P228 Multi-scale Spiking Network Model of Human Cerebral Cortex
Renan O. Shimoura*1, Jari Pronold1,2, Alexander van Meegen1,3, Mario Senden4,5, Claus C. Hilgetag6, Rembrandt Bakker1,7, Sacha J. van Albada1,3
1Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany
2RWTH Aachen University, Aachen, Germany
3Institute of Zoology, University of Cologne, Cologne, Germany
4Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
5Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Centre, Maastricht University, Maastricht, The Netherlands
6Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
7Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
*Email: r.shimoura@fz-juelich.de
Introduction
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.
Methods
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.
Results
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].
Discussion
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 athttps://github.com/INM-6/human-multi-area-model.
Acknowledgements
This work was supported by the German Research Foundation (DFG) Priority Program "Computational Connectomics" (SPP 2041; Project 347572269), the EU Grant 945539 (HBP), EBRAINS 2.0 Project (101147319), the Joint lab SMHB, and HiRSE_PS. The use of the JURECA-DC supercomputer in Jülich was made possible through VSR computation grant JINB33. Open access publication funded by DGF Grant 491111487.
References
[1] https://doi.org/10.1093/CERCOR/BHAE409.
[2] https://doi.org/10.1007/s00429-017-1554-4.
[3] https://doi.org/10.1371/journal.pcbi.1006359.
[4] https://doi.org/10.1159/isbn.978-3-8055-9062-4.
[5]https://doi.org/10.1016/J.NEUROIMAGE.2013.05.041.
[6] https://doi.org/10.1093/cercor/bhs358.
[7] https://doi.org/10.1093/CERCOR/BHV188.
[8] https://doi.org/10.1126/science.aba3313.
[9] https://doi.org/10.1016/J.NEUROIMAGE.2014.05.081.
[10] https://doi.org/10.3389/fnana.2017.00078.
