| Project/Area Number |
16K12487
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | Kobe University |
|---|
Principal Investigator |
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
上原 邦昭 神戸大学, システム情報学研究科, 教授 (60160206)
|
|---|
| Project Period (FY) |
2016-04-01 – 2019-03-31
|
| Project Status |
Completed (Fiscal Year 2018)
|
| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2018: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
|
|---|
| Keywords | ニューラルネットワーク / ゆらぎ / 機械学習 / 非同期順序回路 / 人工ニューラルネットワーク / 生体神経細胞 / 生成モデル / 電子回路 |
|---|
| Outline of Final Research Achievements |
This study aimed at developing a new generative machine learning algorithm and implementation method by leveraging the stochastic behavior (uncertainty) and spike-time coding, which biological neural networks have and artificial ones do not. We derived a mathematical model bridging the gap between stochasticity and homeostasis of neurons. We proposed a biologically-plausible learning algorithm by considering the discrete spikes as sampled drawn from a probabilistic distribution. For circuit implementation, we proposed a new approximation method that requires only one-third circuit resources. We also proposed some practical methods based on the stochasticity.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究は(1)生物の脳が学習するメカニズムにおいて，従来不明であったゆらぎの貢献や時間的な適応について，数式によるモデルを構築できた．(2)確率的な現象をモデル化できる機械学習手法であるボルツマンマシンを，高密度に電子回路実装する手法を開発した．(3)いわゆる深層学習に確率的要素(ゆらぎや不確実性)を持ち込むことで，高い精度を達成したり，小規模データへ適応可能な手法を開発できた．
|
