One of the key ideas in realizing human-like intelligence is to understand enough information-processing mechanisms in the human brain. Brain Informatics is a rapidly expanding interdisciplinary field to systematically utilize brain-related data, information, and knowledge coming from the entire research process for deeply brain investigation. In the past few years, a data-centric conceptual brain model, namely Data-Brain, has been proposed to meet requirements of a systematic methodology of Brain Informatics. Although the Data-Brain model provides a conceptual framework and detailed description for managing and analyzing brain big data, the increasing demand still requires the support of existing and future advanced technologies. The goal of this paper is to discuss and explore the extensible version of the Data-Brain with advanced computing techniques, which will benefit high-efficiency brain data exploitation and facilitate high-efficient brain data management. Particularly, we advocate that the human-in-the-loop approach should be designed in the processes of data mining and knowledge discovery using human-computer interaction and collaborative computing with human intelligence. Their synergistic use is expected to power future progress for building intelligent systems and applications connected with the study of complex human brain.

实现类人智力的关键思想之一是了解人脑中足够的信息处理机制。脑信息学是一个快速发展的跨学科领域，可以系统地利用来自整个研究过程的与脑相关的数据，信息和知识来进行深入的脑研究。在过去的几年中，已经提出了以数据为中心的概念性大脑模型，即Data-Brain，以满足大脑信息学系统方法学的要求。尽管Data-Brain模型提供了用于管理和分析大脑大数据的概念框架和详细描述，但不断增长的需求仍然需要现有和未来先进技术的支持。本文的目的是使用先进的计算技术来讨论和探索Data-Brain的可扩展版本，这将有益于高效的大脑数据开发并促进高效的大脑数据管理。特别是，我们主张应该在人机交互和具有人类智能的协作计算的数据挖掘和知识发现过程中设计人为循环方法。它们的协同使用有望推动构建与复杂人脑研究相关的智能系统和应用程序的未来发展。我们主张应该在人机交互和具有人类智能的协作计算的数据挖掘和知识发现过程中设计人为环进方法。它们的协同使用有望推动构建与复杂人脑研究相关的智能系统和应用程序的未来发展。我们主张应该在人机交互和具有人类智能的协作计算的数据挖掘和知识发现过程中设计人为环进方法。它们的协同使用有望推动构建与复杂人脑研究相关的智能系统和应用程序的未来进展。