Speaker： 李孟 , 研究员

 中国科学院上海微系统与信息技术研究所

Time：2021年7月20日（星期二）上午10:00-11:00

Place：北京大学英杰交流中心306会议室

Host：北京大学分子医学研究所 陈良怡

摘要：Interpreting large-scale neural signals advances our understanding of neural computation in the brain. In this talk, I will present some results emerging from the encoding and decoding of large-scale neural signals. Using tracking microscopy to monitor whole-brain neuronal activity at cellular resolution in freely moving zebrafish larvae and DNN to track multiple objects in the images, we show that zebrafish spontaneously alternate between two persistent internal states during foraging for live prey (Paramecia). By analyzing the activities of ~10000 neurons in the brain, we uncover a dorsal raphe subpopulation with persistent activity that robustly encodes the exploitation state. The exploitation-state-encoding neurons, together with a multimodal trigger network that is associated with state transitions, form a stochastically activated nonlinear dynamical system. The activity of this oscillatory network correlates with a global retuning of sensorimotor transformations during foraging that leads to marked changes in both the motivation to hunt for prey and the accuracy of motor sequences during hunting. This work reveals an important hidden variable that shapes the temporal structure of motivation and decision-making.

报告人简介：李孟，博士，上海微系统所研究员，博士生导师。入选“中科院BR”、“上海QR”计划。2009年哈尔滨工业大学仪器科学与技术专业获博士学位，先后在美国佐治亚医学院、哈佛大学从事博士后研究，2019年加入德国马克普朗克学会生物控制论研究所任研究科学家。2021年6月入职中科院上海微系统所。李孟博士在大尺度群体神经信号编解码领域取得了系列创新性学术成绩：1）解码了十万神经元量级的全脑神经信号，揭示了大脑内在状态动态转化的控制机理，建立了复杂高阶行为与大脑神经网络内在状态间的关系；2）提出了基于自信息理论的群体神经元信息编码体系和大脑连接论理论框架，实现对群体神经元活动的盲解码；3）应用大尺度神经信号分析与建模方法解决脑科学和脑疾病问题。共发表论文26篇，包括第一作者（含共同第一作者）发表在Nature、Cerebral Cortex、Advanced Science等发表的论文11篇。其中Nature文章被Nature杂志以“News and Views”和“News Feature”形式进行重点介绍和评价。