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Front Neuroinform. 2015 Sep 9;9:22. doi: 10.3389/fninf.2015.00022. eCollection 2015.

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

Author information

1
Department of Mathematics and Science, Bergische Universität Wuppertal Wuppertal, Germany.
2
Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre Jülich, Germany ; Programming Environment Research Team, RIKEN Advanced Institute for Computational Science Kobe, Japan.
3
Programming Environment Research Team, RIKEN Advanced Institute for Computational Science Kobe, Japan ; Simulation Laboratory Neuroscience, Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Centre Jülich, Germany.
4
Neural Computation Unit, Okinawa Institute of Science and Technology Okinawa, Japan ; Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute Wako, Japan.
5
Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre Jülich, Germany ; Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University Aachen, Germany ; Department of Physics, Faculty 1, RWTH Aachen University Aachen, Germany.

Abstract

Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.

KEYWORDS:

gap junctions; large-scale simulation; parallel computing; spiking neuronal network; supercomputer; waveform relaxation

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