Research Supervisor Connect
The research will involve computational analysis in complex systems, complex networks, information theory, dynamical systems and computational neuroscience. The student will be exploring applications of, and/or updates to algorithms for, inferring brain network models to represent information flow relationships between brain regions, based on time-series neural recordings (such as fMRI, EEG, MEG, etc). The PhD will be supervised by A/Prof. Joseph Lizier. The applicant will join A/Prof. Lizier’s Information Dynamics team in the Modelling and Simulation group, which studies complex systems and networks at The School of Computer Science. The student will collaborate with A/Prof. Mac Shine (Brain and Mind Centre) and Dr. Ben Fulcher (Physics) and their teams as part of their Systems Neuroscience and Complexity collaboration, within the University’s Centre for Complex Systems.
Associate Professor Joseph Lizier.
Billions of years of evolution have shaped brain structure and function to solve complex problems, likely by shaping information-flow around the trillions of connections that comprise the human brain. We now have access to neural recordings of unprecedented quality and resolution, but we still do not know how distributed whole-brain neural activity patterns give rise to human cognition. Network neuroscience frames cognitive functions as emergent properties of the distributed and dynamic interactions between regions across the brain, seeking to create brain network models from high quality data. Yet the measurements used to model brain networks from time-series recordings have thus far mostly focussed on symmetric correlation-based functional networks.
This project will measure directed, multivariate and nonlinear information flows across the brain to establish network models that more wholistically map cognitive information processing directly from functional neuroimaging data. The project will utilise our JIDT (https://github.com/jlizier/jidt) and IDTxl (https://github.com/pwollstadt/IDTxl) open-source toolkits, implementing the information-theoretic measure transfer entropy and its variants to characterise information flow between time-series. (Further reading is available regarding the algorithms we use for directed functional and effective network inference in doi:10.1162/netn_a_00092 and doi:10.1162/netn_a_00178). Multiple project possibilities are available, analysing various open data sets, including neural time-series recordings such as fMRI, potentially including both resting state and various task recordings, and in human, mouse, etc. There is also the potential to explore improvements to numerical estimators and algorithms for network inference.
Applicants need to satisfy the eligibility criteria for PhD enrolment at The University of Sydney (e.g. First-class honours equivalent results are essential).
Successful candidates will have:
How to apply:
To apply, please email joseph.lizier@sydney.edu.au the following:
The opportunity ID for this research opportunity is 3421