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@ARTICLE{Potjans:19275,
      author       = {Potjans, W and Morrison, A and Diesmann, M},
      title        = {{E}nabling functional neural circuit simulations with
                      distributed computing of neuromodulated plasticity},
      journal      = {Frontiers in computational neuroscience},
      volume       = {4},
      issn         = {1662-5188},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {PreJuSER-19275},
      pages        = {1-17},
      year         = {2010},
      note         = {We are most grateful to Hans Ekkehard Plesser for language
                      legality consultation. We also thank the editor and the
                      reviewers for the constructive interaction which helped us
                      to considerably improve the integration of our work into the
                      special issue. Partially funded by DIP F1.2, BMBF Grant
                      01GQ0420 to the Bernstein Center for Computational
                      Neuroscience Freiburg, EU Grant 15879 (FACETS), the Junior
                      Professor Program of Baden-Wurttemberg, "The Next-Generation
                      Integrated Simulation of Living Matter" project, part of the
                      Development and Use of the Next-Generation Supercomputer
                      Project of the Ministry of Education, Culture, Sports,
                      Science and Technology (MEXT) of Japan and the Helmholtz
                      Alliance on Systems Biology. Access to supercomputing
                      facility through JUGENE-Grant JINB33.},
      abstract     = {A major puzzle in the field of computational neuroscience
                      is how to relate system-level learning in higher organisms
                      to synaptic plasticity. Recently, plasticity rules depending
                      not only on pre- and post-synaptic activity but also on a
                      third, non-local neuromodulatory signal have emerged as key
                      candidates to bridge the gap between the macroscopic and the
                      microscopic level of learning. Crucial insights into this
                      topic are expected to be gained from simulations of neural
                      systems, as these allow the simultaneous study of the
                      multiple spatial and temporal scales that are involved in
                      the problem. In particular, synaptic plasticity can be
                      studied during the whole learning process, i.e., on a time
                      scale of minutes to hours and across multiple brain areas.
                      Implementing neuromodulated plasticity in large-scale
                      network simulations where the neuromodulatory signal is
                      dynamically generated by the network itself is challenging,
                      because the network structure is commonly defined purely by
                      the connectivity graph without explicit reference to the
                      embedding of the nodes in physical space. Furthermore, the
                      simulation of networks with realistic connectivity entails
                      the use of distributed computing. A neuromodulated synapse
                      must therefore be informed in an efficient way about the
                      neuromodulatory signal, which is typically generated by a
                      population of neurons located on different machines than
                      either the pre- or post-synaptic neuron. Here, we develop a
                      general framework to solve the problem of implementing
                      neuromodulated plasticity in a time-driven distributed
                      simulation, without reference to a particular implementation
                      language, neuromodulator, or neuromodulated plasticity
                      mechanism. We implement our framework in the simulator NEST
                      and demonstrate excellent scaling up to 1024 processors for
                      simulations of a recurrent network incorporating
                      neuromodulated spike-timing dependent plasticity.},
      keywords     = {J (WoSType)},
      cin          = {INM-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406},
      pnm          = {Neurowissenschaften / Brain-Scale Simulations
                      $(jinb33_20090701)$},
      pid          = {G:(DE-Juel1)FUEK255 / $G:(DE-Juel1)jinb33_20090701$},
      shelfmark    = {Mathematical $\&$ Computational Biology / Neurosciences},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:21151370},
      pmc          = {pmc:PMC2996144},
      UT           = {WOS:000288499700001},
      doi          = {10.3389/fncom.2010.00141},
      url          = {https://juser.fz-juelich.de/record/19275},
}