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@INPROCEEDINGS{Pronold:1009486,
      author       = {Pronold, Jari and Meegen, Alexander van and Vollenbröker,
                      Hannah and Shimoura, Renan and Senden, Mario and Goulas,
                      Alexandros and Hilgetag, Claus C. and Bakker, Rembrandt},
      title        = {{M}ulti-{S}cale {S}piking {N}etwork {M}odel of {H}uman
                      {C}erebral {C}ortex},
      reportid     = {FZJ-2023-02823},
      year         = {2023},
      note         = {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.},
      abstract     = {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.},
      month         = {Jul},
      date          = {2023-07-15},
      organization  = {32nd Annual Computational Neuroscience
                       Meeting CNS*2023, Leipzig (Germany), 15
                       Jul 2023 - 19 Jul 2023},
      subtyp        = {Other},
      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) / DFG project
                      313856816 - SPP 2041: Computational Connectomics (313856816)
                      / DFG project 347572269 - Heterogenität von
                      Zytoarchitektur, Chemoarchitektur und Konnektivität in
                      einem großskaligen Computermodell der menschlichen
                      Großhirnrinde (347572269) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539) / Brain-Scale
                      Simulations $(jinb33_20220812)$},
      pid          = {G:(DE-HGF)POF4-5231 / G:(GEPRIS)313856816 /
                      G:(GEPRIS)347572269 / G:(EU-Grant)945539 /
                      $G:(DE-Juel1)jinb33_20220812$},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2023-02823},
      url          = {https://juser.fz-juelich.de/record/1009486},
}