| Project/Area Number |
15K21561
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Soft computing
Life / Health / Medical informatics
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| Research Institution | Fukuoka University |
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Principal Investigator |
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 確率共鳴 / 非同期ニューラルネットワーク / 興奮抑制バランス / 学習 / 非同期神経回路 / 相関 / パワースペクトル / ノイズ / カオス / STDP学習 / Izhikevich / 雑音 / 信号処理 / ノイズ同期 / 生体信号処理 |
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| Outline of Final Research Achievements |
To detect weak signals below a threshold of a detector, the stochastic resonance (SR) is widely used, including fishes and crayfish. Also SR is used for the engineering applications like Schmitt triggers. To realize the SR system, the detecters are required to be driven independently. Usually, this is achieved by adding noises to detectors. For real systems, however, to add independent noises to many detectors has many difficulties. To avoid this problems, this study proposed an idea to use the asynchronous neural network as detecters. The asynchronous neural network are composed of excitatory and inhibitory neurons, and by adjusting connecting strength, the element cells can be independent each other. By computer simulations, we confirmed its usefulness.
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