“Sampling hazards in data inference of spatiotemporal dynamics” 周棟焯 博士 （上海交通大學）---2015年6月25日

時間:  2015年6月25日  10:00 am

地點：腦功能基因組學教育部重點實驗室一樓會議室

報告題目：Sampling hazards in data inference of spatiotemporal dynamics

報告人：上海交通大學自然科學研究院  周棟焯  研究員

主持人：周曉明 教授

 

報告人簡介：周棟焯博士于2002和2007年在北京大學獲學士和博士學位，從2007年2月至2009年12月，他在美國紐約大學庫朗研究所從事博士后研究，從2010年1月至今任上海交通大學自然科學研究院特別研究員。目前的主要研究興趣是理論和計算神經科學領域的科學問題，包括神經元網絡動力學的信息編碼原理的研究，針對神經生理實驗現象的數學建模與動力學模擬以及神經元網絡機制的研究，發展有助實驗的數據處理方法等，其工作發表在如PNAS，PLoS Comput. Biol，Phys. Rev. Lett.以及J. Comput. Neurosci.等國際期刊上。

 

報告簡介：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.