Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website .
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机译:详细的脑电路模型的大规模数值模拟对于识别关于脑功能的假设并测试其一致性和合理性非常重要。然而,模拟现实模型所面临的挑战是计算速度。在本文中,我们介绍了GeNN(GPU增强的神经元网络)框架,该框架旨在促进将图形加速器用于大规模神经元网络的计算模型,以应对这一挑战。 GeNN是一个开放源代码库,可通过灵活且可扩展的界面生成代码来加速NVIDIA GPU上的网络仿真执行,而无需用户的深入技术知识。我们提供的性能基准表明,对于基于100万个电导的Hodgkin-Huxley神经元的网络,与CPU的单核相比,可以实现200倍的加速,但是对于其他模型,加速可以有所不同。 GeNN可用于Linux,Mac OS X和Windows平台。可以在项目网站上找到源代码,用户手册,教程,Wiki,深入的示例项目以及所有其他相关信息。
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