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| 001 | 1009486 | ||
| 005 | 20240313103133.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2023-02823 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2023-02823 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Pronold, Jari |0 P:(DE-Juel1)165321 |b 0 |u fzj |
| 111 | 2 | _ | |a 32nd Annual Computational Neuroscience Meeting CNS*2023 |g CNS*2023 |c Leipzig |d 2023-07-15 - 2023-07-19 |w Germany |
| 245 | _ | _ | |a Multi-Scale Spiking Network Model of Human Cerebral Cortex |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 500 | _ | _ | |a References: [1] Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ. Multi-scale account of the network structure of macaque visual cortex. Brain Struct Funct. 2018;223(3):1409–35.[2] Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, et al. A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLOS Comput Biol. 2018;14(10):e1006359.[3] Von Economo C, Koskinas GN. Cellular structure of the human cerebral cortex. Karger Medical and Scientific Publishers; 2009.[4] Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, Wu-Minn HCP Consortium. The WU-Minn human connectome project: an overview. Neuroimage. 2013;80:62–79.[5] Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cerebral Cortex. 2014;24(3):785–806.[6] Mohan H, Verhoog MB, Doreswamy KK, Eyal G, Aardse R, et al. Dendritic and axonal architecture of individual pyramidal neurons across layers of adult human neocortex. Cerebral Cortex. 2015;25(12):4839–53.[7] Minxha J, Adolphs R, Fusi S, Mamelak AN, Rutishauser U. Flexible recruitment of memory-based choice representations by the human medial frontal cortex. Science. 2020;368(6498).[8] Lamme VAF, Roelfsema PR. The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 2000; 23:571–579. |
| 520 | _ | _ | |a The structure of the brain plays a crucial role in shaping its activity. However, the link between structural connectivity and observed neuronal activity remains not fully understood. Previous research utilizing a large-scale spiking network model of leaky integrate-and-fire neurons has addressed this question for macaque cortex [1,2]. In this study, we employ the same framework to investigate human cortex and present a large-scale spiking network model that links the cortical network structure to the resting-state activity of neurons, populations, layers, and areas.Our approach integrates data on cortical architecture, cellular morphologies, and local and cortico-cortical connectivity into a multi-scale framework to predict connection probabilities between neurons based on their types and locations within areas and layers. We represent each cortical area with a 1 mm2 area-specific microcircuit incorporating the full density of neurons and synapses. For this first model version, the laminar thicknesses and neuron densities are derived from the von Economo and Koskinas atlas [3]. The connectivity on the area level is informed by diffusion tensor imaging (DTI) data [4], while predictions on laminar connectivity patterns are derived from predictive connectomics based on macaque data that express regularities of laminar connectivity patterns as a function of cortical architecture. We use the Potjans and Diesmann [5] model as a basis for the local connectivity, scaling it according to cytoarchitectonic data. To map inter-area synapses to target cells, which may have their cell body in a different layer compared to the synapse location, we assign synapses in proportion to the layer- and cell-type-specific dendritic lengths determined from human neuron morphologies [6]. The model contains approximately 4 million neurons and 50 billion synapses and is simulated on JURECA-DC using the NEST simulator.Simulations of the model reveal a state with asynchronous and irregular activity that deviates from experimental recordings in terms of spiking activity and inter-area functional connectivity. By increasing the strength of the inter-area synapses, a state is reached that captures aspects of both microscopic and macroscopic resting-state activity of human cortex measured via electrophysiological recordings from medial frontal cortex and fMRI [7]. Furthermore, we used our model to track the effect of a single additional spike through the large-scale network. We find that a single-spike perturbation spreads rapidly across the entire network within 50-75 ms, comparable to visual response latencies in macaque cortex [8], suggesting that the cortical network allows individual spikes to play a role in fast sensory processing and behavioral responses. Overall, the model serves as a basis for the investigation of multi-scale structure-dynamics relationships in human cortex. |
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| 536 | _ | _ | |a DFG project 313856816 - SPP 2041: Computational Connectomics (313856816) |0 G:(GEPRIS)313856816 |c 313856816 |x 1 |
| 536 | _ | _ | |a DFG project 347572269 - Heterogenität von Zytoarchitektur, Chemoarchitektur und Konnektivität in einem großskaligen Computermodell der menschlichen Großhirnrinde (347572269) |0 G:(GEPRIS)347572269 |c 347572269 |x 2 |
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| 700 | 1 | _ | |a Meegen, Alexander van |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Vollenbröker, Hannah |0 P:(DE-Juel1)180364 |b 2 |
| 700 | 1 | _ | |a Shimoura, Renan |0 P:(DE-Juel1)190767 |b 3 |e Corresponding author |u fzj |
| 700 | 1 | _ | |a Senden, Mario |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Goulas, Alexandros |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Hilgetag, Claus C. |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Bakker, Rembrandt |0 P:(DE-Juel1)145578 |b 7 |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1009486/files/huvi_RenanShimoura_CNS2023.pdf |y OpenAccess |
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