| dc.contributor.author | Lukoševičius, Mantas | |
| dc.contributor.author | Jaeger, Herbert | |
| dc.contributor.author | Schrauwen, Benjamin | |
| dc.date | 2012-11-01 | |
| dc.date.accessioned | 2018-01-08T09:16:10Z | |
| dc.date.available | 2018-01-08T09:16:10Z | |
| dc.date.issued | 2012 | |
| dc.identifier.issn | 1610-1987 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/11319 | |
| dc.description.abstract | Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the readouts from it. It started ten years ago and is currently a prolific research area, giving important insights into RNNs, practical machine learning tools, as well as enabling computation with non-conventional hardware. Here we give a brief introduction into basic concepts, methods, insights, current developments, and highlight some applications of RC. | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 26, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Echo state network | |
| dc.subject | Recurrent neural network | |
| dc.subject | Reservoir computing | |
| dc.title | Reservoir Computing Trends | |
| dc.type | Text/Journal Article | |
| mci.reference.pages | 365-371 | |
| gi.identifier.doi | 10.1007/s13218-012-0204-5 | |
