Computational neuroscience
The development of mathematical models and computational analyses of the neural systems.
Computational Neuroscience complements experimental neuroscience, by helping to integrate, and provide a deeper analysis of, different experimental results. For example, it is through mathematical modeling that we can better understand how learning takes place in different parts of the brain.
Research goals
Our research goals are to develop this understanding using a mathematical framework.
Our current research projects include modelling and analysing:
- The dynamic behaviour of the brain
- Learning in the brain
- Particular circuits within the brain.
Projects
Brain mapping with MRI: brain atlases with multiple topographic features
Team: Andrew Zalesky
Computational neuroscience: Simulating brain dynamics and generative modelling of brain networks
Team: Andrew Zalesky, Dr Caio Seguin
Network communication in the brain
Team: Andrew Zalesky, Dr Caio Seguin
Structure-function coupling in the connectome
Team: Andrew Zalesky, Dr Caio Seguin
Central representation of electroacoustic stimuli: modelling hearing with electrical and acoustic hearing
Team: David Grayden
Creating subject-specific mathematical models to understand the brain
Team: David Grayden, Philippa Karoly, Mark Cook
Advanced epileptic seizure warning methods
Team: David Grayden, Anthony Burkitt, Mark Cook
Understanding cortical processing: Neuronal activity and learning in recurrently connected networks
Team: David Grayden, Anthony Burkitt