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| Hauptseite > Publikationsdatenbank > Reservoir Transfer on Analog Neuromorphic Hardware > print |
| 001 | 873633 | ||
| 005 | 20240313094942.0 | ||
| 024 | 7 | _ | |a 10.1109/NER.2019.8716891 |2 doi |
| 024 | 7 | _ | |a WOS:000469933200299 |2 WOS |
| 037 | _ | _ | |a FZJ-2020-00876 |
| 100 | 1 | _ | |a He, Xu |0 P:(DE-Juel1)171331 |b 0 |u fzj |
| 111 | 2 | _ | |a 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER) |c San Francisco |d 2019-03-20 - 2019-03-23 |w CA |
| 245 | _ | _ | |a Reservoir Transfer on Analog Neuromorphic Hardware |
| 260 | _ | _ | |c 2019 |
| 336 | 7 | _ | |a Abstract |b abstract |m abstract |0 PUB:(DE-HGF)1 |s 1580903721_16416 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Abstract |2 DataCite |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 500 | _ | _ | |a Tianlin Liu was supported by the FZJ through the project SMARTSTART Computational Neuroscience, DB001423. |
| 520 | _ | _ | |a Analog, unclocked, spiking neuromorphic microchips open new perspectives for implantable or wearable biosensors and biocontrollers, due to their low energy consumption and heat dissipation. However, the challenges from a computational point of view are formidable. Here we outline our solutions to realize the reservoir computing paradigm on such hardware and address the combined problems of low bit resolution, device mismatch, approximate neuron models, and timescale mismatch. The main contribution is a computational scheme, called Reservoir Transfer, which enables us to transfer the dynamical properties of a well-performing neural network which has been optimized on a digital computer, onto neuromorphic hardware that displays the abovementioned problematic properties. Here we present a case study of implementing an ECG heartbeat abnormality detector to showcase the proposed method. |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 0 |
| 536 | _ | _ | |a Smartstart - SMARTSTART Training Program in Computational Neuroscience (90251) |0 G:(EU-Grant)90251 |c 90251 |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef Conference |
| 700 | 1 | _ | |a Liu, Tianlin |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Hadaeghi, Fatemeh |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Jaeger, Herbert |0 P:(DE-HGF)0 |b 3 |
| 773 | _ | _ | |a 10.1109/NER.2019.8716891 |
| 909 | C | O | |o oai:juser.fz-juelich.de:873633 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)171331 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |2 G:(DE-HGF)POF3-500 |v Theory, modelling and simulation |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |
| 914 | 1 | _ | |y 2019 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 2 |
| 980 | _ | _ | |a abstract |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
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