Abstract

I will introduce the so-called energy landscape analysis of multivariate time series data. In this analysis, one identifies the state of the system at each time point as the position of a “ball” constrained on an energy landscape inferred from data. A ball tends to go downhill on the energy landscape whereas it sometimes goes uphill to transit from one local minimum of the energy to another, possibly corresponding to major dynamical transitions. The method is based on the Ising model (also known as Boltzmann machine). The application of the method to neuroimaging and other data is illustrated. I will also pose technical challenges we are interested in addressing.

Naoki Masuda received his PhD in 1998 from the University of Tokyo. He worked as Lecturer and then Associate Professor at the University of Tokyo between 2006 and 2014. He moved to University of Bristol, Department of Engineering Mathematics as Senior Lecturer, March 2014. His research interests include network science, mathematical biology and neuroscience (in particular, brain networks and social neuroscience).