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000875222 1001_ $$0P:(DE-Juel1)178920$$aJordan, Jakob$$b0$$eCorresponding author$$ufzj
000875222 245__ $$aEfficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions
000875222 260__ $$aLausanne$$bFrontiers Research Foundation$$c2020
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000875222 520__ $$aInvestigating the dynamics and function of large-scale spiking neuronal networks with realistic numbers of synapses is made possible today by state-of-the-art simulation code that scales to the largest contemporary supercomputers. However, simulations that involve electrical interactions, also called gap junctions, besides chemical synapses scale only poorly due to a communication scheme that collects global data on each compute node. In comparison to chemical synapses, gap junctions are far less abundant. To improve scalability we exploit this sparsity by integrating an existing framework for continuous interactions with a recently proposed directed communication scheme for spikes. Using a reference implementation in the NEST simulator we demonstrate excellent scalability of the integrated framework, accelerating large-scale simulations with gap junctions by more than an order of magnitude. This allows, for the first time, the efficient exploration of the interactions of chemical and electrical coupling in large-scale neuronal networks models with natural synapse density distributed across thousands of compute nodes.
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000875222 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b1$$ufzj
000875222 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b2
000875222 7001_ $$0P:(DE-Juel1)151364$$aKunkel, Susanne$$b3
000875222 773__ $$0PERI:(DE-600)2452979-5$$a10.3389/fninf.2020.00012$$gVol. 14, p. 12$$p12$$tFrontiers in neuroinformatics$$v14$$x1662-5196$$y2020
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