脑空间信息学——连接脑科学与类脑人工智能的桥梁

Brainsmatics—bridging the brain science and brain-inspired artificial intelligence

提出脑空间信息学是示踪、测量、分析、处理和呈现跨层次多尺度脑空间信息数据的一门综合与集成的科学.讨论了脑空间信息学的研究内容、技术体系和关键科学问题,分析了其学科定位,展望了其应用前景.以显微光学切片断层成像为核心的全脑网络可视化技术体系的建立,标志着脑空间信息学这一新兴交叉学科日臻成熟.基于具有明确时空尺度和位置信息的神经元类型、神经环路和网络、血管网络等三维精细脑结构与功能大数据,提取跨层次、多尺度的脑连接时空特征,脑空间信息学将帮助科学家更好地破译脑功能与脑疾病,并推动类脑人工智能的发展.

We propose a new approach of brain-spatial information science, abbreviated to brainsmatics, which refers to the integrated, systematic approach of tracing, measuring, analyzing, managing and displaying cross-level brain spatial data with multi-scale resolution. We discussed its research contents, technological systems and key scientific problems,analyzed its discipline orientation, and forecasted the applications. Taking the micro-optical sectioning tomography（MOST） serial techniques as the core, we have developed a multidisciplinary complete technical system of visible brain-wide network（VBN）, which makes brainsmatics more mature. Based on big data of three-dimensional fine structural and functional imaging of neuron types, neural circuits and networks, vascular network et al, with definite temporal-spatial resolution and specific spatial locations, brainsmatics makes it possible to better decipher the brain function and disease and promote the brain-inspired artificial intelligence by extracting cross-level and multi-scale temporal-spatial characteristics of brain connectivity.