Home > Publications database > Multi-Scale Spiking Network Model of Human Cerebral Cortex > print

001     1017849
005     20240313094903.0
037 _ _ |a FZJ-2023-04363
041 _ _ |a English
100 1 _ |a Pronold, Jari
|0 P:(DE-Juel1)165321
|b 0
111 2 _ |a 2nd Cologne Neuroscience Day
|c Cologne
|d 2023-10-26 - 2023-10-26
|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
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 1704184583_26652
|2 PUB:(DE-HGF)
|x Other
500 _ _ |a References: [1] Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ. Brain Struct Funct. 2018;223(3):1409–35.[2] Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, et al. PLOS Comput Biol. 2018;14(10):e1006359.[3] Potjans TC, Diesmann M. Cerebral Cortex. 2014;24(3):785–806.[4] Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, et al. Neuroimage. 2013;80:62–79[5] Mohan H, Verhoog MB, Doreswamy KK, Eyal G, Aardse R, et al. Cerebral Cortex. 2015;25(12):4839–53.[6] Minxha J, Adolphs R, Fusi S, Mamelak AN, Rutishauser U. Science. 2020;368(6498).
520 _ _ |a Background: The structure of the brain plays a crucial role in shaping its activity. However, the link between structural connectivity and observed neuronal activity remains incompletely 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]. Here, a similar framework is employed to investigate human cortex in a model that links the cortical network structure to the resting-state activity of neurons, populations, layers, and areas.Objectives: The objective of this study is to investigate the link between structural connectivity and observed neuronal activity in human cortex using a large-scale spiking network model, and to create a platform for multi-scale in silico studies of human cortex.Materials and Methods: The model includes all 34 areas in a single hemisphere of human cortex according to the Desikan-Killiany parcellation. Our approach integrates cortical data on architecture, morphology, and connectivity into a multi-scale framework for predicting neuron connections. Each cortical area is represented by a 1 $mm^2$ layered microcircuit adapted from [3] with the full density of neurons and synapses. Inter-area connectivity relies on diffusion tensor imaging data [4] and the determination of laminar patterns of synaptic connectivity takes into account human neuron morphology data [5]. The model comprises 4 million neurons and 50 billion synapses, simulated with the NEST simulator on the supercomputer JURECA-DC. Results and Conclusions: Simulations of the model with uniform synaptic weights reveal a state with asynchronous and irregular activity that deviates from experimental recordings in terms of spiking activity and inter-area functional connectivity. Increasing inter-area synapse strength enables the model to capture both microscopic and macroscopic resting-state activity of human cortex measured via electrophysiological recordings and fMRI [6]. Furthermore, the model reveals rapid propagation of the effects of a single-spike perturbation across the entire network. This suggests individual spikes play a role in fast sensory processing and behavioral responses in the cortical network. Overall, the model serves as a basis for the investigation of multi-scale structure-dynamics relationships in human cortex.
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 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
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 3
536 _ _ |a Brain-Scale Simulations (jinb33_20220812)
|0 G:(DE-Juel1)jinb33_20220812
|c jinb33_20220812
|f Brain-Scale Simulations
|x 4
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 Hilgetag, Claus C.
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Bakker, Rembrandt
|0 P:(DE-Juel1)145578
|b 6
|u fzj
700 1 _ |a van Albada, Sacha
|0 P:(DE-Juel1)138512
|b 7
|u fzj
909 C O |o oai:juser.fz-juelich.de:1017849
|p openaire
|p VDB
|p ec_fundedresources
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)190767
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 6
|6 P:(DE-Juel1)145578
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 7
|6 P:(DE-Juel1)138512
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 2023
920 1 _ |0 I:(DE-Juel1)INM-6-20090406
|k INM-6
|l Computational and Systems Neuroscience
|x 0
920 1 _ |0 I:(DE-Juel1)IAS-6-20130828
|k IAS-6
|l Theoretical Neuroscience
|x 1
920 1 _ |0 I:(DE-Juel1)INM-10-20170113
|k INM-10
|l Jara-Institut Brain structure-function relationships
|x 2
980 _ _ |a poster
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-6-20090406
980 _ _ |a I:(DE-Juel1)IAS-6-20130828
980 _ _ |a I:(DE-Juel1)INM-10-20170113
980 _ _ |a UNRESTRICTED
981 _ _ |a I:(DE-Juel1)IAS-6-20130828

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21