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001042899 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-02699
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001042899 1001_ $$0P:(DE-Juel1)165321$$aPronold, Jari$$b0
001042899 1112_ $$aIAS Retreat 2025$$cJülich$$d2025-05-27 - 2025-05-27$$wGermany
001042899 245__ $$aMulti-scale Spiking Network Model of Human Cerebral Cortex
001042899 260__ $$c2025
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001042899 520__ $$aData-driven models at cellular resolution exist for various brain regions, yet few for human cortex. We present a comprehensive point-neuron network model of a human cortical hemisphere that integrates diverse experimental data into a unified framework bridging cellular and network scales [1]. Like a previous large-scale spiking model of macaque cortex [2,3], our work investigates how resting-state activity emerges in cortical networks.The model represents one hemisphere via the Desikan-Killiany parcellation (34 areas), with each area implemented as a 1 mm² microcircuit that distinguishes cortical layers. It aggregates multimodal data, 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] guide layer-specific inter-area connectivity. The full-density model, comprising 3.47 million leaky integrate-and-fire neurons and 42.8 billion synapses, was simulated using NEST on the JURECA-DC supercomputer.Simulations show that equal strength for local and inter-area synapses yields asynchronous irregular activity that deviates from experimental observations. When inter-area connections are strengthened relative to local synapses, both microscopic spiking statistics from human medial frontal cortex and macroscopic resting-state fMRI correlations are reproduced [8]. In the latter scenario, consistent with empirical findings during visual imagery [9], sustained activity flows primarily from parietal through occipital and temporal to frontal areas.This open-source model enables systematic exploration of structure-dynamics relationships. Future work may leverage the Julich-Brain Atlas to refine the parcellation and incorporate detailed cytoarchitectural and receptor data [10]. The model code is available at https://github.com/INM-6/human-multi-area-model.
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001042899 7001_ $$0P:(DE-Juel1)173607$$avan Meegen, Alexander$$b1
001042899 7001_ $$0P:(DE-Juel1)190767$$aShimoura, Renan$$b2$$eCorresponding author$$ufzj
001042899 7001_ $$0P:(DE-Juel1)180364$$aVollenbröker, Hannah$$b3
001042899 7001_ $$0P:(DE-HGF)0$$aSenden, Mario$$b4
001042899 7001_ $$0P:(DE-HGF)0$$aHilgetag, C. C.$$b5
001042899 7001_ $$0P:(DE-Juel1)145578$$aBakker, Rembrandt$$b6$$ufzj
001042899 7001_ $$0P:(DE-Juel1)138512$$avan Albada, Sacha$$b7$$ufzj
001042899 8564_ $$uhttps://juser.fz-juelich.de/record/1042899/files/poster-HuMAM.pdf$$yOpenAccess
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