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| 001 | 873629 | ||
| 005 | 20240313103124.0 | ||
| 024 | 7 | _ | |a arXiv:1905.12116 |2 arXiv |
| 024 | 7 | _ | |a 2128/24251 |2 Handle |
| 024 | 7 | _ | |a altmetric:61268544 |2 altmetric |
| 037 | _ | _ | |a FZJ-2020-00872 |
| 100 | 1 | _ | |a Liu, Tianlin |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Harnessing Slow Dynamics in Neuromorphic Computation |f - 2019-05-30 |
| 260 | _ | _ | |c 2019 |
| 300 | _ | _ | |a 53 p. |
| 336 | 7 | _ | |a Output Types/Supervised Student Publication |2 DataCite |
| 336 | 7 | _ | |a Thesis |0 2 |2 EndNote |
| 336 | 7 | _ | |a MASTERSTHESIS |2 BibTeX |
| 336 | 7 | _ | |a masterThesis |2 DRIVER |
| 336 | 7 | _ | |a Master Thesis |b master |m master |0 PUB:(DE-HGF)19 |s 1580905475_10825 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a SUPERVISED_STUDENT_PUBLICATION |2 ORCID |
| 500 | _ | _ | |a Master thesis of Tianlin LiuLiu was supported by the FZJ through the project SMARTSTART Computational Neuroscience, DB001423. |
| 502 | _ | _ | |a Masterarbeit, Jacobs University Bremen, 2019 |c Jacobs University Bremen |b Masterarbeit |d 2019 |
| 520 | _ | _ | |a Neuromorphic Computing is a nascent research field in which models and devices are designed to process information by emulating biological neural systems. Thanks to their superior energy efficiency, analog neuromorphic systems are highly promising for embedded, wearable, and implantable systems. However, optimizing neural networks deployed on these systems is challenging. One main challenge is the so-called timescale mismatch: Dynamics of analog circuits tend to be too fast to process real-time sensory inputs. In this thesis, we propose a few working solutions to slow down dynamics of on-chip spiking neural networks. We empirically show that, by harnessing slow dynamics, spiking neural networks on analog neuromorphic systems can gain non-trivial performance boosts on a battery of real-time signal processing tasks. |
| 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 arXivarXiv |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/873629/files/1905.12116.pdf |y OpenAccess |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/873629/files/1905.12116.pdf?subformat=pdfa |x pdfa |y OpenAccess |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-HGF)0 |
| 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 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
| 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)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 2 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a master |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
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