报告人：陈哲，美国纽约大学医学院副教授

题目：破解睡眠过程记忆的奥秘

时间：2017年6月7号，上午10:00-11:30

地点：心理学部213报告厅

简介：Zhe Chen received Ph.D. degree (2005) in Electrical and Computer Engineering from McMaster University, Canada. In 2005 he joined the RIKEN Brain Science Institute as a research scientist. From 2007-2013 he worked MIT and Massachusetts General Hospital/Harvard Medical School, first as a senior research fellow then as a junior research faculty. From 2013-2014, he was a senior research scientist and principal investigator in the Picower Institute for Learning and Memory at MIT. Since 2014 he will become an assistant professor in the Department of Psychiatry, Neuroscience and Physiology at the New York University (NYU) School of Medicine. His research interests focus on computational neuroscience, neuroengineering, neural and biomedical signal processing, computational statistics and machine learning. He received a number of scholarships and honors. He is the lead author of the book Correlative Learning: A Basis for Brain and Adaptive Systems (Johns & Wiley, 2007) and the editor of the book Advanced State Space Methods for Neural and Clinical Data (Cambridge University Press, 2015). He is currently co-editing a book Dynamic Neuroscience: Statistics, Modeling and Control (Springer, 2017). 更多信息参见：https://med.nyu.edu/faculty/zhe-chen

报告人：陈哲，美国纽约大学医学院副教授

题目：神经元集群动态性的隐变量建模

时间：2017年6月7号，下午3:00-4:00

地点：心理学部213报告厅

简介：Neural activity is dynamic at various spatiotemporal scales. We consider a general class of latent variable models for analyses of dynamic neural data. The inference of latent variable models can lead to novel solutions for signal detection, neural decoding, denoising, dimensionality reduction and data visualization. We illustrate our methods with several neuroscience applications using neuronal population spike trains recorded from the rodent hippocampus, rodent somatosensory cortex and anterior cingulate cortex. Particularly, we will emphasize the brain-machine interface (BMI) application for detecting the onset of acute pain signals in a rodent model.