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Creating subject-specific mathematical models to understand the brain

Primary supervisor

Levin Kuhlmann


  • David B. Grayden
  • Philippa Karoly
  • Mark J. Cook

The brain is a complex machine and brain function remains yet to be fully understood. This project works at the intersection of dynamical modelling, statistical signal processing, statistical inference and machine learning to develop subject specific mathematical models of the brain that can be used to infer brain states and monitor and image the brain. This work is centred around a  neurophysiological variable estimation framework we have been developing that can be applied to all kinds of brain activity recordings.

There are two projects to be completed under this program and we are looking for a different student for each of these projects.

Project 1: specifically extend our neurophysiological variable estimation framework to different classes of neural population models validated from microscopic data.

Project 2: specifically extend our neurophysiological estimation framework to coupled networks of neural population models applied to in vivo human recordings.

These projects are critical for applications of our estimation framework to all kinds of neuroscientific studies (e.g. of rest, sensation, cognition, movement or abnormal behaviour) where different models and networks are more appropriate and parsimonious for different kinds of situations and will open new ways to understanding brain function.

Required knowledge

Any or all of the following: dynamical modelling, machine learning, statistical signal processing, statistical inference, and some understanding of brain function

Project funding

Project based scholarship

Learn more about minimum entry requirements.