Brain Network Structure and Dynamics


This research area is concerned with developing better understanding of the structure of the brain, especially through modeling the dynamics and interactions of its neuronal networks, and in applying the results to understand the brain networks that have developed and evolved, and how they connect and reconnect to process information.


Professor Peter Robinson

Research Location

School of Physics

Program Type



Various levels of the brain are interconnected in what appears to be a hierarchical manner, with tightly interconnected local regions more loosely connected to one another to form larger assemblies, which are more loosely connected still. Some key open questions include: (a) Why does the brain have this structure? (b) How does physics constrain the types of network that can exist, and could evolution have selected something different? (c) How do brain regions dynamically connect and reconnect to perform ever-changing information processing tasks? (d) How can we measure and quantify the structure and dynamics of networks in real brains?
We have incorporated simple but realistic models of physiology into a variety of new, more realistic, network structures to address these questions. Our models enable the key requirement of network stability to be incorporated, and we have already found that this greatly constrains possible network structures and modes of activity, and hence the types of brain that could be selected for by evolution. The physiological basis of the model enables it to predict experimental observables such as electroencephalographic or functional magnetic resonance imaging (fMRI) measurements. New analysis and characterization methods are also being explored, based on field theory and matrix analysis. Numerous areas exist for PhD, MSc, or Honors projects, which could include theoretical, computational, and/or data-related components in cooperation with our international and local collaborators.

Specific projects lie in areas including:
1) Analysis of the physical constraints imposed by network stability, geometry, and dimensionality on brain dynamics and structure.
2) Tests of model predictions against international brain-structure and functional imaging databases (we have excellent contacts in this area).
3) Study of the relationship between discrete network modeling and neural field approaches to brain dynamics, the latter being an area of particular expertise of the group.
4) Incorporation of spiking-neuron effects into hierarchical network dynamics.
5) New methods for imaging and image analysis to determine brain network structure noninvasively from activity.
6) Determining how the visual system maps multidimensional feature characteristics (intensity, color, edge orientation, velocity, etc.) to the two-dimensional cortical surface.
7) Model network development and evolution in visual cortex and more generally, including the effects of natural and induced plasticity.

Additional Information

Our approach is to formulate an overall project topic in close consultation with the prospective student, and to allow the approach and details to evolve with increasing student input as the candidature develops. Excellent facilities are available to carry out all aspects of the work, including access to computing resources and network data, especially via CIBF collaborators. Because of the highly interdisciplinary nature of the spectrum of projects, students from a wide variety of backgrounds will be able to find suitable projects in this area, with emphases ranging from highly theoretical to highly applied/clinical in nature. Successful existing and past students have had backgrounds in Physics, Medicine, Engineering, IT, Psychology, Mathematics, Physiology, and other disciplines. Top-up funding may be available for students of University Medal standard, or equivalent. Travel support to present research results at national and international conferences is also available.

HDR Inherent Requirements

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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biological physics, biomedical physics, biomathematics, brain dynamics, nonlinear dynamics, networks, neural networks, neuronal networks, brain structure, brain connectivity, brain mapping, functional connectivity, computational neuroscience, fMRI, neuroimaging, complex systems, theoretical physics, physics, Medical physics, Neuroscience, Biophysics, Applied mathematics, Self-Organization.

Opportunity ID

The opportunity ID for this research opportunity is: 674

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