Dynamic multi-scale modelling of primate visuo-motor systems

人脑是科学界已知的最复杂的系统。它包含数百亿个神经元，这些神经元通过一个由数万亿个连接组成的复杂网络进行通信。神经科学是基础研究的一个非常活跃的领域，有关大脑如何工作的信息积累得如此之快，以至于投入单独的研究努力以量化地将不同的研究结果整合到一个连贯的图景中变得重要。为此，计算模型既要规模大，又要细节精细，而且要包含抽象层，在不牺牲现实性的情况下提供洞察力。该项目通过开发新的计算机模拟技术来推进这一目标，该技术将允许对脑组织进行超大规模、准确的模拟。这些模拟的细节和逼真程度会因大脑的不同部分而有所不同，并随着时间的推移，将计算能力集中在每个时刻的关键区域。为了确保使用这项技术的计算机模拟将提供对大脑功能的洞察，将开发一个抽象层次，不断将模拟的精细细节与许多细胞的大规模信息处理功能联系起来。这项技术将提供一个基础，我们可以在此基础上建立具有逼真的解剖结构、逼真的神经活动模式和逼真的信息处理能力的大脑计算机模型。这最终将促进我们对大脑功能的理解，并促进像我们一样理解世界的人工系统的发展。

The human brain is the most complex system known to science. It contains tens of billions of neurons, which communicate through an intricate network of trillions of connections. Neuroscience is a very active field of basic research, and information about how the brain works is being amassed so quickly that is has become important to invest separate research effort to quantitatively integrate diverse research results into a coherent picture. For this purpose, computational models are needed that are both large in scale and fine in detail, and which contain layers of abstraction that provide insight without sacrificing realism. This project advances this goal by developing new computer simulation technology that will allow very large scale, accurate simulations of brain tissue. The degree of detail and realism in these simulations will vary across different parts of the brain, and over time, to focus computational power on the key areas at each instant. To ensure that computer simulations that use this technology will provide insight into brain function, a hierarchy of abstractions will be developed, which constantly link fine detail of simulations to large-scale information processing functions of many cells. This technology will provide a foundation on which we can build computer models of the brain that have realistic anatomy, realistic patterns of neural activity, and realistic information processing capabilities. This will ultimately advance our understanding of brain function and facilitate development of artificial systems that understand the world as we do.