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| 001 | 873632 | ||
| 005 | 20240313094942.0 | ||
| 020 | _ | _ | |a 978-3-030-30486-7 (print) |
| 020 | _ | _ | |a 978-3-030-30487-4 (electronic) |
| 024 | 7 | _ | |a 10.1007/978-3-030-30487-4_54 |2 doi |
| 024 | 7 | _ | |a 0302-9743 |2 ISSN |
| 024 | 7 | _ | |a 1611-3349 |2 ISSN |
| 024 | 7 | _ | |a WOS:000546494000054 |2 WOS |
| 037 | _ | _ | |a FZJ-2020-00875 |
| 100 | 1 | _ | |a Tetko, Igor V. |0 0000-0002-6855-0012 |b 0 |e Editor |
| 111 | 2 | _ | |a ICANN 2019: Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation pp |c Munich |d 2019-09-17 - 2019-09-19 |w Germany |
| 245 | _ | _ | |a Training Delays in Spiking Neural Networks |
| 260 | _ | _ | |c 2019 |
| 336 | 7 | _ | |a Abstract |b abstract |m abstract |0 PUB:(DE-HGF)1 |s 1593502935_1667 |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 Book |0 PUB:(DE-HGF)3 |2 PUB:(DE-HGF) |m book |
| 336 | 7 | _ | |a Output Types/Conference Abstract |2 DataCite |
| 336 | 7 | _ | |a OTHER |2 ORCID |
| 520 | _ | _ | |a Spiking Neural Networks (SNNs) are a promising computational paradigm, both to understand biological information processing and for low-power, embedded chips. Although SNNs are known to encode information in the precise timing of spikes, conventional artificial learning algorithms do not take this into account directly. In this work, we implement the spike timing by training the synaptic delays in a single layer SNN. We use two different approaches: a classical gradient descent and a direct algebraic method that is based on a complex-valued encoding of the spikes. Both algorithms are equally able to correctly solve simple detection tasks. Our work provides new optimization methods for the data analysis of highly time-dependent data and training methods for neuromorphic chips. |
| 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 Book Series |
| 700 | 1 | _ | |a Kůrková, Věra |0 0000-0002-8181-2128 |b 1 |e Editor |
| 700 | 1 | _ | |a Karpov, Pavel |0 0000-0003-4786-9806 |b 2 |e Editor |
| 700 | 1 | _ | |a Theis, Fabian |0 0000-0002-2419-1943 |b 3 |e Editor |
| 700 | 1 | _ | |a State, Laura |0 0000-0001-8084-5297 |b 4 |
| 700 | 1 | _ | |a Vilimelis Aceituno, Pau |0 0000-0002-1218-4009 |b 5 |e Corresponding author |
| 773 | _ | _ | |a 10.1007/978-3-030-30487-4_54 |
| 909 | C | O | |o oai:juser.fz-juelich.de:873632 |p openaire |p VDB |p ec_fundedresources |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 0000-0001-8084-5297 |
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| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
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| 920 | 1 | _ | |0 I:(DE-Juel1)INM-10-20170113 |k INM-10 |l Jara-Institut Brain structure-function relationships |x 2 |
| 980 | _ | _ | |a abstract |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a book |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
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| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
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