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Parallel Event-Driven Neural Network Simulations Using the Hodgkin-Huxley Neuron Model

机译:使用Hodgkin-Huxley神经元模型的并行事件驱动神经网络仿真

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Neural systems are composed of a large number of highly-connected neurons and are widely simulated within the neurological community. In this paper, we examine the application of parallel discrete event simulation techniques to networks of a complex model called the Hodgkin-Huxley neuron[1]. We describe the conversion of this model into an event-driven simulation, a technique that offers the potential of much greater performance in parallel and distributed simulations compared to time-stepped techniques. We report results of an initial set of experiments conducted to determine the feasibility of this parallel event-driven Hodgkin-Huxley model and analyze its viability for large-scale neural simulations.
机译:神经系统由大量高度连接的神经元组成,并在神经系统内被广泛模拟。在本文中,我们研究了并行离散事件模拟技术在称为Hodgkin-Huxley神经元[1]的复杂模型的网络中的应用。我们描述了该模型到事件驱动仿真的转换,这种技术与时分技术相比,在并行和分布式仿真中具有更大的性能潜力。我们报告了一组初步实验的结果,以确定该并行事件驱动的Hodgkin-Huxley模型的可行性,并分析了其在大规模神经模拟中的可行性。

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