Louis Tao Letian is Professor in the Center for Bioinformatics at Peking University. He received a Ph.D. degree in Physics from the University of Chicago, and an undergraduate degree in Physics from Harvard University. He has held research positions at Cambridge University, Columbia University and New York University, and was most recently an Assistant Professor in the Department of Mathematical Sciences at the New Jersey Institute of Technology. He specializes in large-scale scientific computation and mathematical modeling of biological systems, using a combination of direct numerical simulations, numerical bifurcation theory, and mathematical asymptotics. Current research interests include computational neuroscience of the mammalian visual pathway, the monkey oculomotor system and the zebrafish lateral line, and complex networks in systems biology.

Tentative Title

Low-Dimensional Characterization of Neuronal Network Activity in a Large-Scale Model of the Visual Cortex

Abstract

A major theoretical challenge in systems neuroscience modeling is to summarize the dynamics of complex neuronal networks in low dimensional models. While most approaches have focused either on developing reduced descriptions of single neurons or on mean-field, population density models of networks, here, we describe our progress in developing low-dimensional dynamical systems models of large-scale cortical networks using a data-driven approach. Taking a model visual cortical network to be our experimental system, we use empirical principal components analysis of simulation data as a dimension reduction tool to generate target dynamical systems which allow us to predict (and postdict) the simulation data in an approximate, but mathematically consistent, fashion. Furthermore, we use empirical, data-driven PCA on a small subset of model neurons; our results suggest that it may be possible to generate such target dynamical systems from simultaneous electro-physiological measurements of network neurons *in vivo*.