| Project/Area Number |
10680391
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | HIMEJI INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
MATSUI Nobuyuki Himeji Institute of Technology Computer Engineering Professor, 工学部, 教授 (10173783)
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| Co-Investigator(Kenkyū-buntansha) |
NISHIMURA Haruhiko Hyogo University of Education Studies of Information Science, Professor, 学校教育学部, 教授 (40218201)
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| Project Period (FY) |
1998 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥3,000,000 (Direct Cost: ¥3,000,000)
Fiscal Year 2000: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1999: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1998: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | quantum computing / qubit neuron / neural network / attention / perceptual alternation / coupled oscillator / chaos / stochastic resonance / 量子ビット / 創発 / 量子描像ニューロン / 視認知 / 認知交替 / 量子回路 |
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| Research Abstract |
In this research, we dealt with the attention phenomena of human perceptual-alternation and its quantum neuro computing model. In order to construct a new framework for describing the cohesiveness of the distribution and synthesis inherent in a neural network for this perceptual-alternation, a neural state is described quantum dynamically and a qubitlike neural network corresponding to the quantum circuit of quantum computation is studied. As a result, we construct the feed-forward neural network with our qubit neuron model, and discuss its information processing abilities with conventional neuron models through the learning simulations of the 4bit parity check problem and the general function identification problem, and we obtain the performance figure of learning. From these figures, we find that our qubit neuron model is more excellent than the conventional one. From our main results, we conclude that our qubit neuron model is an effective method of enhancing the performance of neural networks. We also construct a neural network model for perceptual alternation of ambiguous figures by using coupled oscillatory neurons or chaos neurons that can describe the timing of neuronal firing. From our results of simulations with this model, it is found that the model is able to represent the perceptual alternation in terms of the phase activities and synchronizations of neuronal phases. It is also shown that the distribution of perceptual duration fits Gamma distribution well as it is reported in psychophysical experiments. There, however, remains a further investigation of how to connect our qubit neuron with the coupled oscillatory perceptual-alternation model.
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