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| 001 | 1043040 | ||
| 005 | 20250627204348.0 | ||
| 024 | 7 | _ | |a arXiv:2505.21185 |2 arXiv |
| 024 | 7 | _ | |a 10.48550/arXiv.2505.21185 |2 doi |
| 037 | _ | _ | |a FZJ-2025-02733 |
| 100 | 1 | _ | |a Senk, Johanna |0 P:(DE-Juel1)162130 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Constructive community race: full-density spiking neural network model drives neuromorphic computing |
| 260 | _ | _ | |c 2025 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1751007085_3940 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cortical microcircuit below $1\,\text{mm}^2$ of brain surface. The model reproduces fundamental features of brain activity but its impact remained limited because of its computational demands. For theory and simulation, however, the model was a breakthrough because it removes uncertainties of downscaling, and larger models are less densely connected. This sparked a race in the neuromorphic computing community and the model became a de facto standard benchmark. Within a few years real-time performance was reached and surpassed at significantly reduced energy consumption. We review how the computational challenge was tackled by different simulation technologies and derive guidelines for the next generation of benchmarks and other domains of science. |
| 536 | _ | _ | |a 5234 - Emerging NC Architectures (POF4-523) |0 G:(DE-HGF)POF4-5234 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a 5235 - Digitization of Neuroscience and User-Community Building (POF4-523) |0 G:(DE-HGF)POF4-5235 |c POF4-523 |f POF IV |x 1 |
| 536 | _ | _ | |a EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) |0 G:(EU-Grant)101147319 |c 101147319 |f HORIZON-INFRA-2022-SERV-B-01 |x 2 |
| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 3 |
| 536 | _ | _ | |a HiRSE_PS - Helmholtz Platform for Research Software Engineering - Preparatory Study (HiRSE_PS-20220812) |0 G:(DE-Juel-1)HiRSE_PS-20220812 |c HiRSE_PS-20220812 |x 4 |
| 536 | _ | _ | |a ACA - Advanced Computing Architectures (SO-092) |0 G:(DE-HGF)SO-092 |c SO-092 |x 5 |
| 588 | _ | _ | |a Dataset connected to arXivarXiv |
| 650 | _ | 7 | |a Performance (cs.PF) |2 Other |
| 650 | _ | 7 | |a Distributed, Parallel, and Cluster Computing (cs.DC) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Kurth, Anno C. |b 1 |
| 700 | 1 | _ | |a Furber, Steve |b 2 |
| 700 | 1 | _ | |a Gemmeke, Tobias |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Golosio, Bruno |b 4 |
| 700 | 1 | _ | |a Heittmann, Arne |b 5 |
| 700 | 1 | _ | |a Knight, James C. |b 6 |
| 700 | 1 | _ | |a Müller, Eric |b 7 |
| 700 | 1 | _ | |a Noll, Tobias |b 8 |
| 700 | 1 | _ | |a Nowotny, Thomas |b 9 |
| 700 | 1 | _ | |a Coppola, Gorka Peraza |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Peres, Luca |b 11 |
| 700 | 1 | _ | |a Rhodes, Oliver |b 12 |
| 700 | 1 | _ | |a Rowley, Andrew |b 13 |
| 700 | 1 | _ | |a Schemmel, Johannes |b 14 |
| 700 | 1 | _ | |a Stadtmann, Tim |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Tetzlaff, Tom |0 P:(DE-Juel1)145211 |b 16 |u fzj |
| 700 | 1 | _ | |a Tiddia, Gianmarco |b 17 |
| 700 | 1 | _ | |a van Albada, Sacha J. |0 P:(DE-Juel1)138512 |b 18 |u fzj |
| 700 | 1 | _ | |a Villamar, José |0 P:(DE-Juel1)191583 |b 19 |u fzj |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 20 |u fzj |
| 773 | _ | _ | |a 10.48550/arXiv.2505.21185 |y 2025 |t arXiv |
| 909 | C | O | |o oai:juser.fz-juelich.de:1043040 |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)162130 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 3 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 10 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a RWTH Aachen |0 I:(DE-588b)36225-6 |k RWTH |b 15 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 16 |6 P:(DE-Juel1)145211 |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 20 |6 P:(DE-Juel1)144174 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5234 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5235 |x 1 |
| 914 | 1 | _ | |y 2025 |
| 920 | _ | _ | |l no |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-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 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-10-20170113 |
| 980 | _ | _ | |a UNRESTRICTED |
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