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@PHDTHESIS{vanMeegen:1010679,
      author       = {van Meegen, Alexander},
      title        = {{S}imulation and theory of large-scale cortical networks},
      volume       = {98},
      school       = {Univ. Köln},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2023-03187},
      isbn         = {978-3-95806-708-0},
      series       = {Schriften des Forschungszentrums Jülich Reihe Information
                      / Information},
      pages        = {250 p.},
      year         = {2023},
      note         = {Dissertation, Univ. Köln, 2022},
      abstract     = {Cerebral cortex is composed of intricate networks of
                      neurons. These neuronal networks are strongly
                      interconnected: every neuron receives, on average, input
                      from thousands or more presynaptic neurons. In fact, to
                      support such a number of connections, a majority of the
                      volume inthe cortical gray matter is filled by axons and
                      dendrites. Besides the networks, neurons themselves are also
                      highly complex. They possess an elaborate spatial structure
                      and support various types of active processes and
                      nonlinearities. In the face of such complexity, it seems
                      necessary to abstract away some of the details and to
                      investigate simplified models.In this thesis, such
                      simplified models of neuronal networks are examined on
                      varying levels of abstraction. Neurons are modeled as point
                      neurons, both rate-based and spike-based, and networks are
                      modeled as block-structured random networks. Crucially, on
                      this level of abstraction, the models are still amenable to
                      analytical treatment using the framework of dynamical
                      mean-field theory.The main focus of this thesis is to
                      leverage the analytical tractability of random networks of
                      point neurons in order to relate the network structure, and
                      the neuron parameters, to the dynamics of the neurons—in
                      physics parlance, to bridge across the scales from neurons
                      to networks.More concretely, four different models are
                      investigated: 1) fully connected feedforward networks and
                      vanilla recurrent networks of rate neurons; 2)
                      block-structured networks of rate neurons in continuous
                      time; 3) block-structured networks of spiking neurons; and
                      4) a multi-scale, data-based network of spiking neurons. We
                      consider the first class of models in the light of Bayesian
                      supervised learning and compute their kernel in the
                      infinite-size limit. In the second class of models, we
                      connect dynamical mean-field theory with large-deviation
                      theory, calculate beyond mean-field fluctuations, and
                      perform parameter inference. For the third class of models,
                      we develop a theory for the autocorrelation time of the
                      neurons. Lastly, we consolidate data across multiple
                      modalities into a layer- and population-resolved model of
                      human cortex and compare its activity with cortical
                      recordings.In two detours from the investigation of these
                      four network models, we examine the distribution of neuron
                      densities in cerebral cortex and present a software toolbox
                      for mean-field analyses of spiking networks.},
      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) / 5232 -
                      Computational Principles (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) /
                      Brain-Scale Simulations $(jinb33_20090701)$ / Brain-Scale
                      Simulations $(jinb33_20191101)$ / Brain-Scale Simulations
                      $(jinb33_20220812)$ / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 /
                      G:(GEPRIS)313856816 / G:(GEPRIS)347572269 /
                      $G:(DE-Juel1)jinb33_20090701$ /
                      $G:(DE-Juel1)jinb33_20191101$ /
                      $G:(DE-Juel1)jinb33_20220812$ / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-20231004081300012-6971752-0},
      doi          = {10.34734/FZJ-2023-03187},
      url          = {https://juser.fz-juelich.de/record/1010679},
}