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| 001 | 912235 | ||
| 005 | 20221205131404.0 | ||
| 024 | 7 | _ | |a 2128/32938 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-05435 |
| 100 | 1 | _ | |a van der Vlag, Michiel |0 P:(DE-Juel1)179447 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a From Cortical Microcircuits to Consciousness |g Corticon |c Paris |d 2022-04-11 - 2022-04-13 |w France |
| 245 | _ | _ | |a RateML: A Code Generation Tool for Brain Network Model |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
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| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1670237135_7995 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Recent studies on consciousness[1-4] make use of The Virtual Brain[5] simulator to explore hypothesis based on theoretical claims and experimental data. TVB is a whole-brain simulator, making use of dynamical models under constriction of biologically realistic connectivity, allowing users to relate brain structure to activity. In order to simplify model creation for TVB, a tool called RateML can be utilized. This tools enables users to create a model by transforming a high-level XML file to either a Python model to be used in the regular simulator and a Compute Unified Device Architecture model and simulator object suited for extreme parameters space exploration making use of the highly parallel architecture of the GPU. |
| 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 SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
| 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 2 |
| 536 | _ | _ | |a SLNS - SimLab Neuroscience (Helmholtz-SLNS) |0 G:(DE-Juel1)Helmholtz-SLNS |c Helmholtz-SLNS |x 3 |
| 856 | 4 | _ | |u https://www.humanbrainproject.eu/en/education/participatecollaborate/infrastructure-events-training/corticon/ |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/912235/files/Poster.pdf |y OpenAccess |
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| 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 |
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