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題目：Lattice models arising from neural networks

報(bào)告人：韓曉瑩（Xiaoying Han） Auburn University 教授

時(shí)間:  12月19日（周二）上午 10: 00-10: 50

騰訊會(huì)議號(hào)：244340759

摘要：Several lattice dynamical systems modeling evolution of biological or artificial neural networks will be introduced. An emphasis will be given to the interconnection structures of neural networks and how they affect the stability of the networks. Effects of noisy external input and/or time delays in responding to activation will be addressed. Mathematical tools and techniques to study long term dynamics of these networks will be presented.

個(gè)人簡(jiǎn)介：Dr. Xiaoying Han graduated from the Special Class for the Gifted Young at the University of Science and Technology of China, with a B.E. in Computer Science in 2001. She received her Ph.D. in Mathematics from the State University of New York at Buffalo in 2007 and has since worked at Auburn University. She was promoted to full professor in 2017, awarded the Marguertie Scharnagle Endowed Professorship in 2018, and obtained College of Sciences and Mathematics Young Faculty Scholar Award in 2021. In 2020 she was named a U.S. Fulbright Scholar, funding her for a research visit to Brazil. She is the co-editor-in-chief for the journal Discrete Contin. Dyn. Syst. Ser. S, as well as an associate editor for a few other journals including Discrete Contin. Dyn. Syst. Ser. B, Stoch. Anal. Appl., etc. She has co-authored 4 monographs, and published around 70 papers in journals including SIAM J. Appl. Math., J. Differential Equations, SIAM J. Math. Anal., etc.