Full scale simulations of neuronal network models of the brain are challenging due to the high density of connections between neurons. This contribution reports run times shorter than the simulated span of biological time for a full scale model of the local cortical microcircuit with explicit representation of synapses on a recent conventional compute node. Realtime performance is relevant for robotics and closed-loop applications while sub-realtime is desirable for the study of learning and development in the brain, processes extending over hours and days of biological time.

翻译：大脑神经网络模型的全面模拟具有挑战性,因为神经元之间的连接密度很高。这一贡献报告比模拟的生物时间长度短得多,因为局部皮质微电路的全面模型比模拟的生物时间短得多,在最近的常规计算节点上直截了当地表示突触。实时性能与机器人和闭环应用有关,而亚实时则适合用于大脑的学习与发展研究,过程持续数小时和数日的生物时间。