respective finite-size NTR ranges from that of finite automata, through Turing machines, upto that of a certain restricted type of BSS machines that possess super-Turing computational power. The latter two results are surprising since similar results were known to hold only for certain kinds of analog neural networks.