学术报告
同济大学计算机科学与技术系智信讲坛第(106)期
题目：Brain Network Analysis Based on Communication Theory
报告人：Tongtong Li
时间：2017年7月24日周一上午 9:00
地点：电信楼403
组织单位：计算机科学与技术系
邀请人：吴俊教授
报告人简介：
Tongtong Li received her Ph.D. degree in Electrical Engineering in 2000 from Auburn University. From 2000 to 2002, she was with Bell Labs, and had been working on the design and implementation of 3G and 4G systems. Since 2002, she has been with Michigan State University, where she is now an Associate Professor. Prof. Li’s research interests fall into the areas of wireless and wired communications, wireless security, information theory and statistical signal processing, with applications in neuroscience.More recently, She has been working on brain networks and connectivity-system modeling, capacity and stability analysis, as well as brain signal extraction and brain-controlled applications. She is a recipient of a National Science Foundation (NSF) CAREER Award (2008) for her research on efficient and reliable wireless communications. Prof. Li served as an Associate Editor for IEEE Signal Processing Letters from 2007-2009, and an Editorial Board Member for EURASIP Journal Wireless Communications and Networking from 2004-2011. She served as an Associate Editor for IEEE Transactions on Signal Processing from 2012-2016.
内容提要：
The brain is a communication network. At the neuron level, information exchanges are achieved through communications between synapses (meaning conjunction). At the system level, different brain regions formulate a dynamic communication network, and connectivity between the brain regions generates our minds. Brain researchers are increasingly looking for advanced computational analysis tools to assist them in understanding the functions and dysfunctions of specific brain circuits. At the same time, driven by the revolution in information theory, the communications area has accumulated rich methodologies for system modeling, design, signal processing and extraction, and network characterization and evaluation. To this end: can we develop innovative computational analysis tools for brain research by exploiting the methodologies in communications, especially tools in information theory? In this talk, we will look at: (1) Characterization of brain connectivity using mutual information; (2) Causality analysis using directed information framework; (3) Brain network connectivity pattern analysis and its applications in classification. We will also discuss some future work on brain network analysis.
欢迎各位老师同学踊跃参加！