Including Gap Junctions into Distributed Neuronal Network Simulations

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10087)


Contemporary simulation technology for neuronal networks enables the simulation of brain-scale networks using neuron models with a single or a few compartments. However, distributed simulations at full cell density are still lacking the electrical coupling between cells via so called gap junctions. This is due to the absence of efficient algorithms to simulate gap junctions on large parallel computers. The difficulty is that gap junctions require an instantaneous interaction between the coupled neurons, whereas the efficiency of simulation codes for spiking neurons relies on delayed communication. In a recent paper [15] we describe a technology to overcome this obstacle. Here, we give an overview of the challenges to include gap junctions into a distributed simulation scheme for neuronal networks and present an implementation of the new technology available in the NEural Simulation Tool (NEST 2.10.0). Subsequently we introduce the usage of gap junctions in model scripts as well as benchmarks assessing the performance and overhead of the technology on the supercomputers JUQUEEN and K computer.


Computational neuroscience Spiking neuronal network Gap junctions Waveform relaxation Supercomputer Large-scale simulation 



We gratefully acknowledge the NEST core team for an in-depth discussion of the user interface and Mitsuhisa Sato for hosting our activities at RIKEN AICS. Computing time on the K computer was provided through early access in the framework of the co-development program, project hp130120 of the General Use Category (2013), the Strategic Program (project hp150236, Neural Computation Unit, OIST), and MEXT SPIRE Supercomputational Life Science. The authors gratefully acknowledge the computing time on the supercomputer JUQUEEN [22] at Forschungszentrum Jülich granted by JARA-HPC Vergabegremium (provided on the JARA-HPC partition, jinb33) and Gauss Centre for Supercomputing (GCS) (provided by John von Neumann Institute for Computing (NIC) on GCS share, hwu12). Partly supported by Helmholtz Portfolio Supercomputing and Modeling for the Human Brain (SMHB), the Initiative and Networking Fund of the Helmholtz Association, the Helmholtz young investigator group VH-NG-1028, the Next-Generation Supercomputer Project of MEXT, and EU grant agreement No 720270 (HBP SGA1). All network simulations carried out with NEST (


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Mathematics and Natural SciencesUniversity of WuppertalWuppertalGermany
  2. 2.Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute IJülich Research CentreJülichGermany
  3. 3.RIKEN Advanced Institute for Computational ScienceProgramming Environment Research TeamKobeJapan
  4. 4.Department of Computational Science and Technology, School of Computer Science and CommunicationKTH Royal Institute of TechnologyStockholmSweden
  5. 5.Simulation Laboratory Neuroscience, Bernstein Facility for Simulation and Database TechnologyInstitute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research CentreJülichGermany
  6. 6.Okinawa Institute of Science and Technology, Neural Computation UnitOkinawaJapan
  7. 7.Laboratory for Neural Circuit TheoryRIKEN Brain Science InstituteWakoJapan
  8. 8.HPC Usability Research TeamRIKEN Advanced Institute for Computational ScienceKobeJapan
  9. 9.Jülich Supercomputing Centre, Jülich Research CentreJülichGermany
  10. 10.Institut für MathematikUniversität KasselKasselGermany
  11. 11.Department of Psychiatry, Psychotherapy and Psychosomatics, Medical FacultyRWTH Aachen UniversityAachenGermany
  12. 12.Department of Physics, Faculty 1RWTH Aachen UniversityAachenGermany

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