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@MASTERSTHESIS{Kempter:943468,
      author       = {Kempter, Miriam},
      title        = {{I}mplementation of a {S}piking {N}eural {N}etwork {M}odel
                      with {B}iologically {P}lausible {C}onnectivity
                      {C}haracteristics for {N}euromorphic {B}enchmarking},
      school       = {RWTH Aachen},
      type         = {Bachelorarbeit},
      reportid     = {FZJ-2023-01037},
      pages        = {65 p.},
      year         = {2022},
      note         = {Bachelorarbeit, RWTH Aachen, 2022},
      abstract     = {The cortex exhibits a complex connectivity structure,
                      containing spatially clustered connectivity patterns. Those,
                      so-called patchy connections, are often neglected in studies
                      concerning neural networks. However, they perform an
                      important role in the efficiency of the cortex. The goal of
                      neuromorphic hardware systems is, to imitate the highly
                      efficient cortex structure, and perform faster-than-realtime
                      calculations. In order to benchmark the performance of such
                      systems, it is important to have realistic neural network
                      models available. Here, such a benchmark model will be
                      presented. Focusing on connectivity, the restrains on all
                      other network aspects are kept to a minimum. It respects the
                      biological distant dependent connectivity patterns,
                      including spatial structure and patchy connectivity. Three
                      different variations of the model, with slightly different
                      spatially clustering approaches, are constructed.
                      Additionally the model will provide benchmarking features. A
                      fourth model, without spatial clustering is included to
                      serve as a comparison.},
      pnm          = {ACA - Advanced Computing Architectures (SO-092) / SLNS -
                      SimLab Neuroscience (Helmholtz-SLNS) / 899 - ohne Topic
                      (POF4-899)},
      pid          = {G:(DE-HGF)SO-092 / G:(DE-Juel1)Helmholtz-SLNS /
                      G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)2},
      url          = {https://juser.fz-juelich.de/record/943468},
}