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@PHDTHESIS{Diaz:901960,
      author       = {Diaz, Sandra},
      title        = {{S}tructural plasticity as a connectivity generation and
                      optimization algorithm in neural networks},
      volume       = {47},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2021-03936},
      isbn         = {978-3-95806-577-2},
      series       = {Schriften des Forschungszentrums Jülich IAS Series},
      pages        = {167},
      year         = {2021},
      note         = {Dissertation, RWTH Aachen University, 2021},
      abstract     = {Our brains are formed by networks of neurons and other
                      cells which receive, filter, store and process information
                      and produce actions. The morphology of the neurons changes
                      through time as well as the connections between them. For
                      years the brain has been studied as a snapshot in time, but
                      today we know that the way it structurally changes is
                      strongly involved in learning, healing, and adaptation. The
                      ensemble of structural changes that neural networks present
                      through time is called structural plasticity. In this work,
                      I present structural plasticity from its neurobiological
                      foundations and the implementation of a model to describe
                      generation and optimization of connectivity in spiking
                      neural networks. I have targeted two relevant and open
                      questions in the computational neuroscience community: how
                      can we model biologically inspired structural changes in
                      simulations of spiking neural networks and how can we use
                      this model and its implementation to optimize brain
                      connectivity to answer specific scientific questions related
                      to healing, development, and learning. I present several
                      studies which explain the implementation of structural
                      plasticity in a well established neural network simulator
                      and its application on different types of neural networks.
                      In this thesis I have also defined the requirements and use
                      cases for the co-development of tools to visualize and
                      interact with the structural plasticity algorithm. Moreover,
                      I present two scientific applications of the structural
                      plasticity model in the clinical neuroscience and computer
                      science fields. In conclusion, my thesis provides the basis
                      of a software framework and a methodology to address complex
                      neuroscience questions related to plasticity and the links
                      between structure and function in the brain, with potential
                      applications not only in neuroscience but also for machine
                      learning and optimization.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2021110925},
      url          = {https://juser.fz-juelich.de/record/901960},
}