School of Computer Science, The University of Manchester Manchester, UK ; Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wakoshi Saitama, Japan.
Faculty of Life Sciences, The University of Manchester Manchester, UK.
Front Neurosci. 2014 May 30;8:131. doi: 10.3389/fnins.2014.00131. eCollection 2014.
Simulations of neural circuits are bounded in scale and speed by available computing resources, and particularly by the differences in parallelism and communication patterns between the brain and high-performance computers. SpiNNaker is a computer architecture designed to address this problem by emulating the structure and function of neural tissue, using very many low-power processors and an interprocessor communication mechanism inspired by axonal arbors. Here we demonstrate that thousand-processor SpiNNaker prototypes can simulate models of the rodent barrel system comprising 50,000 neurons and 50 million synapses. We use the PyNN library to specify models, and the intrinsic features of Python to control experimental procedures and analysis. The models reproduce known thalamocortical response transformations, exhibit known, balanced dynamics of excitation and inhibition, and show a spatiotemporal spread of activity though the superficial cortical layers. These demonstrations are a significant step toward tractable simulations of entire cortical areas on the million-processor SpiNNaker machines in development.
神经回路的模拟受到可用计算资源（特别是大脑和高性能计算机之间的并行性和通信模式的差异）的规模和速度限制。SpiNNaker 是一种计算机体系结构，旨在通过模拟神经组织的结构和功能来解决这个问题，使用非常多的低功耗处理器和一种受轴突树突启发的处理器间通信机制。在这里，我们展示了千个处理器 SpiNNaker 原型可以模拟包含 50000 个神经元和 5000 万个突触的啮齿动物桶系统模型。我们使用 PyNN 库来指定模型，并使用 Python 的固有特性来控制实验过程和分析。这些模型再现了已知的丘脑皮质反应转换，表现出已知的、兴奋和抑制的平衡动力学，并显示出通过浅层皮质层的活动的时空扩展。这些演示是朝着在正在开发的百万处理器 SpiNNaker 机器上进行整个皮质区域的可处理模拟迈出的重要一步。
