Creative information processing systems based on non-standard computation toward brain potentiality

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In this research, we tried to find the methods of creative thinking based on non-standard computation toward brain potentiality such as perceptual alternation phenomena and investigated the emergence of this creative effect through system applications to these methods. In our study of the creative information processing systems, we have obtained the following main results:1. We found the multiplet as the characteristics of association in discrete states and discrete time Hopfield neural network described by quaternion.2. We confirmed the efficiency of the qubit neural network in the color night vision and data-mining applications.3. We developed the block learning SOM system with high adaptability under non-stationary environments.4. We cleared the efficiency of the multi-stable perception model by multi-layered bi-directional associative memory.5. We derived flocking behaviors of agents from a scheme of reinforcement learning, and also clarified the effect of reinforcement learning using chaotic exploration in the maze world problem.6. We constructed the algorithms on defect-tolerance in the asynchronous cellular automaton and showed that defective cells are detected and isolated by configurations of random flies that move around in cellular space.As a result, we clarified that our creative information processing systems have better performances than conventional ones in processing various problems. We have presented a lot of our fruitful results so that we consider our research aims have been achieved. However, many subjects remain to be explored, for examples, Clifford algebra approach, Bloch representation on qubit neuron state and its application to cellular neural network, etc. These are our future works.