Analysis of Perceptual Alternation Phenomena and Artificial Consciousness Model based on Non-standard Computation

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In this research, we dealt with the perceptual-alternation phenomena and a theoretical scheme for Artificial Consciousness based on Non-standard Computation. In order to construct a new framework for this perceptual-alternation, we have developed new computation methods, describing the cohesiveness of distribution and synthesis inherent in a neural network. This is called Non-standard computation models : Qubit neural network, Quaternion neural network, Fault-tolerant Asynchronous Cellular Automata, Neural Chaos model, Coupled Oscillatory neural network, Neutral variation Genetic Algorithm, Coupled Gradient Descent method, Stochastic Resonance neural network, Block-Matching based Self-Organizing Map and so on. Discussing them from a theoretical and unified point of view, we investigated their information processing abilities by processing concrete problems as bench mark tests : n-bits parity check problems, general function identification problems, image compression problems, non-holonomic control problems, etc. As a result, we clarified that our non-standard computation models have better performances than conventional models in processing various problems. We have not only established the framework of non-standard computing methods, but also constructed the non-standard computation description on the binding problem. We, furthermore, indicated the existence of quantal state in brain by means of our analysis of the perceptual alternation as a change of the mode of consciousness. This will contribute to the construction of models of computational Artificial Consciousness for future works.