| Title |
Optoelectronic Reservoir Computing
|
|---|---|
| Published in |
Scientific Reports, February 2012
|
| DOI | 10.1038/srep00287 |
| Pubmed ID | |
| Authors |
Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, S. Massar |
| Abstract |
Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an optoelectronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 20% |
| Belgium | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 418 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Belgium | 4 | <1% |
| United States | 4 | <1% |
| France | 2 | <1% |
| Japan | 2 | <1% |
| India | 1 | <1% |
| Canada | 1 | <1% |
| Australia | 1 | <1% |
| Spain | 1 | <1% |
| Germany | 1 | <1% |
| Other | 2 | <1% |
| Unknown | 399 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 118 | 28% |
| Researcher | 56 | 13% |
| Student > Master | 47 | 11% |
| Student > Bachelor | 23 | 6% |
| Professor | 20 | 5% |
| Other | 72 | 17% |
| Unknown | 82 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Physics and Astronomy | 122 | 29% |
| Engineering | 100 | 24% |
| Computer Science | 43 | 10% |
| Materials Science | 20 | 5% |
| Mathematics | 7 | 2% |
| Other | 33 | 8% |
| Unknown | 93 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 23. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 January 2023.
All research outputs
#1,536,511
of 24,393,999 outputs
Outputs from Scientific Reports
#14,528
of 132,765 outputs
Outputs of similar age
#8,146
of 158,924 outputs
Outputs of similar age from Scientific Reports
#18
of 136 outputs
Altmetric has tracked 24,393,999 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 132,765 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.6. This one has done well, scoring higher than 89% of its peers.
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We're also able to compare this research output to 136 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.