000917354 001__ 917354
000917354 005__ 20240313095023.0
000917354 037__ $$aFZJ-2023-00582
000917354 1001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b0$$eCorresponding author$$ufzj
000917354 1112_ $$aINCF Neuroinformatics Assembly 2022$$cvirtual$$d2022-09-13 - 2022-09-13$$wvirtual
000917354 245__ $$aOn concepts, correctness, and performance of network simulations
000917354 260__ $$c2022
000917354 3367_ $$033$$2EndNote$$aConference Paper
000917354 3367_ $$2DataCite$$aOther
000917354 3367_ $$2BibTeX$$aINPROCEEDINGS
000917354 3367_ $$2DRIVER$$aconferenceObject
000917354 3367_ $$2ORCID$$aLECTURE_SPEECH
000917354 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1683103334_1181$$xInvited
000917354 500__ $$aSession: Tools and formats for large scale network modelling
000917354 520__ $$aThe development of large-scale neuronal network models is an iterative process and best described by a loop linking the conceptual description with its algorithmic implementation. Models are informed by experimental data and insights from theory; their simulated dynamics needs to be validated against experimentally observed activity. The inherent interdisciplinarity and intricacy of this endeavor make it prone to pitfalls that complicate the comprehensibility and reproducibility of each step. Addressing shortcomings in model descriptions, we review how connectivity is specified in published models and propose unified connectivity concepts and guidelines including a graphical notation for network diagrams. Furthermore, we focus on the challenge to compare simulations of a cortical microcircuit model with respect to correctness and performance across different simulation technologies; literature shows a race for the fastest and most energy efficient simulation. We also showcase a conceptual workflow for performance benchmarking of the simulator NEST together with the benchmarking framework beNNch as a reference implementation. Finally, we introduce NNMT, a toolbox for mean-field based analysis methods of neuronal network models. The presented approaches aim at raising awareness to common difficulties and fostering a sustainable and collaborative modeling culture.
000917354 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000917354 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000917354 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x2
000917354 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x3
000917354 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x4
000917354 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x5
000917354 536__ $$0G:(EU-Grant)754304$$aDEEP-EST - DEEP - Extreme Scale Technologies (754304)$$c754304$$fH2020-FETHPC-2016$$x6
000917354 536__ $$0G:(GEPRIS)250805958$$aGRK 2041 - GRK 2041: Modell Romantik. Variation - Reichweite - Aktualität (250805958)$$c250805958$$x7
000917354 536__ $$0G:(GEPRIS)347572269$$aDFG project 347572269 - Heterogenität von Zytoarchitektur, Chemoarchitektur und Konnektivität in einem großskaligen Computermodell der menschlichen Großhirnrinde (347572269)$$c347572269$$x8
000917354 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x9
000917354 536__ $$0G:(DE-82)EXS-SF-neuroIC001$$aneuroIC001 - NeuroModelingTalk (NMT) - Approaching the complexity barrier in neuroscientific modeling (EXS-SF-neuroIC001)$$cEXS-SF-neuroIC001$$x10
000917354 536__ $$0G:(DE-Juel-1)ZT-I-PF-3-026$$aMetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026)$$cZT-I-PF-3-026$$x11
000917354 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x12
000917354 536__ $$0G:(DE-Juel1)HGF-SMHB-2014-2018$$aMSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)$$cHGF-SMHB-2014-2018$$fMSNN$$x13
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000917354 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162130$$aForschungszentrum Jülich$$b0$$kFZJ
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000917354 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
000917354 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x2
000917354 9141_ $$y2023
000917354 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000917354 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000917354 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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000917354 980__ $$aI:(DE-Juel1)INM-10-20170113
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000917354 981__ $$aI:(DE-Juel1)IAS-6-20130828