“Sampling hazards in data inference of spatiotemporal dynamics” 周栋焯 博士 (上海交通大学)---2015年6月25日
“Sampling hazards in data inference of spatiotemporal dynamics” 周栋焯 博士 (上海交通大学)---2015年6月25日
时间:  2015年6月25日  10:00 am
报告题目:Sampling hazards in data inference of spatiotemporal dynamics
报告人:上海交通大学自然科学研究院  周栋焯  研究员
主持人:周晓明 教授
报告人简介:周栋焯博士于2002和2007年在北京大学获学士和博士学位,从2007年2月至2009年12月,他在美国纽约大学库朗研究所从事博士后研究,从2010年1月至今任上海交通大学自然科学研究院特别研究员。目前的主要研究兴趣是理论和计算神经科学领域的科学问题,包括神经元网络动力学的信息编码原理的研究,针对神经生理实验现象的数学建模与动力学模拟以及神经元网络机制的研究,发展有助实验的数据处理方法等,其工作发表在如PNAS,PLoS Comput. Biol,Phys. Rev. Lett.以及J. Comput. Neurosci.等国际期刊上。
报告简介:Most of dynamical processes are continuous, whereas in experiment, signals are often measured in the form of discrete spatiotemporal series and conclusions are drawn by analyzing these sampled signals. In this talk, I will illustrate two examples to show how different samplings may lead to artifact of data processing and provide corresponding approaches to extract the intrinsic properties from the underlying continuous processes. The first example is about analyzing spatiotemporal activities measured by voltage-sensitive-dye-based optical imaging in the primary visual cortex of the awake monkey. Through computational modeling, we show that our model can well capture the phenomena observed in experiment and can separate them from those statistical effects arising from spatial averaging procedures in experiment. The second example is about analyzing Granger causality for information flow within continuous dynamical processes. We show that different sampling rate may potentially yield incorrect causal inferences and such sampling artifact can be present for both linear and nonlinear processes. We show how such hazards lead to incorrect network reconstructions and describe a strategy to obtain a reliable Granger causality inference.