Computational, Systems and Developmental Neuroscience

Geoffrey J. Goodhill, Ph.D.

Queensland Brain Institute & School of Mathematics and Physics
University of Queensland

We are interested in how brains process information, particularly during development. This includes how growing nerve fibres use molecular cues to make guidance decisions, how map-like representations of visual inputs form in the optic tectum and visual cortex, and how these maps code sensory information. We are addressing these questions using a combination of experimental, mathematical and computational techniques. Members of the lab come from diverse backgrounds including biology, mathematics, physics and computer science.

Full publication list

YouTube video of recent talk (2014 INCF Congress)

YouTube video: What has Maths got to do with the Brain?

2015 QBI Short Course in Mathematical and Computational Neuroscience

Interested in doing a PhD?

Some projects

Decision-making during axon guidance

small.jpg Growing nerve fibres (axons) must often navigate long distances to find their appropriate targets in the developing brain. They do this by sensing a variety of molecular cues in their environment. But how is this noisy sensory information interpreted to decide in which direction to grow? We are developing Bayesian and information-theoretic models to understand optimal decision-making in this context, particularly in the context of following a chemical gradient (chemotaxis), and how intracellular signalling pathways transduce these decisions. We are also interested in understanding the complex and dynamic morphology of the tips of growing nerve fibres (growth cones), and the role this plays in guidance. To help answer these questions we are developing new experimental techniques for studying axon guidance, most recently involving microfluidic technologies.

Some recent publications
Goodhill, G.J. (2013). Axonal growth and guidance. Scholarpedia, 8(10):1663. Link to article
Sutherland, D.J. & Goodhill, G.J. (2013). The interdependent roles of calcium and cAMP in axon guidance. Developmental Neurobiology, published online Nov 29th 2013. PDF
Yuan, J., Chan, S., Mortimer, D., Nguyen, H. & Goodhill, G.J. (2013). Optimality and saturation in axonal chemotaxis. Neural Computation, 25, 833-853 (2013). PDF
Forbes, E.M., Thompson, A.W., Yuan, J, & Goodhill, G.J. (2012). Calcium and cAMP levels interact to determine attraction versus repulsion in axon guidance. Neuron, 74, 490-503. PDF SI
Forbes, E.M., Hunt, J.J. & Goodhill, G.J. (2011). The combinatorics of neurite self-avoidance. Neural Computation, 23, 2746-2769. PDF
Thompson, A.W., Pujic, Z., Richards, L.J. & Goodhill, G.J. (2011). Cyclic nucleotide-dependent switching of mammalian axon guidance depends on gradient steepness. Molecular and Cellular Neuroscience, 47, 45-52. PDF
Mortimer, D., Dayan, P., Burrage, K. & Goodhill, G.J. (2011). Bayes-optimal chemotaxis. Neural Computation, 23, 336-373. PDF
Mortimer D, Pujic Z, Vaughan T, Thompson AW, Feldner J, Vetter I, Pujic Z, & Goodhill GJ (2010). Axon guidance by growth rate modulation. Proc. Natl. Acad. Sci. USA, 107, 5202-5207. PDF SI
Mortimer, D., Dayan, P., Burrage, K. & Goodhill, G.J. (2010). Optimizing chemotaxis by measuring unbound-bound transitions. Physica D, 239, 477-484. PDF F1000 review
Mortimer D, Feldner J, Vaughan T, Vetter I, Pujic Z, Rosoff WJ, Burrage K, Dayan P, Richards LJ, Goodhill GJ (2009). A Bayesian model predicts the response of axons to molecular gradients. Proc. Natl. Acad. Sci. USA, 106, 10296-10301. PDF SI
Mortimer, D., Fothergill, T., Pujic, Z., Richards, L.J. & Goodhill, G.J. (2008). Growth Cone Chemotaxis. Trends in Neurosciences, 31, 90-98. PDF
Xu, J., Rosoff, W.J., Urbach, J,S. & Goodhill, G.J. (2005). Adaptation is not required to explain the long-term response of axons to molecular gradients. Development, 132, 4545-4552. PDF
Rosoff, W.J., Urbach, J.S., Esrick, M., McAllister, R.G. Richards, L.J. & Goodhill, G.J. (2004). A new chemotaxis assay shows the extreme sensitivity of axons to molecular gradients. Nature Neuroscience, 7, 678-682. PDF News and Views F1000 reviews

Feature maps in mammalian visual cortex

ormap.gif How is information about the world represented in the brain, and how do these representations adapt as the statistical structure of the world changes? A particularly attractive system in which to investigate these questions is the visual system. The visual cortex of mammals contains overlaid maps of visual features such as orientation and ocular dominance. We are studying how the properties of these maps adapt in response to abnormal visual input. Previously we have shown how mathematical models can reproduce a wide variety of experimental data in this regard. Currently we are using data from experimental collaborators to develop new statistical methods for characterising map structure and its plasticity.

Some recent publications
Hughes, N.J., Hunt, J.J., Cloherty, S.L., Ibbotson, M.R., Sengpiel, F. & Goodhill, G.J. (2014). Stripe-rearing changes multiple aspects of the structure of primary visual cortex. Neuroimage, in press.
Hunt, J.J., Dayan, P. & Goodhill, G.J. (2013). Sparse coding can predict primary visual cortex receptive field changes induced by abnormal visual input. PLoS Computational Biology, 9, e1003005. PDF
Hunt, J.J., Ibbotson, M.R. & Goodhill, G.J. (2012). Sparse coding on the spot: spontaneous retinal waves suffice for orientation selectivity. Neural Computation, 24, 2422-2433. PDF
Hunt, J.J., Bosking, W.H. & Goodhill, G.J. (2011). Statistical structure of lateral connections in the primary visual cortex. Neural Systems & Circuits, 1:3. PDF
Giacomantonio, C.E., Ibbotson, M.R. & Goodhill, G.J. (2010). The influence of restricted orientation on map structure in primary visual cortex. Neuroimage, 52, 875-883. PDF
Hunt, J.J., Giacomantonio, C.E., Tang, H., Mortimer, D., Jaffer, S.,Vorobyov, V., Ericksson, G., Sengpiel, F. & Goodhill, G.J. (2009). Natural scene statistics and the structure of orientation maps in the visual cortex. Neuroimage, 47, 157-172. PDF
Giacomantonio, C.E. & Goodhill, G.J. (2007). The effect of angioscotomas on map structure in primary visual cortex. Journal of Neuroscience, 27, 4935-4946. PDF
Goodhill, G.J. (2007). Contributions of theoretical modelling to the understanding of neural map development. Neuron, 56, 301-311. PDF

Axon guidance and information coding in the optic tectum

small_head.jpg Map-like patterns of neural wiring are common throughout the nervous system. How do they develop, and how do they function? The zebrafish optic tectum offers a very convenient model system to study these questions. We are studying how axons from the retina find their appropriate targets in the tectum, and how spatial information about the world is decoded from population activity in the tectal map.

Some recent publications
Simpson, H.D., Kita, E.M., Scott, E.K. & Goodhill, G.J. (2013). A quantitative analysis of branching, growth cone turning and directed growth in zebrafish retinotectal axon guidance. Journal of Comparative Neurology, 521, 1409-1429. PDF
Simpson, H.D. & Goodhill, G.J. (2011). A simple model can unify a broad range of phenomena in retinotectal map development. Biological Cybernetics, 104, 9-29. PDF F1000 review
Goodhill, G.J. & Xu, J. (2005). The development of retinotectal maps: a review of models based on molecular gradients. Network, 16, 5-34. PDF


Google Scholar profile

Lab Personnel

Collaborators

Peter Dayan

Michael Ibbotson

Linda Richards

Ethan Scott

Frank Sengpiel


Contact us

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Geoffrey J. Goodhill
Queensland Brain Institute
University of Queensland
St Lucia
QLD 4072
AUSTRALIA