Dr Mark McDonnell |
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| Position: | Senior Research Fellow |
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| Division/Portfolio: | Research and Innovation Portfolio | |
| School/Unit: | Institute For Telecommunications Research | |
| Campus: | Mawson Lakes Campus | |
| Office: | W1-19A | |
| Telephone: | +61 8 830 23341 | |
| Fax: | +61 8 830 23817 | |
| Email: | Mark_dot_McDonnell_at_unisa_dot_edu_dot_au | |
| URL for Business Card: | http://people.unisa.edu.au/Mark.McDonnell | |
Dr Mark McDonnell is a Senior Research Fellow at the university's Institute for Telecommunications Research (ITR). He joined ITR in June 2007, after being awarded an Australian Research Council (ARC) APD Fellowship, for his project, "A lossy compression paradigm for sensory neural coding." Prior to this, he was a postdoctoral researcher in the Centre for Biomedical Engineering (CBME) and a lecturer in the School of Electrical and Electronic Engineering at The University of Adelaide.
Recent News
October 2009: McDonnell awarded an ARC Australian Research Fellowship (ARF) in the Discovery Project scheme, 2010-2014.
March 2009: Mark has been selected as a participant in UNISA's 2009-10 Research Leadership Development program.
Academic Awards, Prizes and Fellowships
Dr McDonnell received a PhD in Electrical & Electronic Engineering and Applied Mathematics (2006) (summa cum laude), from The University of Adelaide in Australia.
Mark's research is interdisciplinary, and overlaps electronic engineering and the biological sciences. I am motivated to answer fundamental scientific questions that may lead to improved health, or inspire new technology. This includes:
1. Computational Neuroscience- the use of the tools of mathematics, engineering and computer science to assist our understanding of the aspects of the brain and nervous system, e.g. biophysics, physiology and information processing.
2. Bio-inspired Engineering and biomedical prosthetics- applying knowledge gained from scientific studies of biology to create useful improved technology, and prosthetics such as cochlear implants.
Some inspiring links:
Grand challenges for engineering. Reverse-engineer the brain
The world's 23 toughest math questions. Challenge number 1: The Mathematics of the Brain
Mark McDonnell was a 2007 Australian Fresh Scientist. Details on my story at the Fresh Science webpage.
May 2008 article in 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."
2007 UNISA media release on my research
2007 article in The Researcher
Media coverage of the "Two-envelopes" paper:
My ResearcherID page (links to ISI publications database)
Professional associations
Australian Research Council (ARC) INTREADER (Expert Assessor of International Standing)
Member IEEE (Institute of Electrical and Electronics Engineers)
Accredited Member of the Australian Mathematical Society (MAustMS)
Junior Member, American Physical Society
Member ACoRN (ARC Communications Research Network)
Member COSNET (ARC Complex Open Systems Research Network)
Member SANI (South Australian Neuroscience Institute)
Member SPIE (International Society for Optical Engineering)
Qualifications
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
Research interests
- Computational neuroscience
- Synchronous brain oscillations and their role in communication, memory and processing
- Information theory applied to (1) biomedical prosthetics (e.g. cochlear implants) and (2) sensor networks
- Signal processing for sensor networks such as stochastic pooling networks
- Complex network theory: statistical physics approaches, emergent phenomena, nonequilibrium systems
- Suprathreshold stochastic resonance
- Novel architectures for analog-to-digital converter circuits
Research publications
BOOKS:
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.
Publisher's webpage for the book
Book review in Contemporary Physics Vol. 50, No. 2, March–April 2009, 428–439.
BOOK CHAPTERS:
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)
McDonnell M D, Stocks N G, Pearce C E M and Abbott D (2009). “Models of information transfer through neural populations with suprathreshold stochastic resonance,” Book chapter in Emerging Brain-Inspired Nano-Architectures, Eds. V. Beiu & U. Rueckert, Imperial College Press (accepted 14 Jun. 2005, In Press)
JOURNAL PUBLICATIONS:
18. McDonnell MD, Burkitt A N, Grayden D B, Meffin H and Alex J. Grant (2009). "A channel model for inferring the optimal number of electrodes for future cochlear implants," IEEE Transactions on Information Theory (Accepted 7 Oct 2009).
17. McDonnell MD, FlitneyAP (2009). "Signal acquisition via polarization modulation in single photon sources," Physical Review E 80:060102(R) 2009. doi link, Arxiv Preprint.
16. 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:
15. 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.
14. McDonnell M D and Stocks N (2009) Suprathreshold stochastic resonance. Scholarpedia, 4(6):6508 [Review Article]
13. 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]
12. 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.
11. 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
10. McDonnell M D, Amblard P O and Stocks N G (2009). "Bio-inspired communication: performance limits for information transmission and compression in stochastic pooling networks with binary quantizing nodes. Journal of Computational and Theoretical Nanoscience, (Accepted 8 Jan 2009, In Press).
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.
CONFERENCE PUBLICATIONS:
Numerous papers are listed on my personal webpage
Expertise for Media Contact
I am able to provide media comment in the following areas of expertise:
Discipline: Computational neuroscience
- stochastic resonance
- information theory
Community Service
| 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 |
| Organisation URL: | http://ewh.ieee.org/r10/s_australia/ |
| Level of involvement: | Chapter Vice President |
| Year from: | 2008 |
| Year to: | 2009 |
| Comments: | (Chapter Secretary in 2008) |
| Organisation Name: | ARC Communications Research Network |
| Type of Organisation: | Professional organisation |
| Organisation URL: | http://www.acorn.net.au/ |
| Level of involvement: | Local Representative for UNISA Node |
| Year from: | 2008 |
| Year to: | 2009 |
| Organisation Name: | Fluctuation and Noise Letters (FNL): An Interdisciplinary Scientific Journal on Random Processes in Physical, Biological and Technological Systems |
| Organisation URL: | http://www.worldscinet.com/fnl/ |
| Level of involvement: | Editorial Board |
| Year from: | 2009 |
Research Degree Supervisor
Dr Mark McDonnell undertakes interdisciplinary research that overlaps electronic engineering, mathematics and biological science.If you have a background in electronic engineering, mathematics, physics, computer science or physiology and are considering postgraduate research on an exciting, stimulating, and incredibly diverse topic, then I want to talk to you today!
Current students:
My research interests include:
If you are considering either postgraduate or honours research in any of these areas I would love to hear from you!
I will happily supervise either a mathematical, computational or hardware focused project, provided you are enthusiastic, capable and self-motivated.
Please contact me via the above listed email address.
Examples of research projects on offer include:
Current Projects:
Computational Neuroscience: Using Information Theory To Understand The Brain
Computational neuroscience is the collective name for scientific research into understanding the brain that employs the same tools as mathematicians, physicists, computer scientists and electronic engineers. Our brains operate very differently to digital electronic computers. One of the goals of computational neuroscience is to understand the physical mechanisms biology uses to acquire, process, communicate and compute information. There are several levels that can be studied including the chemistry that occurs within a single neuron (brain cell), the overall electrical activity of a single neuron, and the behaviour of groups (populations) of neurons. I am particularly interested in sensory neurons, that is, the neurons involved in acquiring information about our environment and that allow our senses to work. One way to better understand our senses and neural coding is to model and analyze, using information theory, the features that are also present in artificial communication or sensor networks. Some of the unsolved questions in this area include |
| Recent mathematical modelling has helped us to understand the mechanisms that lead to oscillations in the activity of large networks of neurons in the brain. This project will build on these models to assess the hypothesis that brain oscillations allows communication of information between different brain regions, and links between such oscillations and memory formation. |
I would happily assist a self-motivated student to undertake a project in complex systems modelling on topics such as synchronization phenomena, small world networks and emergence of unexpected behaviour from simple rules. The particular focus can be anything, but I am especially interested in brain function, ecology, biological synchronization (see the wonderful book, Sync, by Steven Strogatz) and social interactions. |
Advances in digital signal processing have led to renewed efforts to "move the ADC closer to the antenna" in communications and sensor systems. The ultimate goal is to directly digitise at the antenna, so that no analog components are required, and all amplification, filtering and demodulation is instead achieved in software or firmware. The key to meeting this challenging goal is substantial improvements in the speed, resolution and dynamic range of analog-to-digital converters. This may require completely new types of ADC architectures and device technology, such as making use of large numbers of extremely fast, but unreliable and noisy parallel switching devices -- whether transistors, or novel technology like spintronics -- and to utilise redundancy to achieve reliable digitisation. Biological sensory neurons may utilise this principle, and one of my interests is to explore this possibility. This project would be suitable for a student with a strong background in any of the following: electronic engineering, probability theory, information theory, biological mathematics, biophysics or computational neuroscience. |
A Stochastic Pooling Networks is a model that is used to understand complex nonlinear interactions between random noise, redundancy and compression. They are very versatile and can be used, for example, in the context of biological neural populations, electronic circuits and sensor networks. There are many interesting and fun possibilities for research in this area for potential students with backgrounds in any of the following areas: mathematics (pure or applied), numerical computing, information theory, electronic engineering or physics. |
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