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@ARTICLE{Diaz:809783,
      author       = {Diaz, Sandra and Naveau, Mikaël and Butz-Ostendorf, Markus
                      and Morrison, Abigail},
      title        = {{A}utomatic {G}eneration of {C}onnectivity for
                      {L}arge-{S}cale {N}euronal {N}etwork {M}odels through
                      {S}tructural {P}lasticity},
      journal      = {Frontiers in neuroanatomy},
      volume       = {10},
      number       = {57},
      issn         = {1662-5129},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2016-02710},
      pages        = {1662-5129},
      year         = {2016},
      abstract     = {With the emergence of new high performance computation
                      technology in the last decade, the simulation of large scale
                      neural networks which are able to reproduce the behavior and
                      structure of the brain has finally become an achievable
                      target of neuroscience. Due to the number of synaptic
                      connections between neurons and the complexity of biological
                      networks, most contemporary models have manually defined or
                      static connectivity. However, it is expected that modeling
                      the dynamic generation and deletion of the links among
                      neurons, locally and between different regions of the brain,
                      is crucial to unravel important mechanisms associated with
                      learning, memory and healing. Moreover, for many neural
                      circuits that could potentially be modeled, activity data is
                      more readily and reliably available than connectivity data.
                      Thus, a framework that enables networks to wire themselves
                      on the basis of specified activity targets can be of great
                      value in specifying network models where connectivity data
                      is incomplete or has large error margins. To address these
                      issues, in the present work we present an implementation of
                      a model of structural plasticity in the neural network
                      simulator NEST. In this model, synapses consist of two
                      parts, a pre- and a post-synaptic element. Synapses are
                      created and deleted during the execution of the simulation
                      following local homeostatic rules until a mean level of
                      electrical activity is reached in the network. We assess the
                      scalability of the implementation in order to evaluate its
                      potential usage in the self generation of connectivity of
                      large scale networks. We show and discuss the results of
                      simulations on simple two population networks and more
                      complex models of the cortical microcircuit involving 8
                      populations and 4 layers using the new framework.},
      cin          = {IAS / JSC / INM-6 / JARA-HPC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)VDB1106 / I:(DE-Juel1)JSC-20090406 /
                      I:(DE-Juel1)INM-6-20090406 / $I:(DE-82)080012_20140620$},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 511 -
                      Computational Science and Mathematical Methods (POF3-511) /
                      SMHB - Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / W2Morrison - W2/W3 Professorinnen
                      Programm der Helmholtzgemeinschaft (B1175.01.12) / SLNS -
                      SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-511 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(DE-HGF)B1175.01.12 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000376778500001},
      pubmed       = {pmid:27303272},
      doi          = {10.3389/fnana.2016.00057},
      url          = {https://juser.fz-juelich.de/record/809783},
}