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Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions
Frontiers in Neuroinformatics ( IF 3.5 ) Pub Date : 2020-05-05 , DOI: 10.3389/fninf.2020.00012
Jakob Jordan 1, 2, 3, 4 , Moritz Helias 2, 3, 4, 5 , Markus Diesmann 2, 3, 4, 5, 6 , Susanne Kunkel 7
Affiliation  

Investigating 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.

中文翻译:

带间隙连接的尖峰神经元网络分布式仿真中的高效通信

今天,通过可扩展到最大的当代超级计算机的最先进的模拟代码,研究具有真实突触数量的大规模尖峰神经元网络的动力学和功能成为可能。然而,由于在每个计算节点上收集全局数据的通信方案,除了化学突触之外,涉及电相互作用(也称为间隙连接)的模拟的扩展性很差。与化学突触相比,间隙连接的数量要少得多。为了提高可扩展性,我们通过将现有的持续交互框架与最近提出的针对尖峰的定向通信方案集成来利用这种稀疏性。使用 NEST 模拟器中的参考实现,我们展示了集成框架的出色可扩展性,将间隙连接的大规模模拟加速一个数量级以上。这首次允许在具有分布在数千个计算节点上的自然突触密度的大规模神经元网络模型中有效探索化学和电耦合的相互作用。
更新日期:2020-05-05
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