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@INPROCEEDINGS{Pronold:894265,
      author       = {Pronold, Jari and van Meegen, Alexander and Vollenbröker,
                      Hannah and Bakker, Rembrandt and van Albada, Sacha},
      title        = {{M}ulti-scale spiking network model of human cortex},
      reportid     = {FZJ-2021-03140},
      year         = {2021},
      abstract     = {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).},
      month         = {Jul},
      date          = {2021-07-03},
      organization  = {30th Annual Computational Neuroscience
                       Meeting. CNS*2021, Online (USA), 3 Jul
                       2021 - 7 Jul 2021},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / SPP 2041
                      347572269 - Integration von Multiskalen-Konnektivität und
                      Gehirnarchitektur in einem supercomputergestützten Modell
                      der menschlichen Großhirnrinde (347572269) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      Brain-Scale Simulations $(jinb33_20191101)$ / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(GEPRIS)347572269 /
                      G:(EU-Grant)945539 / $G:(DE-Juel1)jinb33_20191101$ /
                      G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/894265},
}