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| 001 | 912537 | ||
| 005 | 20221216132046.0 | ||
| 024 | 7 | _ | |a 2128/33146 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-05710 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Yegenoglu, Alper |0 P:(DE-Juel1)161462 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a End of year colloquium 2022 at JSC |c Jülich |d 2022-12-08 - 2022-12-08 |w Germany |
| 245 | _ | _ | |a Optimizing Spiking Neural Networks with L2L on HPC systems |f 2022-12-08 - |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Talk (non-conference) |b talk |m talk |0 PUB:(DE-HGF)31 |s 1671182565_8017 |2 PUB:(DE-HGF) |x Other |
| 336 | 7 | _ | |a Other |2 DINI |
| 520 | _ | _ | |a In my talk I present the optimization of spiking neural networks (SNN) on HPC system using the L2L framework. I explain the problems when training SNNs to learn to solve tasks, then I introduce the concept of learning to learn and the framework L2L which implements the concept. Furthermore, I describe how optimization can be applied with L2L on SNNs and showcase two examples, namely optimizing a spiking reservoir network to classify digits and a swarm with a foraging behaviour. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 1 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 2 |
| 536 | _ | _ | |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) |0 G:(DE-Juel1)PHD-NO-GRANT-20170405 |c PHD-NO-GRANT-20170405 |x 3 |
| 536 | _ | _ | |a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) |0 G:(DE-Juel1)HDS-LEE-20190612 |c HDS-LEE-20190612 |x 4 |
| 536 | _ | _ | |a CSD-SSD - Center for Simulation and Data Science (CSD) - School for Simulation and Data Science (SSD) (CSD-SSD-20190612) |0 G:(DE-Juel1)CSD-SSD-20190612 |c CSD-SSD-20190612 |x 5 |
| 536 | _ | _ | |a SLNS - SimLab Neuroscience (Helmholtz-SLNS) |0 G:(DE-Juel1)Helmholtz-SLNS |c Helmholtz-SLNS |x 6 |
| 536 | _ | _ | |a ICEI - Interactive Computing E-Infrastructure for the Human Brain Project (800858) |0 G:(EU-Grant)800858 |c 800858 |f H2020-SGA-INFRA-FETFLAG-HBP |x 7 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/912537/files/yegenoglu_eoyc_2022.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:912537 |p openaire |p open_access |p VDB |p driver |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)161462 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l no |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a talk |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
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