Name
Abstract | ||
|---|---|---|
Reservoir computing is a computational framework suited for sequential data processing, consisting of a reservoir part and a read-out part. Not only theoretical and numerical studies on reservoir computing but also its implementation with physical devices have attracted much attention. In most studies, the reservoir part is constructed with identical units. However, a variability of physical units is inevitable, particularly when implemented with nano/micro devices. Here we numerically examine the effect of variability of reservoir units on computational performance. We show that the heterogeneity in reservoir units can be beneficial in reducing the prediction error in the reservoir computing system with a simple cycle reservoir. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46687-3_20 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Reservoir computing,Sequential data processing,Simple cycle reservoir,Heterogeneous neurons,Energy efficiency | Sequential data,Space-based architecture,Architecture,Mean squared prediction error,Efficient energy use,Computer science,Reservoir computing,Distributed computing | Conference |
Volume | ISSN | Citations |
9947 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gouhei Tanaka | 1 | 51 | 11.80 |
| Ryosho Nakane | 2 | 41 | 6.96 |
| Toshiyuki Yamane | 3 | 61 | 9.08 |
| Daiju Nakano | 4 | 55 | 8.65 |
| Seiji Takeda | 5 | 2 | 1.38 |
| Shigeru Nakagawa | 6 | 2 | 1.05 |
| Akira Hirose | 7 | 426 | 67.35 |