Reverse Replay during Human Problem Solving
报告人：
Peter Dayan 博士、教授
Gatsby Computational Neuroscience Unit
University College London
报告人简介：
Peter Dayan studied Mathematics at Cambridge University, and received his PhD training in the Computational Neuroscience with Dr. David Willshaw at the University of Edinburgh and then did postdocs with Dr. Terry Sejnowski at the Salk Institute and Dr. Geoff Hinton at the University of Toronto, respectively. After three years of being an assistant professor at MIT, he found the Gatsby Computational Neuroscience Unit at UCL in 1998. He was a director of the Unit from 2002-2017. He won the Rumelhart Prize in 2012 and shared the Brain Prize in 2017. Dayan's interests centre on mathematical and computational models of neural processing, with a particular emphasis on representation, learning and decision making.
报告摘要 ：
The fast ‘replay’ of sequences of neural representations has been suggested as supporting learning and online planning. However, it has largely been studied in spatial tasks in rodents. I will show how we came upon reverse replay during our latest attempt to use the decoding of MEG data to capture the process of human model-based planning in a non-spatial sequential decision-making problem. During epochs in which our subjects were planning, their brains spontaneously visited representations of approximately four states in the problem in fast sequences lasting on the order of 120 milliseconds. These sequences followed backward trajectories along the permissible paths in the task. I will discuss the possible implications of this finding. This is joint work with Zeb Kurth-Nelson, Marcos Economides and Ray Dolan
时间：2017年12月29日上午10-12点
地点：京师学堂第三会议室（地下一层）
