| Budget Amount *help |
¥16,770,000 (Direct Cost: ¥12,900,000、Indirect Cost: ¥3,870,000)
Fiscal Year 2023: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2022: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2021: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2020: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2019: ¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
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| Outline of Final Research Achievements |
The purpose of this research is to develop a circuit architecture for implementing the reservoir computing (RC) model in a single-electron device. RC's greatest feature is a method for setting connections and coupling weights between neurons contained in a part called the reservoir, which do not require settings and methods that have needed to be fully considered in general neural networks.
In this study, I aimed to construct a new single-electron information processing mechanism that can solve the problems of the number of wires, learning (changing the weight of synaptic connections), and noise utilization, which are issues in single-electron circuit research, and found the possibility of such a mechanism.
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