| Project/Area Number |
10650353
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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 |
情報通信工学
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| Research Institution | The Unicersity of Electro-Communications |
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Principal Investigator |
HARUHISA Takahashi The Unicersity of Electro-Communications, Information and Communication Engineering, Professor, 電気通信学部, 教授 (90135418)
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| Co-Investigator(Kenkyū-buntansha) |
MITSUO Wakatsuki The Unicersity of Electro-Communications, Information and Communication Engineering, Assistant Professor, 電気通信学部, 助手 (30251705)
TETSURO Nishino The Unicersity of Electro-Communications, Information and Communication Engineering, Associate Professor, 電気通信学部, 助教授 (10198484)
ETSUJI Tomita The Unicersity of Electro-Communications, Information and Communication Engineering, Professor, 電気通信学部, 教授 (40016598)
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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 |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2000: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1999: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 1998: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Covariance / phasor neural network / binding / Boltsmann machine / ニューラルネットワーク / 複素ニューロン / パルスニューロン / 機能的結合 / ボレッマンマシン |
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| Research Abstract |
The information binding in human brain is getting important to explain and to understand how human being can recognize the outer world so easily. Besides this, we expect that applying the binding mechanism to recognizer, we can get much better recognition performance. In this research we developed the neural networks that can well explain the information binding in the human brain. Based on the fact that the previous neural models are difficult to represent the spike synchronization or asynchronization, we developed two phasor neural models. One is the phase-rate oscillating neural model, in which the phase represents the sinusoidal phase. The other is covariance neural model, in which the cosine of the phase difference between two neurons represents the covariance coefficient. In both model we implemented the Hebb learning and Boltzmann learning rules to get the efficient learning. From the computer simulation results, we showed that the proposed learning rules work much more efficient than ordinal models. Applying the knowledge obtained from these models, we proposed the brain wave recognizer which shows much higher performance than previous. We also developed the theory of generalization in learning.
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