| Budget Amount *help |
¥69,940,000 (Direct Cost: ¥53,800,000、Indirect Cost: ¥16,140,000)
Fiscal Year 2017: ¥16,250,000 (Direct Cost: ¥12,500,000、Indirect Cost: ¥3,750,000)
Fiscal Year 2016: ¥11,440,000 (Direct Cost: ¥8,800,000、Indirect Cost: ¥2,640,000)
Fiscal Year 2015: ¥11,700,000 (Direct Cost: ¥9,000,000、Indirect Cost: ¥2,700,000)
Fiscal Year 2014: ¥15,860,000 (Direct Cost: ¥12,200,000、Indirect Cost: ¥3,660,000)
Fiscal Year 2013: ¥14,690,000 (Direct Cost: ¥11,300,000、Indirect Cost: ¥3,390,000)
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| Outline of Final Research Achievements |
Novel computing methods and architectures were constructed for coarse-grained molecular devices and materials that utilize noise and fluctuations. The following two computing methods were developed. A cellular-automaton model that imitates neuromorphic spike generation in a molecular spiking neural networks was developed. By using dynamics of the model, performance of 'reservoir computing' was evaluated by using standard benchmarks for memorizing complex temporal sequences. The results showed that the model was able to learn complex temporal sequences at high precision. For the physical demonstration, one had to resolve two issues, i.e., reservoir's initial-value dependence and difficulty in reproduction of complex temporal sequences. To resolve this problem, a nonlinear phenomena, called consistency, was introduced in the reservoir, which resulted in successful generation of initial-value independent and reproductive complex temporal sequences.
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