论文标题
数字人脑的模拟和同化
Simulation and assimilation of the digital human brain
论文作者
论文摘要
在这里，我们介绍了数字大脑（DB），这是一个基于个性化的磁性图像数据和生物学约束，在人脑大脑的大型神经元量表上模拟尖峰神经元网络的平台。 DB的目的是重现人脑作用的静止状态和某些方面。模拟的一部分实现了一个具有多达860亿个神经元和14,012 GPU的建筑，其中包括GPU之间的两级路由方案，以加速高达47.8万亿个神经元突触的Spike传播。我们表明，DB可以以高相关系数的形式重现人脑静止状态的血氧级依赖性信号，并与其感知输入相互作用，如视觉任务所示。这些结果表明，实施人脑数字表示的可行性，这可以打开广泛的潜在应用。
Here, we present the Digital Brain (DB), a platform for simulating spiking neuronal networks at the large neuron scale of the human brain based on personalized magnetic-resonance-imaging data and biological constraints. The DB aims to reproduce both the resting state and certain aspects of the action of the human brain. An architecture with up to 86 billion neurons and 14,012 GPUs, including a two-level routing scheme between GPUs to accelerate spike transmission up to 47.8 trillion neuronal synapses, was implemented as part of the simulations. We show that the DB can reproduce blood-oxygen-level-dependent signals of the resting-state of the human brain with a high correlation coefficient, as well as interact with its perceptual input, as demonstrated in a visual task. These results indicate the feasibility of implementing a digital representation of the human brain, which can open the door to a broad range of potential applications.
