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Simulation neurotechnologies for advancing brain research: Parallelizing large networks in NEURON

机译：用于神经研究的仿真神经技术：在NEURON中并行化大型网络

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Large multiscale neuronal network simulations are of increasing value as more big data is gathered about brain wiring and organization under the auspices of current major research initiative, such as Brain research through advancing innovative neurotechnologies. The development of these models requires development of new simulation technologies. We describe here the current use of the NEURON simulator with MPI (message passing interface) for simulation in the domain of moderately large networks on commonly available High Performance Computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike passing paradigm and post-simulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (from 500 to 100,000 cells), and using different numbers of nodes (1 to 256). We compare three types of networks, composed either of Izhikevich integrate-and-fire neurons, single-compartment Hodgkin-Huxley cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than Hodgkin-Huxley networks, although differences were small since all tested cells were point neurons with a single compartment.
机译：大型多尺度神经网络仿真的价值日益增加，因为在当前主要研究计划的主持下，例如通过先进的神经技术进行大脑研究，收集了有关大脑布线和组织的更多大数据。这些模型的开发需要开发新的仿真技术。我们在这里描述了NEURON模拟器与MPI（消息传递接口）的当前用法，用于在通常的高性能计算机（HPC）上的中等规模网络中进行模拟。我们讨论了此类仿真的基本布局，包括仿真设置方法，运行时尖峰传递范式以及仿真后数据存储和数据管理方法。通过使用Neuroscience Gateway（用于提供访问大型HPC的计算神经科学门户），我们可以对不同大小（从500到100,000个细胞）的神经元网络的仿真进行基准测试，并使用不同数量的节点（1到256个）。我们比较了三种类型的网络，它们由Izhikevich集成并发射神经元，单隔室霍奇金-赫克斯利细胞或混合网络组成，每种网络各占一半。结果表明，仿真运行时间随网络规模近似线性增加，而随节点数量线性近似减少。具有I＆F神经元的网络比Hodgkin-Huxley网络要快，尽管差异很小，因为所有测试的细胞都是具有单个隔室的点神经元。

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