Abstract: Current experimental techniques usually cannot probe the global interconnection pattern of a network. Thus, reconstructing or reverse- engineering the network topology of coupled nodes based upon observed data has become a very active research area. Most existing reconstruction methods are based on networks of oscillators with generally smooth dynamics. However, for nonlinear and non-smooth stochastic dynamical systems, e.g., neuronal networks, the reconstruction of the full topology remains a challenge. Here, we present a noninterventional reconstruction method, which is based on Granger causality theory, for the widely used conductance-based, integrate-and-fire type neuronal networks. For this nonlinear system, we have established a direct theoretical connection between Granger causal connectivity and structural connectivity.

 

报告人简介: 周栋焯博士于2002和2007年在北京大学数学科学学院计算数学专业获学士和博士学位, 从2007年2月至2009年12月, 他在美国纽约大学库朗研究所从事博士后研究。从2010年1月至今任上海交通大学数学系、自然科学研究院特别研究员。周栋焯博士目前的主要研究兴趣是理论和计算神经科学领域的科学问题, 包括神经元网络动力学的数学性质与高性能计算方法的研究, 针对神经生理实验现象的数学建模与动力学模拟的研究, 以及发展相关对实验有帮助的数据处理方法等。他至今已发表SCI论文近20篇, 其工作发表在如PNAS, PRL, PLoS Comput. Biol., PLoS ONE以及J. Comput. Neurosci.等应用数学与科学的国际重要期刊上。