State Space Methods in Neuronal Data Analysis
陈哲博士
2013-07-01
演讲人简介:
Zhe (Sage) Chen received Ph.D. degree (2005) in electrical and computer engineering from McMaster
University, Canada. In 2005 he joined RIKEN Brain Science Institute as a research scientist.
Since 2007 he has been working at MIT and Massachusetts General Hospital/Harvard Medical School.
He is currently an Assistant Professor at School of Medicine, University of North Carolina. His
research interests include computational neuroscience, neuroengineering, neural signal processing,
computational statistics and machine learning. He has received a number of scholarships and honors,
and served as guest editor for a few journals. He is the lead author of the book Correlative
Learning: A Basis for Brain and Adaptive Systems (Wiley, 2007). He is a senior member of the
IEEE and an Early Career Award (ECA) recipient from the Mathematical Biosciences Institute.
State space models are useful tools for dynamic analysis of time series, and have been widely
used in the areas of control, signal processing and robotics. In this tutorial talk, I will
discuss the formulation and principle of state space analysis for neuronal data, such as
continuous EEG/LFP or discrete neural spike trains. Basic state space model and its extensions
will be covered, along with various methods of statistical inference. I will present plenty
of examples of state-of-the-art space space analysis for neural data.
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