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@INPROCEEDINGS{Senk:863678,
      author       = {Senk, Johanna and Kriener, Birgit and Hagen, Espen and Bos,
                      Hannah and Plesser, Hans Ekkehard and Gewaltig, Marc-Oliver
                      and Diesmann, Markus and Djurfeldt, Mikael and Voges, Nicole
                      and van Albada, Sacha},
      title        = {{C}onnectivity {C}oncepts for {N}euronal {N}etworks},
      reportid     = {FZJ-2019-03684},
      year         = {2019},
      abstract     = {A statement like “$N_\text{s}$ source neurons and
                      $N_\text{t}$ target neurons are connected randomly with
                      connectionprobability $p$” may be used to describe the
                      structure of a neuronal network model, but itsinterpretation
                      is inherently ambiguous. A lacking detail is, for example,
                      information on thedistribution of in- and outgoing
                      connections, resulting in substantial differences in
                      networkdynamics. For reproducible research, unambiguous
                      network descriptions and correspondingalgorithmic
                      implementations are necessary [1]. Here, we review
                      simulation software (e.g., NEST[2]), specification languages
                      (e.g., CSA [3]), and published network models made available
                      by thecommunity in databases like ModelDB [4] and Open
                      Source Brain [5]. We investigate the networkstructures
                      computational neuroscientists use in their models and the
                      terminology they use todescribe these models. From this, we
                      derive a set of connectivity concepts providing modelers
                      withguidelines to specify connectivity in a complete and
                      concise way. Furthermore, this work aims toguide the
                      comprehensive and efficient implementation of connection
                      routines in simulationsoftware like NEST, thereby
                      facilitating reproducible research on network
                      models.$\qquad$References: $\qquad$1. Nordlie E, et al.
                      (2009) Towards Reproducible Descriptions of Neuronal Network
                      Models. PLoS Comput Biol. 5(8): e1000456.
                      doi:10.1371/journal.pcbi.1000456 $\qquad$2. Gewaltig M-O and
                      Diesmann M (2007). NEST (NEural Simulation Tool).
                      Scholarpedia. 2(4):1430, doi:10.4249/scholarpedia.1430
                      $\qquad$3. Djurfeldt M (2012) The Connection-set Algebra—A
                      Novel Formalism for the Representation of Connectivity
                      Structure in Neuronal Network Models. Neuroinform.
                      10:287–304. doi:10.1007/s12021-012-9146-1 $\qquad$4.
                      ModelDB [https://senselab.med.yale.edu/modeldb] $\qquad$5.
                      Gleeson P, Cantarelli M, Marin B, . . . van Albada SJ, van
                      Geit W, R Silver RA (in press) Open Source Brain: a
                      collaborative resource for visualizing, analyzing,
                      simulating and developing standardized models of neurons and
                      circuits. Neuron.},
      month         = {Jun},
      date          = {2019-06-24},
      organization  = {NEST Conference 2019, Aas (Norway), 24
                       Jun 2019 - 25 Jun 2019},
      subtyp        = {After Call},
      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          = {571 - Connectivity and Activity (POF3-571) / HBP SGA1 -
                      Human Brain Project Specific Grant Agreement 1 (720270) /
                      HBP SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / Brain-Scale Simulations $(jinb33_20121101)$ / SPP
                      2041 347572269 - Integration von Multiskalen-Konnektivität
                      und Gehirnarchitektur in einem supercomputergestützten
                      Modell der menschlichen Großhirnrinde (347572269) /
                      Advanced Computing Architectures $(aca_20190115)$},
      pid          = {G:(DE-HGF)POF3-571 / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907 / $G:(DE-Juel1)jinb33_20121101$ /
                      G:(GEPRIS)347572269 / $G:(DE-Juel1)aca_20190115$},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/863678},
}