Associate Professor Mark McDonnell
|Position:||Associate Research Professor|
|Division/Portfolio:||Division of Information Technology, Engineering and the Environment|
|School/Unit:||School of Information Technology and Mathematical Sciences|
|Group:||Inst for Telecomm Research|
|Campus:||Mawson Lakes Campus|
|Telephone:||+61 8 830 23341|
|Fax:||+61 8 830 23817|
|URL for Business Card:||http://people.unisa.edu.au/Mark.McDonnell|
Assoc Prof Mark D. McDonnell currently holds a five-year Australian Research Fellowship awarded in 2010 by the Australian Research Council (ARC) in the Discovery Projects scheme. He is a member of University of South Australia's Institute for Telecommunications Research (ITR).
Assoc Prof McDonnell is Principal Investigator in the Computational and Theoretical Neuroscience Laboratory.
The Laboratory's research is interdisciplinary, and overlaps electronic engineering and the biological sciences. We are motivated to answer fundamental scientific questions about information processing in neurobiology, that may lead to improved health, or inspire new technology.
Recent and past news
July 2014: The Conversation published my article "To understand the brain you need electronic engineers too."
June 2014: IEEE Life Sciences Newsletter features summaries of articles published in Proceedings of the IEEE (Vol. 102, No. 5) special issue on Engineering Intelligent Electronic Systems Based on Computational Neuroscience, edited by Mark McDonnell. See Overview.
May 2014: Guest Editor of the May 2014 issue of the Proceedings of the IEEE (Vol. 102, No. 5): a special issue on Engineering Intelligent Electronic Systems Based on Computational Neuroscience.
August 2013: Appointed to Editorial Board of PLoS One.
October 2012: John Bekkers and Mark McDonnell awarded a National Health and Medical Research Council (NHMRC) Project Grant: "Persistent firing in cortical interneurons: mechanisms and potential anticonvulsant role" for 2013-2015.
July 2012: Mark McDonnell delivered an invited Berstein Lecture at the Bernstein Center for Computational Neuroscience in Munich, Germany.
May 2012: Mark McDonnell is currently a Visiting Professor at University of British Colmbia, Vancouver (Professor Lawrence Ward's Cognitive Neuroscience Laboratory and the Brain Research Centre) until 30 December 2012.
December 2011: Awarded Senior Membership of the IEEE (Institute of Electrical and Electronics Engineers).
December 2011: Mark McDonnell is chair of the Biomimetic Sensors and Neuronal Information Processing Symposium within the IEEE ISSNIP conference in Adelaide.
November 2011: Mark McDonnell awarded an Endeavour Research Fellowship for a research visit to University of British Columbia, Vancouver, Canada, to work with Prof Lawrence Ward and the Brain Research Centre.
June 2011: Paper "The benefits of noise in neural systems: bridging theory and experiment" published in Nature Reviews Neuroscience.
February 2011: UNISA video profiling my work, with a focus on the benefits of holding an ARC funded research fellowship at UNISA: Click here.
August 2010: A generous top-up PhD scholarship (total value $37222 tax free p.a.) is available under Mark McDonnell's supervision, within the Computational and Theoretical Neuroscience Laboratory. Click here for details. Download selection criteria: Selection_Criteria_for_CTNL_Top-up.pdf
May 2010: McDonnell's research group is now known as the Computational and Theoretical Neuroscience Laboratory
October 2009: McDonnell awarded an ARC Australian Research Fellowship (ARF) in the Discovery Project scheme, 2010-2014.
August 2009: Media coverage of the "Two-envelopes" paper:
March 2009: McDonnell has been selected as a participant in UNISA's 2009-10 Research Leadership Development program.
October 2008: McDonnell's book published by Cambridge Univesity Press, "Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantisation."
May 2008: Article published in popular science magazine: Australasian Science. Noise May Be Music to Bionic Ears, by Mark McDonnell and Robert Morse: "Bionic ear implants could be improved by introducing noise to mimic biological unpredictability."
Academic Awards, Prizes and Fellowships
McDonnell received a PhD in Electrical & Electronic Engineering and Applied Mathematics (2006) (summa cum laude), from The University of Adelaide in Australia.
McDonnell commenced at UniSA in June 2007, after being awarded an APD Fellowship from the ARC (2007-2009). He has also been a postdoctoral researcher in the Centre for Biomedical Engineering (CBME) and a lecturer in the School of Electrical and Electronic Engineering at University of Adelaide (2005-2007).
|COMP 3022||Computer Science Topics for Software Engineers|
Senior Member IEEE (Institute of Electrical and Electronics Engineers)
Accredited Member of the Australian Mathematical Society (MAustMS)
Member, American Physical Society
Member ACoRN (ARC Communications Research Network)
Member COSNET (ARC Complex Open Systems Research Network)
Member SANI (South Australian Neuroscience Institute)
PhD, The University of Adelaide, 2006
B. Sc. (Hons, Applied Mathematics), The University of Adelaide, 2002
B. E. (Hons, Electrical and Electronic), The University of Adelaide, 1998
B. Sc. (Mathematical and Computer Sciences), The University of Adelaide, 1997
- Computational neuroscience (see Computational and Theoretical Neuroscience Laboratory)
- Machine learning: deep neural networks; recurrent neural networks; spiking neural networks; neuromorphic implementations
- Synchronous brain oscillations and their role in intra-brain communication, learning and neurobiological information processing
- Stochastic facilitation in neurobiology, e.g. suprathreshold stochastic resonance, short term synaptic plasticity.
- Connectomics: the study of complex network connectivity at multiple-scales in the brain (e.g. whole brain through to neuronal networks), and how functional networks relate to structural networks.
- Grand challenges for engineering: To reverse-engineer the brain.
- Information theory applied in computational neuroscience (e.g. stochastic pooling networks) and biomedical prosthetics (e.g. cochlear implants)
- The world's 23 toughest math questions: Challenge number 1: The Mathematics of the Brain
McDonnell M D, Stocks N G, Pearce C E M and Abbott D (2008) "Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantisation", Cambridge University Press, ISBN 978-0521882620.
1. Professor K. Alan Shore in Contemporary Physics Vol. 50, pp. 428–439, 2009.
2. Professor Miguel A.F. Sanjuan in Contemporary Physics Vol. 51, pp. 448-449, 2010.
PUBLISHED FULLY REFEREED JOURNAL PUBLICATIONS (Reverse chronological order):
42. P. E. Greenwood, M. D. McDonnell and L. M. Ward. Dynamics of gamma bursts in local field potentials. Neural Computation, 27:74-103, 2015.
41. M. D. McDonnell, M. D. Tissera, T. Vladusich, A. van Schaik and J. Tapson. Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the ‘extreme learning machine’ algorithm. PLOS One, 10: Article Number e0134254, 2015.
40. B. Zhou and M. D. McDonnell. Optimizing threshold levels for information transmission in binary threshold networks: independent multiplicative noise on each threshold. Physica A, 419:659-667, 2015.
39. M. D. Tissera and M. D. McDonnell. Deep extreme learning machines: Supervised autoencoding architecture for classification. Neurocomputing, Accepted (18 March 2015), In Press.
38. M. D. McDonnell, N. Iannella, M.-S. To, H. C. Tuckwell, J. Jost, B. S. Gutkin and L. M. Ward. A review of methods for identifying stochastic resonance in simulations of single neuron models. Network: Computation in Neural Systems, 26:35-71, 2015.
37. L. Xu, T. Vladusich, F. Duan, L. J. Gunn, D. Abbott and M. D. McDonnell. Decoding suprathreshold stochastic resonance with optimal weights. Physics Letters A, 379: 2277-2283, 2015.
36. T. Vladusich and M. D. McDonnell. A unified account of perceptual layering and surface appearance in terms of gamut relativity. PLOS One, 9: Article Number e113159, 2014.
35. M. D. McDonnell and X. Gao. M-ary suprathreshold stochastic resonance: Generalization and scaling beyond binary threshold nonlinearities. EPL (Europhysics Letters), 108: Article Number 60003, 2014.
34. B. Moezzi, N. Iannella and M. D. McDonnell. Modelling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics. Frontiers in Computational Neuroscience, 8: Article Number 163, 2014.
33. M. D. McDonnell, O. N. Yaveroglu, B. A. Schmerl, N. Iannella and L. M. Ward. Motif-role-fingerprints: the building-blocks of motifs, clustering-coefficients and transitivities in directed networks. PLOS One, 9: Article Number e114503, 2014.
32. Gao X, Grayden D B and McDonnell M D (2014). "Stochastic information transfer from cochlear implant electrodes to auditory nerve fibers." Physical Review E 90:022722.
31. McDonnell M D and Ward L M (2014). "Small modifications to network topology can induce stochastic bistable spiking dynamics in a balanced cortical model." PLoS One, 9:Article number e88254.
30. Schmerl B A and McDonnell M D (2013). "Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model." Physical Review E 88: 052722. Download a preprint from arxiv.
29. McDonnell M D, Li F, Amblard P O and Grant A J (2013). "Optimal sensor selection for noisy binary detection in stochastic pooling networks." Physical Review E 88:022118.
28. McDonnell M D, Mohan A and Stricker C (2013). "Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation." Frontiers in Computational Neuroscience 7:Article Number 58.
27. Mohan A, McDonnell M D and Stricker C (2013). "Interaction of short-term depression and firing dynamics in shaping single neuron encoding." Frontiers in Computational Neuroscience7:Article number 14.
26. Kostal L, Lansky P and McDonnell M D (2013). "Metabolic cost of neuronal information in an empirical stimulus-response model." Biological Cybernetics 107:355-365.
25. Prettejohn B J, Berryman M J and McDonnell M D (2013). "A model of the affects of authority on consensus formation in adaptive networks: impact on network topology and robustness." Physica A 392:857–868.
24. McDonnell M D, Mohan A, Stricker C and Ward L M (2012). "Input-rate modulation of gamma oscillations is sensitive to network topology, delays and short-term plasticity." Brain Research 1434:162-177.
23. McDonnell M D and Ward L M (2011). "The benefits of noise in neural systems: bridging theory and experiment." Nature Reviews Neuroscience 12:415-426.
22. McDonnell M D, Ikeda S and Manton J (2011). "An introductory review of information theory in the context of computational neuroscience." Biological Cybernetics 105:55–70.
21. Prettejohn B J, Berryman M J and McDonnell M D (2011). "Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientists." Frontiers in Computational Neuroscience 5:11 (pp 1-18).
20. McDonnell M D, Grant A J, Land I, Vellambi B N, Abbott D and Lever K (2011) "Gain from the two-envelope problem via information asymmetry: On the suboptimality of randomized switching." Proceedings of the Royal Society A 467:2825-2851.
19. McDonnell MD, Burkitt A N, Grayden D B, Meffin H and Alex J. Grant (2010). "A channel model for inferring the optimal number of electrodes for future cochlear implants," IEEE Transactions on Information Theory 56:928-940. DOI link.
18. McDonnell MD, Stocks NG and Amblard PO (2010). "Communication of uncoded sensor measurements through nanoscale binary-node stochastic pooling networks," Nano Communication Networks 1:209-223.
17. McDonnell MD, Amblard PO and Stocks NG (2010). "Bio-inspired communication:performance limits for information transmission and compression in stochastic pooling networks with binary quantizing nodes," Journal of Computational and Theoretical Nanoscience 7:876-883.
15. McDonnell M D and Abbott D (2009). "Randomized switching in the two-envelope problem," Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 465 pp 3309–3322, 2009 abstract link
Media coverage of the "Two-envelopes" paper:
14. Nikitin A P, Stocks N G, Morse R P and McDonnell M D (2009). “Neural Population Coding Is Optimized by Discrete Tuning Curves,” Physical Review Letters 103, 138101, doi link, Arxiv Preprint. This paper selected for inclusion in Virtual Journal of Biological Physics Research.
12. McDonnell M D and Abbott D(2009). “What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology,” PLoS Computational Biology 5, Art. No. e1000348 (2009) Full text link [Review Article]
11. McDonnell M D (2009). “Information capacity of stochastic pooling networks is achieved by discrete inputs,” Physical Review E 79, Art. No. 041107 (2009) doi link. This paper selected for inclusion in Virtual Journal of Nanoscale Science and Technology. This paper selected for inclusion in Virtual Journal of Biological Physics Research.
10. McDonnell M D, Amblard P O and Stocks N G (2009). “Stochastic Pooling Networks,” Journal of Statistical Mechanics: Theory and Experiment, Art. No. P01012 (2009) doi link
9. Stocks N G, Nikitin A P, McDonnell M D and Morse R P. (2009). "The role of stochasticity in an information-optimal neural population code". Journal of Physics: Conference Series, 197:012015, doi link to full text here.
8. McDonnell M D and Stocks N G (2008). “Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations,” Physical Review Letters 101, 058103, doi link, Arxiv Preprint. This paper selected for inclusion in Virtual Journal of Biological Physics Research.
7. McDonnell M D, Stocks N G and Abbott D (2007). “Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance,” Physical Review E 75, 061105, doi link, Arxiv Preprint
6. McDonnell M D, Stocks N G, Pearce C E M and Abbott D (2006). “Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance,” Physics Letters A 352, pp. 183-189.
5. McDonnell M D, Stocks N G, Pearce C E M and Abbott D (2005). “Quantization in the presence of large amplitude threshold noise,” Fluctuation and Noise Letters 5, pp. L457-L468.
4. McDonnell M D, Stocks N G, Pearce C E M and Abbott D (2003). “Stochastic resonance and data processing inequality,” IEE Electronics Letters 39, pp. 1287-1288.
3. McDonnell M D, Abbott D and Pearce C E M (2002). “An analysis of noise enhanced information transmission in an array of comparators,” Microelectronics Journal 33, pp. 1079-1089.
2. McDonnell M D, Abbott D and Pearce C E M (2002) “A Characterization of suprathreshold stochastic resonance in an array of comparators by correlation coefficient,” Fluctuation and Noise Letters 2, pp. L205-L220.
1. McDonnell M D, Possingham H P, Ball I R and Cousins E A (2002). “Mathematical Methods for Spatially Cohesive Reserve Design,” Environmental Modeling & Assessment 7, pp. 107-114.
McDonnell M D (2009). “Applying stochastic signal quantization theory to the robust digitization of noisy analog signals", In Applications of Nonlinear Dynamics: Model and Design of Complex Systems, Eds. V. In, P. Longhini and A. Palacios, Springer, ISBN 978-3540856313 (Webpage for book)
Numerous full-length refereed conference papers are listed here
I am able to provide media comment in the following areas of expertise:
Discipline: Computational neuroscience
- stochastic resonance
- information theory
|Organisation Name:||The Australian Association of Computational Neuroscientists and Neuromorphic Engineers|
|Type of Organisation:||Professional organisation|
|Level of involvement:||Founding Member|
|Organisation Name:||PLoS ONE|
|Level of involvement:||Editorial Board|
|Organisation Name:||Fluctuation and Noise Letters (FNL): An Interdisciplinary Scientific Journal on Random Processes in Physical, Biological and Technological Systems|
|Level of involvement:||Editorial Board|
|Organisation Name:||Institute of Electrical and Electronics Engineers (IEEE)|
|Section:||IEEE South Australia (SA) Section, Joint Communications and Signal Processing Chapter|
|Type of Organisation:||Professional organisation|
|Level of involvement:||Chapter Vice President|
|Comments:||(Chapter Secretary in 2008)|
|Organisation Name:||ARC Communications Research Network|
|Type of Organisation:||Professional organisation|
|Level of involvement:||Local Representative for UNISA Node|
projects page of the Computational and Theoretical Neuroscience Laboratory.
I am Principal Supervisor for the following PhD students: (see CTNL People):
- Daniel Padilla. Project: Bayesian models of neuronal information processing (Thesis submitted and under examination, April 2015)
- Siyi Wang. Project: Information theory for molecular communication (Thesis submitted and under examination, April 2015)
- Gao Xiao. Project: Information theoretic approaches to optimising cochlear implant electrode design (started Februrary 2012)
- Brett Schmerl. Project: Modelling the complex neuronal networks of the olfactory cortex (started March 2012)
- Bahar Moezzi. Project: modelling of stochastic spontaneous nerve fibre activity in the auditory brainstem (started July 2013)
- Migel Tissera. Project: Modelling and neuromorphic applications of stochastic memristors (started July 2013)
- Philip Crouch. Project: Spiking neuron implementation of hierarchical temporal memory model of cortical function (transferred to UniSA 2014)
- Mark McKenzie. Project: Reinforcement learning (Commenced March 2015).
I am on the supervision panel for the following PhD students:
- Nab Dahal. Project: augmented cognition (Thesis submitted and under examination, April 2015)
I was principal supervisor of the following completed PhD students:
Change | Staff home page help