Often in complex systems science we are faced with a system for which we have a multivariate time-series of measurements (e.g. brain recordings), but we do not know the underlying network structure between the variables which produced this data. There are several recently proposed methods for inferring an underlying information network from such time-series data (e.g. using transfer entropy), yet many of the features of these techniques remain unclear. This project will explore the strengths and weakness of these techniques, aiming to establish their range of applicability. It will also seek to enhance these algorithms by incorporating findings on the relationship between network structure and dynamics that will be established in the wider research project the PhD student is embedded in. The PhD project will involve software development on algorithms and numerical analysis on model data as well as brain imaging data sets.
The PhD student will be supervised by Dr. Joseph Lizier and Prof. Mikhail Prokopenko, joining Dr. Lizier’s ARC DECRA fellowship project “Relating function of complex networks to structure using information theory” within the Faculty of Engineering and Information Technologies at the University of Sydney.
|Lab||The University of Sydney|
|Keywords||complex networks; information theory; transfer entropy; information processing;|
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