Biological modelling and biomedical artificial intelligence

Graphic of a molecule coming out of a computer screen. Credit: Mike Agliolo

We study neural information processing using computer simulations and experiments. We model and analyse experimental data in a variety of neuroscience fields:

  • Visual
  • Auditory
  • Central and sympathetic nervous systems.

Experimental validation is important to understand and discover biological phenomena and mechanisms. However, in many cases a modelling approach has comparative advantages:

  • The ability to precisely control otherwise confounding effects
  • The relative ease with which underlying mechanisms can be analysed.

We use mathematical modelling and computer simulation to uncover the mystery of biological systems, fitting mathematical models to published experimental data and data collected at Swinburne and in the labs of our collaborators.

We use deep learning biologically-inspired algorithms to reformulate existing biologically-inspired robotic visual control systems.  We apply machine learning and artificial intelligence methods to make sense of biological data.  

We capitalise on the high performance computing facilities at Swinburne University, running computationally intensive simulations.

Our work leads to advances in knowledge in basic neuroscience and has the potential to improve the quality of life of people with neurological disorders.