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| Hauptseite > Publikationsdatenbank > Multi-scale spiking network model of human cortex > print |
| 001 | 894265 | ||
| 005 | 20240313094852.0 | ||
| 037 | _ | _ | |a FZJ-2021-03140 |
| 100 | 1 | _ | |a Pronold, Jari |0 P:(DE-Juel1)165321 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 30th Annual Computational Neuroscience Meeting. CNS*2021 |c Online |d 2021-07-03 - 2021-07-07 |w USA |
| 245 | _ | _ | |a Multi-scale spiking network model of human cortex |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a AbstractIs our current knowledge about the structural connectivity of the brain compatible with the measured activity? Using a large-scale spiking network model of leaky integrate-and-fire neurons to achieve simulations with the full neuron and synapse density, we previously answered this question in the affirmative for macaque cortex [1,2]. Here, we apply the same framework to investigate human cortex. Concretely, we present a large-scale spiking network model that relates the cortical network structure to the resting-state activity of neurons, populations, layers, and areas.The construction of the model is based on the integration of data on cortical architecture, single-cell properties, and local and cortico-cortical connectivity into a consistent multi-scale framework. It predicts connection probabilities between any two neurons based on their types and locations within areas and layers. Every area is represented by a 1 mm² microcircuit with area-specific architecture and the full density of neurons and synapses. The cortical architecture in terms of laminar thicknesses and neuron densities is taken from the von Economo and Koskinas atlas [3] and enriched with more detailed data extracted from the BigBrain atlas [4]. While connectivity on the area level is informed by DTI data [5], it is necessary to complement this with predictions on laminar connectivity patterns. We rely on predictive connectomics based on macaque data which express regularities of laminar connectivity patterns as a function of cortical architecture. The local connectivity uses the model by Potjans and Diesmann [6] as a blueprint and is scaled according to the cytoarchitectonic data. Analysis of human neuron morphologies provides synapse-to-soma mappings based on layer- and cell-type-specific dendritic lengths [7]. The model contains roughly 4 million neurons and 50 billion synapses and is simulated on a supercomputer using the NEST simulator.While the available data constrain the parameter space to some extent, the model remains underdetermined. Mean-field theory guides the exploration of the parameter space in search for a low-rate asynchronous irregular state that generates substantial inter-area interactions through cortico-cortical weights that poise the network at the edge of stability. Different realizations of the model are assessed via comparison with experimental data. The simulated functional connectivity is compared with experimental resting-state fMRI data. Furthermore, simulated spiking data is compared with spike recordings from medial frontal cortex recorded in epileptic patients [8]. Preliminary results show that the model can reproduce an asynchronous irregular network state and functional connectivity similar to the resting-state fMRI data. The model serves as a basis for the investigation of multi-scale structure-dynamics relationships in human cortex.AcknowledgmentsFunding: DFG SPP 2041, HBP SGA3 (grant 945539). Compute time: grant JINB33.[1] Schmidt M et al. (2018) Brain Struct Func 223(3), 1409.[2] Schmidt M et al. (2018) PLOS Comp Biol 14(10), e1006359.[3] Von Economo C (2009) Cellular Structure of the Human Cerebral Cortex.[4] Wagstyl K et al. (2020) PLOS Biol 18(4), e3000678.[5] Van Essen DC et al. (2013) NeuroImage 80, 62.[6] Potjans TC, Diesmann M (2014) Cereb Cortex 24(3), 785.[7] Mohan H et al. (2015) Cereb Cortex 25(12), 4839.[8] Minxha J et al. (2020) Science 368(6498). |
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| 536 | _ | _ | |a SPP 2041 347572269 - Integration von Multiskalen-Konnektivität und Gehirnarchitektur in einem supercomputergestützten Modell 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 |x 2 |
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| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 4 |
| 700 | 1 | _ | |a van Meegen, Alexander |0 P:(DE-Juel1)173607 |b 1 |u fzj |
| 700 | 1 | _ | |a Vollenbröker, Hannah |0 P:(DE-Juel1)180364 |b 2 |u fzj |
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| 700 | 1 | _ | |a van Albada, Sacha |0 P:(DE-Juel1)138512 |b 4 |u fzj |
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