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| 001 | 908053 | ||
| 005 | 20240313103120.0 | ||
| 024 | 7 | _ | |a 10.1088/2634-4386/ac55fc |2 doi |
| 024 | 7 | _ | |a 2128/31303 |2 Handle |
| 024 | 7 | _ | |a altmetric:125537151 |2 altmetric |
| 024 | 7 | _ | |a WOS:001064089200001 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-02348 |
| 082 | _ | _ | |a 621.3 |
| 100 | 1 | _ | |a Kurth, Anno C |0 P:(DE-Juel1)176776 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Sub-realtime simulation of a neuronal network of natural density |
| 260 | _ | _ | |a Bristol |c 2022 |b IOP Publishing Ltd. |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1655197237_19649 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Full scale simulations of neuronal network models of the brain are challenging due to the high density of connections between neurons. This contribution reports run times shorter than the simulated span of biological time for a full scale model of the local cortical microcircuit with explicit representation of synapses on a recent conventional compute node. Realtime performance is relevant for robotics and closed-loop applications while sub-realtime is desirable for the study of learning and development in the brain, processes extending over hours and days of biological time. |
| 536 | _ | _ | |a 5234 - Emerging NC Architectures (POF4-523) |0 G:(DE-HGF)POF4-5234 |c POF4-523 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) |0 G:(EU-Grant)720270 |c 720270 |f H2020-Adhoc-2014-20 |x 1 |
| 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 2 |
| 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 3 |
| 536 | _ | _ | |a ACA - Advanced Computing Architectures (SO-092) |0 G:(DE-HGF)SO-092 |c SO-092 |x 4 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Senk, Johanna |0 P:(DE-Juel1)162130 |b 1 |
| 700 | 1 | _ | |a Terhorst, Dennis |0 P:(DE-Juel1)169778 |b 2 |
| 700 | 1 | _ | |a Finnerty, Justin |0 P:(DE-Juel1)174496 |b 3 |
| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 4 |
| 770 | _ | _ | |a Focus Issue on Energy Efficient Neuromorphic Devices, Systems and Algorithms |
| 773 | _ | _ | |a 10.1088/2634-4386/ac55fc |g Vol. 2, no. 2, p. 021001 - |0 PERI:(DE-600)3099608-9 |n 2 |p 021001 |t Neuromorphic computing and engineering |v 2 |y 2022 |x 2634-4386 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/908053/files/Kurth_2022_Neuromorph._Comput._Eng._2_021001.pdf |y OpenAccess |
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| 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 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2021-10-13T13:51:14Z |
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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 |
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