Lattice systems arising from neural networks, referred to as neural lattice models, have attracted much attention recently. They can be broadly classified into two types: one developed as the discretization of continuous neural field models, namely neural field lattice systems, and the other as the limit of finite dimensional discrete neural networks when their sizes become increasingly large. In the lecture we will introduce a few interesting neural lattice models and investigate their long term dynamics. These dynamics provide insight into the stability of large neural networks, as well as justification of finite dimensional approximations for numerical simulations of such networks.
27/07/2020
The webinar can be reached at this Goggle Meet link:
https://meet.google.com/rfw-tbug-paz
at 4:00pm (Italian time)
Xiaoying Han received her B.E. in computer science from the special class for the gifted young of university of science and technology of China, and Ph.D. in applied mathematics from State University of New York at Buffalo. She joined the department of mathematics and statistics at Auburn University in 2007 and is currently a professor of mathematics at Auburn University. Xiaoying Han’s research specialty lies in analysis and simulation of nonautonomous/random differential equations and infinite dimensional dynamical systems with applications in the applied sciences. She was named the 2018-2020 Margurite Scharnagle Endowed Professor of Auburn University, and awarded the 2020-2021 U.S. Fulbright scholar.