Neural Computation
Neural networks and analog computation: beyond the Turing limit
Neural networks and analog computation: beyond the Turing limit
Neural network computations with negative triggering thresholds
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Performance of BSDT decoding algorithms based on locally damaged neural networks
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
BSDT Atom of Consciousness Model, AOCM: The Unity and Modularity of Consciousness
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
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Turing machine (TM) theory constitutes the theoretical basis for contemporary digital (von Neumann) computers. But it is problematic whether it could be an adequate theory of brain functions (computations) because, as it is widely accepted, the brain is a selectional device with blurred bounds between the areas responsible for data processing, control, and behavior. In this paper, by analogy with TMs, the optimal decoding algorithm of recent binary signal detection theory (BSDT) is presented in the form of a minimal onedimensional abstract selectional machine (ASM). The ASM's hypercomplexity is explicitly hypothesized, its optimal selectional and super-Turing computational performance is discussed. BSDT ASMs can contribute to a mathematically strict and biologically plausible theory of functional properties of the brain, mind/brain relations and super-Turing machines mimicking partially some cognitive abilities in animals and humans.