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| 001 | 1044827 | ||
| 005 | 20250806202244.0 | ||
| 037 | _ | _ | |a FZJ-2025-03382 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Shimoura, Renan |0 P:(DE-Juel1)190767 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 34th Annual Computational Neuroscience Meeting |g CNS*2025 |c Florence |d 2025-07-05 - 2025-07-09 |w Italy |
| 245 | _ | _ | |a Multi-scale Spiking Network Model of Human Cerebral Cortex |
| 260 | _ | _ | |c 2025 |
| 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 |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1754456223_30104 |2 PUB:(DE-HGF) |x Other |
| 520 | _ | _ | |a Data-driven models at cellular resolution have been built for various brain regions, yet few exist for the human cortex. We present a comprehensive point-neuron network model of a human cortical hemisphere integrating diverse experimental data into a unified framework bridging cellular and network scales [1]. Our approach builds on a large-scale spiking network model of macaque cortex [2,3] and investigates how resting-state activity emerges in cortical networks.We constructed a spiking network model representing one hemisphere using the Desikan-Killiany parcellation (34 areas), with each area implemented as a 1 mm² microcircuit distinguishing the cortical layers. The model aggregates data across multiple modalities, including electron microscopy for synapse density, cytoarchitecture from the von Economo atlas [4], DTI-based connectivity [5], and local connection probabilities from the Potjans-Diesmann microcircuit [6]. Human neuron morphologies [7] inform the layer-specific inter-area connectivity. The full-density model, based on leaky integrate-and-fire neurons, comprises 3.47 million neurons with 42.8 billion synapses and was simulated using the NEST simulator on the JURECA-DC supercomputer.When local and inter-area synapses have the same strength, model simulations show asynchronous irregular activity deviating from experiments in terms of spiking activity and inter-area functional connectivity. When inter-areal connections are strengthened relative to local synapses, the model reproduces both microscopic spiking statistics from human medial frontal cortex and macroscopic resting-state fMRI correlations [8]. Analysis reveals that single-spike perturbations influence network-wide activity within 50-75 ms. The ongoing activity flows primarily from parietal through occipital and temporal to frontal areas, consistent with empirical findings during visual imagery [9].This open-source model integrates human data across scales to investigate cortical organization and dynamics. By preserving neuron and synapse densities, it accounts for the majority of the inputs to the modeled neurons, enhancing the self-consistency compared to downscaled models. The model allows systematic study of structure-dynamics relationships and forms a platform for investigating theories of cortical function. Future work may leverage the Julich-Brain Atlas to refine the parcellation and incorporate detailed cytoarchitectural and receptor distribution data [10]. The model code is publicly available at https://github.com/INM-6/human-multi-area-model. |
| 536 | _ | _ | |a 5231 - Neuroscientific Foundations (POF4-523) |0 G:(DE-HGF)POF4-5231 |c POF4-523 |f POF IV |x 0 |
| 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 |
| 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 2 |
| 536 | _ | _ | |a EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319) |0 G:(EU-Grant)101147319 |c 101147319 |f HORIZON-INFRA-2022-SERV-B-01 |x 3 |
| 536 | _ | _ | |a JL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027) |0 G:(DE-Juel1)JL SMHB-2021-2027 |c JL SMHB-2021-2027 |x 4 |
| 536 | _ | _ | |a Brain-Scale Simulations (jinb33_20220812) |0 G:(DE-Juel1)jinb33_20220812 |c jinb33_20220812 |f Brain-Scale Simulations |x 5 |
| 536 | _ | _ | |a HiRSE_PS - Helmholtz Platform for Research Software Engineering - Preparatory Study (HiRSE_PS-20220812) |0 G:(DE-Juel-1)HiRSE_PS-20220812 |c HiRSE_PS-20220812 |x 6 |
| 700 | 1 | _ | |a Pronold, Jari |0 P:(DE-Juel1)165321 |b 1 |
| 700 | 1 | _ | |a Meegen, Alexander van |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Senden, Mario |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Hilgetag, Claus |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Bakker, Rembrandt |0 P:(DE-Juel1)145578 |b 5 |
| 700 | 1 | _ | |a van Albada, Sacha |0 P:(DE-Juel1)138512 |b 6 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1044827 |p openaire |p VDB |p ec_fundedresources |
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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)POF4-5231 |x 0 |
| 914 | 1 | _ | |y 2025 |
| 920 | _ | _ | |l no |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Computational and Systems Neuroscience |x 0 |
| 980 | _ | _ | |a poster |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a UNRESTRICTED |
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