In this work, we present a computing platform named digital twin brain (DTB) that can simulate spiking neuronal networks of the whole human brain scale and more importantly, a personalized biological brain structure. In comparison to most brain simulations with a homogeneous global structure, we highlight that the sparseness, couplingness and heterogeneity in the sMRI, DTI and PET data of the brain has an essential impact on the efficiency of brain simulation, which is proved from the scaling experiments that the DTB of human brain simulation is communication-intensive and memory-access intensive computing systems rather than computation-intensive. We utilize a number of optimization techniques to balance and integrate the computation loads and communication traffics from the heterogeneous biological structure to the general GPU-based HPC and achieve leading simulation performance for the whole human brain-scaled spiking neuronal networks. On the other hand, the biological structure, equipped with a mesoscopic data assimilation, enables the DTB to investigate brain cognitive function by a reverse-engineering method, which is demonstrated by a digital experiment of visual evaluation on the DTB. Furthermore, we believe that the developing DTB will be a promising powerful platform for a large of research orients including brain-inspiredintelligence, rain disease medicine and brain-machine interface.

翻译：本文提出一个名为数字孪生脑（DTB）的计算平台，该平台能够模拟全人脑尺度的脉冲神经元网络，更重要的是，可模拟个性化生物脑结构。与大多数采用均匀全局结构的脑模拟相比，我们强调脑部sMRI、DTI和PET数据中的稀疏性、耦合性和异质性对脑模拟效率具有根本性影响。扩展实验证明，人脑模拟的DTB属于通信密集型和内存访问密集型计算系统，而非计算密集型。我们利用一系列优化技术，将异构生物结构的计算负载与通信流量均衡集成至基于GPU的通用高性能计算平台，实现了全人脑尺度脉冲神经元网络的领先模拟性能。另一方面，配备介观数据同化的生物结构使DTB能够通过逆向工程方法探究脑认知功能，这一点通过DTB上视觉评估的数字实验得到验证。我们相信，发展中的DTB将成为类脑智能、脑疾病医学和脑机接口等众多研究方向的有力平台。