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@INPROCEEDINGS{Eppler:155353,
      author       = {Eppler, Jochen Martin and Kunkel, Susanne and Helias,
                      Moritz and Zaytsev, Yury and Plesser, Hans Ekkehard and
                      Gewaltig, Marc-Oliver and Morrison, Abigail and Diesmann,
                      Markus},
      title        = {20 years of {NEST}: a mature brain simulator},
      reportid     = {FZJ-2014-04526},
      year         = {2013},
      abstract     = {imulators have been developed, each specialized on one or
                      few spatial and temporal scales [1]. But thereliable and
                      reproducible simulation of such complex systems as the brain
                      is a very demanding challenge.Thus, the Computational
                      Neuroscience community concentrated on a few reliable and
                      widely used simula-tion tools in recent years. This
                      concentration was not least the result of a series of
                      large-scale EU fundedprojects, such as FACETS, BrainScaleS
                      and the recently announced Human Brain Project.From its
                      humble beginnings as a PhD-student project 20 years ago, the
                      Neural Simulation Tool NEST [2]saw its first incarnation as
                      the SYNOD simulator in 1995 [3]. By tightly coupling
                      software development withcomputational neuroscience research
                      [4], simulator technology evolved steadily, facilitating new
                      scientificinsight at almost every step. Some key examples
                      were parallelization [5,6], exact integration of
                      modelequations [7], precise spike times in a time-driven
                      simulator [8,9], spike-timing dependent [10] and
                      neuro-modulated plasticity [11], and a Topology module for
                      spatially structured networks [12]. Streamlined
                      datastructures [13] allow NEST to efficiently exploit the
                      capabilities of some of the largest computers on Earth
                      forsimulations on the brain scale [14]. Systematic quality
                      assurance through testsuites [15] and continuousintegration
                      technology [16] ensure simulator reliability. With a
                      user-friendly Python-based interface [17],integration with
                      PyNN [18] for simulator-independent scripting and MUSIC
                      support [19] for integrated multi-scale simulation, NEST is
                      a powerful simulation tool for brain-scale simulations
                      today. References[1] Brette et al (2007) Simulation of
                      networks of spiking neurons: A review of tools and
                      strategies. J Comput Neurosci.[2] Gewaltig $\&$ Diesmann
                      (2007) NEST (NEural Simulation Tool). Scholarpedia.[3]
                      Diesmann et al. (1995) SYNOD: an Environment for Neural
                      Systems Simulations. The Weizmann Institute of Science.[4]
                      Kunkel et al. (2010) NEST: Science-driven development of
                      neuronal network simulation software.[5] Morrison et al.
                      (2005) Advancing the boundaries of high connectivity network
                      simulation with distributed computing.[6] Plesser et al.
                      (2007) Efficient Parallel Simulation of Large-Scale Neuronal
                      Networks on Clusters of Multiprocessor Computers.[7] Rotter
                      $\&$ Diesmann (1999) Exact digital simulation of
                      time-invariant linear systems with applications to neuronal
                      modeling.[8] Morrison et al. (2007) Exact subthreshold
                      integration with continuous spike times in discrete time
                      neural network simulations.[9] Hanuschkin et al. (2010) A
                      general and efficient method for incorporating exact spike
                      times in globally time-driven simulations.[10] Morrison et
                      al. (2007) Spike-time dependent plasticity in balanced
                      recurrent networks.[11] Potjans et al. (2010) Enabling
                      functional neural circuit simulations with distributed
                      computing of neuromodulated plasticity.[12] Plesser $\&$
                      Enger (2013) NEST Topology User Manual.[13] Kunkel (2011)
                      Meeting the memory challenges of brain-scale network
                      simulation Front.[14] Helias et al. (2012 Supercomputers
                      ready for use as discovery machines for neuroscience.[15]
                      Eppler et al. (2009) A testsuite for a neural simulation
                      engine.[16] Zaytsev (2013) Increasing quality and managing
                      complexity in neuroinformatics software development with
                      continuous integration.[17] Eppler et al. (2008) PyNEST: A
                      Convenient Interface to the NEST Simulator.[18] Davison et
                      al. (2008) PyNN: a common interface for neuronal network
                      simulators.[19] Djurfeldt et al. (2010) Run-time
                      interoperability between neuronal network simulators based
                      on the MUSIC framework.},
      month         = {Jul},
      date          = {2013-07-02},
      organization  = {INM Retreat 2013, Jülich (Germany), 2
                       Jul 2013 - 3 Jul 2013},
      subtyp        = {Other},
      cin          = {INM-6 / JSC / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)JSC-20090406 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {331 - Signalling Pathways and Mechanisms in the Nervous
                      System (POF2-331) / 89574 - Theory, modelling and simulation
                      (POF2-89574) / BRAINSCALES - Brain-inspired multiscale
                      computation in neuromorphic hybrid systems (269921) /
                      W2Morrison - W2/W3 Professorinnen Programm der
                      Helmholtzgemeinschaft (B1175.01.12) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF2-331 / G:(DE-HGF)POF2-89574 /
                      G:(EU-Grant)269921 / G:(DE-HGF)B1175.01.12 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/155353},
}