001047386 001__ 1047386
001047386 005__ 20251111202158.0
001047386 037__ $$aFZJ-2025-04272
001047386 041__ $$aEnglish
001047386 1001_ $$0P:(DE-Juel1)190767$$aShimoura, Renan$$b0$$eCorresponding author$$ufzj
001047386 1112_ $$aNEST GPU Workshop$$cCagliari$$d2025-10-08 - 2025-10-15$$wItaly
001047386 245__ $$aMulti-Area Cortical Models: Bridging Structure and Dynamics in Large-Scale Spiking Networks
001047386 260__ $$c2025
001047386 3367_ $$033$$2EndNote$$aConference Paper
001047386 3367_ $$2DataCite$$aOther
001047386 3367_ $$2BibTeX$$aINPROCEEDINGS
001047386 3367_ $$2DRIVER$$aconferenceObject
001047386 3367_ $$2ORCID$$aLECTURE_SPEECH
001047386 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1762843374_847$$xInvited
001047386 500__ $$aReferences: [1] Schmidt, M., Bakker, R., Hilgetag, C. C., Diesmann, M., & van Albada, S. J. (2018). Multi-scale account of the network structure of macaque visual cortex. Brain Structure & Function, 223(3), 1409–1435. [2] Schmidt, M., Bakker, R., Shen, K., Bezgin, G., Diesmann, M., & van Albada, S. J. (2018). A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLoS Computational Biology, 14(10), e1006359. [3] Pronold, J., van Meegen, A., Shimoura, R. O., Vollenbröker, H., Senden, M., Hilgetag, C. C., Bakker, R., & van Albada, S. J. (2024). Multi-scale spiking network model of human cerebral cortex. Cerebral Cortex, 34(10), bhae409. [4] Pronold, J., Morales-Gregorio, A., Rostami, V., & van Albada, S. J. (2024). Cortical multi-area model with joint excitatory-inhibitory clusters accounts for spiking statistics, inter-area propagation, and variability dynamics. bioRxiv.
001047386 520__ $$aIn recent years, we have developed multiscale spiking networks that bridge local circuits and cortical areas, as illustrated by the macaque visual cortex [1, 2] and the human cerebral cortex models [3]. These models serve as platforms for linking anatomical structure to emergent network dynamics. For instance, in Pronold et al. [4], we extended the macaque multi-area model by implementing joint excitatory-inhibitory (EI) clustering. This clustered architecture adjusts resting-state spiking activity statistics, supports robust signal propagation across the hierarchy, and reproduces the experimentally observed reduction of neural variability upon stimulation. However, scaling into this structure poses a significant computational bottleneck, causing network construction time in the NEST simulator to increase substantially (e.g., from one minute to several hours for 50 clusters). To address this initialization challenge, strategies include code refactoring and developing specialized connection routines within the core NEST framework. The NEST-GPU platform presents a potential alternative for assessing new network initialization strategies. Additionally, it may offer an effective environment for accelerating the simulation dynamics of these massive networks at full scale.
001047386 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001047386 536__ $$0G:(GEPRIS)347572269$$aDFG project G:(GEPRIS)347572269 - Heterogenität von Zytoarchitektur, Chemoarchitektur und Konnektivität in einem großskaligen Computermodell der menschlichen Großhirnrinde (347572269)$$c347572269$$x1
001047386 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001047386 536__ $$0G:(DE-Juel1)jinb33_20220812$$aBrain-Scale Simulations (jinb33_20220812)$$cjinb33_20220812$$fBrain-Scale Simulations$$x3
001047386 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x4
001047386 536__ $$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$$x5
001047386 909CO $$ooai:juser.fz-juelich.de:1047386$$popenaire$$pVDB$$pec_fundedresources
001047386 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190767$$aForschungszentrum Jülich$$b0$$kFZJ
001047386 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)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001047386 9141_ $$y2025
001047386 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001047386 980__ $$aconf
001047386 980__ $$aVDB
001047386 980__ $$aI:(DE-Juel1)IAS-6-20130828
001047386 980__ $$aUNRESTRICTED