Title: Statistical Learning of Neuronal Functional Connectivity
演讲嘉宾： 张春明教授，威斯康星大学统计系
演讲时间：2015年12月30日（周三）上午10：00-11：00
演讲地点：明德主楼1031
Abstract:
"Identifying the network structure of a neuron ensemble is critical forunderstanding how information is transferred within such a neural population. However, the spike train data pose significant challenges to conventional statistical methods due to not only the complexity, massive size and large scale, but also high dimensionality. In this paper, we propose a SIE regularization method for estimating the conditional intensities under the GLM framework to better capture the functional connectivity among neurons. We study the consistency of parameter estimation and model selection of the proposed method. An AcceleratedFull Gradient Update algorithm is developed to efficiently handle thecomplex penalty in the SIE-GLM for large sparse data sets applicable tospike train data. Simulation results indicate that our proposed methodoutperforms existing approaches. An application of the proposed methodto a real spike train data set provides some insight into the neuronalnetwork."
Biography (in English): Chunming Zhang got the B.S. in Mathematical Statistics from Nankai University in 1990, the M.S. degree in Computational Mathematics from Chinese Academy of Sciences in 1993, and the Ph.D. in Statistics from University of North Carolina–Chapel Hill in 2000. She was an Assistant professor (2000-2005), Associate Professor (2005-2010), and full professor (2010--) at the Dept. of Statistics, University of Wisconsin-Madison. She served as Associate Editors for Annals of Statistics (2007–2009),Journal of the American Statistical Association (2011–), and Journal of Statistical Planning and Inference (2012–). She was Program Chair–Elect (2014) and Program Chair (2015), Section on Nonparametric Statistics, American Statistical Association.She was elected to be Fellow of the Institute of Mathematical Statistics(2011). Her research interests include statistical modeling, estimation and inference with applications to neuroscience and neuroimaging data, genetics and econometrics and finance.
个人简历：张春明1990年数理统计专业本科毕业于南开大学，1993年计算数学专业硕士毕业于中国科学院，2000年统计专业博士毕业于美国北卡莱罗纳大学教堂山分校。之后，她任美国威斯康星大学麦迪逊分校统计系的助理教授 (2000-2005)，副教授(2005-2010)和正教授(2010至今)。担任以下刊物的副主编：Annals of Statistics (2007–2009), Journal of the American Statistical Association (2011–), and Journal of Statistical Planning and Inference (2012–).于2014年被选为并于2015年担任美国统计学会，非参数统计分会，的程序主席。于2011年获选为国际数理统计学会“会士”。她的研究兴趣包括统计建模和推断应用于神经科学及大脑成像，基因与遗传学，计量经济及金融。