主 题: Information Sciences Seminar——Latent Variable Modeling of Neural Population Dynamics
报告人: Dr. Zhe Chen (New York University)
时 间: 2017-11-30 15:00-16:00
地 点: Room 1365, Sciences Building No.1
Abstract: Neural activity is dynamic at various spatiotemporal scales. We consider a general class of latent variable models for analyses of dynamic neural data. Statistical 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, monkey primary motor cortex, rodent somatosensory cortex and anterior cingulate cortex. Neuroengineering applications in real-time brain-machine interface (BMI) will be discussed.
Bio: 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), and the co-editor of Dynamic Neuroscience: Statistics, Modeling and Control (Springer, 2018).
Dr. Chen has served in several US NSF/NIH grant panels. He is the associate editor of the international journal Neural Networks (Elsevier). He has served as the guest editor of three Special Issues published in Computational Intelligence and Neuroscience, and Frontiers in Computational Physiology and Medicine. He is a senior member of the IEEE and an Early Career Award (ECA) recipient from the Mathematical Biosciences Institute. His Scholarpedia article “State Space Model” (coauthored with Emery Brown) has won a Brain Corporation Prize in Computational Neuroscience (3rd Place). He is the lead principal investigator for two active NSF/NIH-CRCNS (Collaborative Research in Computational Neuroscience) grants from the National Science Foundation (NSF) and the National Institutes of Health (NIH).