主讲人简介：

戴琼海，1964年生，清华大学教授（信息学院&生命科学学院），清华脑与认知科学研究院院长、清华信息科学技术学院院长，中国人工智能学会理事长。近年来主要从事交叉科学的研究——脑工程与新一代人工智能的研究。研制完成了多维多尺度计算摄像仪器，旨在提供从亚细胞、组织到器官的多尺度动态观测数据，希望突破百万级脑神经连接的观测，揭示神经系统结构和功能等脑科学规律，为创建新一代神经计算方法（表达、转换和规则）提供支撑，为从脑科学到人工智能提供新的途径。

Born in 1964, Qionghai Dai is a Professor in Tsinghua University, and the director of the Institute of Brain and Cognitive Sciences at Tsinghua University (THUIBCS). Qionghai’s research centers on the interdisciplinary study of Brain Engineering and the next-generation Artificial Intelligence. He has built up various multi-scale multi-dimensional computational imaging instruments, aiming for the simultaneous multi-scale observation of dynamic structures spanning from organelles, cells, tissue, and organs. By developing advanced imaging techniques for the simultaneous recording of millions of neurons, he tries to understand the structures and mechanisms of entire neural circuits on various tasks at single-cell level, which can provide theoretical supports for next-generation neuromorphic computing algorithms (including expression, transform, and rules), as a new pathway from Brain Science to Artificial Intelligence.

讲座内容简介：

本报告围绕脑科学与人工智能: 1. 回顾了脑科学的发现对人工智能进步的贡献，人工智能突破性进展、尤其是深度卷积神经网络和贝叶斯网络在多个研究领域获得重大应用，表明人工智能已经进入了全新发展时代；2. 介绍以探索人类大脑工作机制以及绘制脑功能活动全图为目标的欧美日脑科学计划。进一步介绍奥巴马“脑计划”中的阿波罗计划的研究任务：大脑皮层网络的机器智能（MICrONS），其如何通过反向设计一立方毫米的大脑，将大脑激发的智能计算向前推进。3. 针对大规模神经网络观测记录的挑战，分析国际上开展宽视场高分辨率脑成像系统的进展，介绍了研制的宽视场计算摄像仪器的进展。4. 鉴于大脑皮层神经元组织结构对深度卷积神经网络的启示意义，探讨介观尺度皮层网络功能观测下，如何通过神经科学与数据科学结合来推动人工智能发展。

This talk centers on the correlation of Brain Science and Artificial Intelligence. It starts with reviewing the contribution of brain science development towards the advances of artificial intelligence, followed by the breakthrough progress of artificial intelligence, especially the important applications of deep convolution neural network and Bayesian network in many research fields. It shows that the artificial intelligence has entered a new era of development. Secondly, it introduces the American, European, and Japanese brain science programs for the working mechanism of the human brain and the mapping of brain function, and the Apollo Project of the Obama Brain Initiative: Machine Intelligence of the Cerebral Cortex Network (MICrons), about how to design a cubic-millimeter brain in reverse to advance the intelligent computing. Thirdly, in view of the challenges of large-scale neural network observation and recording, it reviews the recent developments of high-resolution brain imaging system with wide field of view, and introduces the novel Real-time, Ultra-large-Scale imaging at high resolution (RUSH) macroscope. Finally, given the enlightenment of the structure of cerebral cortical neurons on deep convolution neural networks, it explores how to promote the development of artificial intelligence through the combination of neuroscience and data science under the observation of mesoscopic cortical network functions.