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| Hauptseite > Publikationsdatenbank > Multi-Area Cortical Models: Bridging Structure and Dynamics in Large-Scale Spiking Networks > print |
| Hauptseite > Publikationsdatenbank > Multi-Area Cortical Models: Bridging Structure and Dynamics in Large-Scale Spiking Networks > print |
| 001 | 1047386 | ||
| 005 | 20251111202158.0 | ||
| 037 | _ | _ | |a FZJ-2025-04272 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Shimoura, Renan |0 P:(DE-Juel1)190767 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a NEST GPU Workshop |c Cagliari |d 2025-10-08 - 2025-10-15 |w Italy |
| 245 | _ | _ | |a Multi-Area Cortical Models: Bridging Structure and Dynamics in Large-Scale Spiking Networks |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
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| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1762843374_847 |2 PUB:(DE-HGF) |x Invited |
| 500 | _ | _ | |a References: [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. |
| 520 | _ | _ | |a In 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. |
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| 536 | _ | _ | |a DFG project G:(GEPRIS)347572269 - Heterogenität von Zytoarchitektur, Chemoarchitektur und Konnektivität in einem großskaligen Computermodell der menschlichen Großhirnrinde (347572269) |0 G:(GEPRIS)347572269 |c 347572269 |x 1 |
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