A/Prof. Mark McDonnell is Principal Investigator of the Computational and Theoretical Neuroscience Laboratory.
For information on my lab's research see http://ctnl.unisa.edu.au
For my Google Scholar page see here
A/Prof. McDonnell's research expertise encompasses computational and mathematical modeling of noise and random variability in nonlinear and complex systems. He has successfully applied the results of this fundamental science to applications in neuroscience, biomedical engineering and machine learning. McDonnell has published over 80 refereed papers in these areas, including several review articles, a book published by Cambridge University Press and a patent. McDonnell has been received two... Read more
Current Machine Learning Research:
Current Computational and Theoretical Neuroscience Research:
A/Prof McDonnell is seeking PhD research students for 2017 in:
About me
A/Prof. Mark McDonnell is Principal Investigator of the Computational and Theoretical Neuroscience Laboratory.
For information on my lab's research see http://ctnl.unisa.edu.au
For my Google Scholar page see here
A/Prof. McDonnell's research expertise encompasses computational and mathematical modeling of noise and random variability in nonlinear and complex systems. He has successfully applied the results of this fundamental science to applications in neuroscience, biomedical engineering and machine learning. McDonnell has published over 80 refereed papers in these areas, including several review articles, a book published by Cambridge University Press and a patent. McDonnell has been received two prestigious research fellowships awarded by the Australian Research Council, the South Australian Tall Poppy of Science award, and an Endeavour Award held as Visiting Professor at University of British Columbia, Canada. He has been funded by the Australian Research Council, the National Health and Medical Research Council, and the Defense Science and Technology Group, and he has supervised 10 PhD students, with three graduations to date.
About me
About me
Doctor of Philosophy The University of Adelaide
Bachelor of Science (Applied Mathematics) (Honours) The University of Adelaide
Bachelor of Engineering (Electrical and Electronic) The University of Adelaide
Research
Excludes commercialinconfidence projects.
Enhancing Speech Recognition Performance in KALDI using Deep Recurrent Neural Networks, Defence Science & Technology Organisation  Business & Commercialisation Office, 11/02/2016  01/06/2017
Multiobject visual detection, tracking and classification algorithms, Defence Science & Technology Organisation  Business & Commercialisation Office, 15/02/2013  31/12/2016
Persistent firing in cortical interneurons: mechanisms and potential anticonvulsant role, NHMRC  Project Grant, 01/11/2013  31/10/2016
Communication and information storage mechanisms in complex dynamical brain networks, ARC  Discovery Projects, 01/06/2010  15/02/2016
Fellowship Support  Communication and information storage mechanisms in complex dynamical brain networks, UniSA  ResearchSA Fellowship, 01/01/2010  31/07/2015
Research
Research since 2008 is shown below. To see earlier years visit ORCID, ResearcherID or Scopus
Year  Output 

2008 
McDonnell, MD, Stocks, N, Pearce, C & Abbott, D 2008, Stochastic Resonance: From suprathreshold stochastic resonance to stochastic signal quantization, Cambridge University Press, US. 
Year  Output 

2014 
McDonnell, MD 2014, 'Distributed Bandpass Filtering and Signal Demodulation in cortical Network Models', in V In, A Palacios & P Longhini, International Conference on Theory and Application in Nonlinear Dynamics (ICAND 2012), Springer, US, pp. 155166. 
2009 
McDonnell, MD 2009, 'Applying Stochastic Signal Quantization Theory to the Robust Digitization of Noisy Analog Signals', in Visarath In, Patrick Longhini & Antonio Palacios, Applications of Nonlinear Dynamics: Model and Design of Complex Systems, SpringerVerlag, Berlin, pp. 249261.
1

Year  Output 

2016 
Gunn, LJ, ChapeauBlondeau, F, McDonnell, MD, Davis, BR, Allison, A & Abbott, D 2016, 'Too good to be true: when overwhelming evidence fails to convince', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 472, no. 2187, article no. 20150748.
24

2016 
McDonnell, MD, Goldwyn, JH & Lindner, B 2016, 'Editorial: Neuronal stochastic variability: influences on spiking dynamics and network activity', Frontiers in Computational Neuroscience, v. 10, article no. 38, pp. 13. 
2016 
Tissera, MD & McDonnell, MD 2016, 'Deep extreme learning machines: supervised autoencoding architecture for classification', Neurocomputing, v. 174, pt. A, pp. 42¿49. 
2016 
Xu, L, Duan, F, Abbott, D & McDonnell, MD 2016, 'Optimal weighted suprathreshold stochastic resonance with multigroup saturating sensors', Physica A: Statistical Mechanics and its Applications, pp. 457, pp. 348355. 
2015 
Greenwood, PE, McDonnell, MD & Ward, LM 2015, 'Dynamics of gamma bursts in local field potentials', Neural Computation, v. 27, no. 1, pp. 74103.
3

2015 
McDonnell, MD, Iannella, N, To, MS, Tuckwell, HC, Jost, J, Gutkin, BS & Ward, LM 2015, 'A review of methods for identifying stochastic resonance in simulations of single neuron models', Network: computation in neural systems, v. 26, no. 2, pp.3571.
1

2015 
McDonnell, MD, Tissera, MD, Vladusich, T, van Schaik, A & Tapson, J 2015, 'Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm', PLoS One, v. 10, no. 8, article no. e0134254.
1
1

2015 
Xu, L, Vladusich, T, Duan, F, Gunn, LJ, Abbott, D & McDonnell, MD 2015, 'Decoding suprathreshold stochastic resonance with optimal weights', Physics Letters A: General Physics, Nonlinear Science, Statistical Physics, Atomic, Molecular and Cluster Physics, Plasma and Fluid Physics, Condensed Matter, Crossdisciplinary Physics, Biological Physics, Nanosciences, Quantum Physics, v. 379, no. 38, pp. 22772283. 
2015 
Zhou, B & McDonnell, M D 2015, 'Optimising threshold levels for information transmission in binary threshold networks: Independent multiplicative noise on each threshold', Physica A: Statistical Mechanics and its Applications, v. 419, pp. 659667.
1

2014 
Gao, X, Grayden, DB & McDonnell, MD 2014, 'Stochastic information transfer from cochlear implant electrodes to auditory nerve fibers', Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, v. 90, pp. 112.
3

2014 
McDonnell, MD & Gao, X 2014, 'Mary suprathreshold stochastic resonance: Generalization and scaling beyond binary threshold nonlinearities', Europhysics Letters: a letters journal exploring the frontiers of physics, v. 108, pp. 17. 
2014 
McDonnell, MD & Ward, LM 2014, 'Small modifications to network topology can induce stochastic bistable spiking dynamics in a balanced cortical model', PLoS One, v. 9 no. 4, pp. 121.
4

2014 
McDonnell, MD, Yaveroglu, ON, Schmerl, BA, Iannella, N & Ward, LM 2014, 'Motifrolefingerprints : the buildingblocks of motifs, clusteringcoefficients and transitivities in directed networks', PLoS One, v. 9, no. 12.
3

2014 
Moezzi, B, Iannella, N & McDonnell, MD 2014, 'Modeling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics', Frontiers in Computational Neuroscience, v. 8, article no. 163.
1

2014 
Vladusich, T & McDonnell, MD 2014, 'A unified account of perceptual layering and surface appearance in terms of gamut relativity', PLoS One, v. 9, no. 11, pp. 115. 
2013 
Kostal, L, Lansky, P & McDonnell, MD 2013, 'Metabolic cost of neuronal information in an empirical stimulusresponse model', Biological Cybernetics: communication and control in organisms and automata, v. 107, no. 3, pp. 355365.
9
1

2013 
McDonnell, MD, Li, F, Amblard, PO & Grant, AJ 2013, 'Optimal sensor selection for noisy binary detection in stochastic pooling networks', Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, v. 88, no. 2.
2
1

2013 
McDonnell, MD, Mohan, A & Stricker, C 2013, 'Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of shortterm synaptic depression and facilitation', Frontiers in Computational Neuroscience, v. 7, pp. 114.
1

2013 
Mohan, A, McDonnell, MD & Stricker, C 2013, 'Interaction of shortterm depression and firing dynamics in shaping single neuron encoding', Frontiers in Computational Neuroscience, v. 7, pp. 114.
1

2013 
Prettejohn, BJ, Berryman, MJ & McDonnell, MD 2013, 'A model of the effects of authority on consensus formation in adaptive networks : impact on network topology and robustness', Physica A: Statistical Mechanics and its Applications, v. 392, no. 4, pp. 857868.
1

2013 
Schmerl, BA & McDonnell, MD 2013, 'Channelnoiseinduced stochastic facilitation in an auditory brainstem neuron model', Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, v. 88, article no. 052722.
5
1

2012 
McDonnell, MD, Mohan, A, Stricker, C & Ward, LM 2012, 'Inputrate modulation of gamma oscillations is sensitive to network topology, delays and shortterm plasticity', Brain Research, v. 1434, pp. 162177.
6

2011 
McDonnell, MD & Ward, LM 2011, 'The benefits of noise in neural systems: bridging theory and experiment', Nature Reviews Neuroscience, v. 12, no. 7, pp. 415425.
145
23

2011 
McDonnell, MD 2011, 'Is electrical noise useful?', Proceedings of the Institute of Electrical and Electronics Engineers (IEEE), v. 99, no. 2, pp. 242246.
19

2011 
McDonnell, MD, Grant, AJ, Land, I, Vellambi, BN, Abbott, D & Lever, K 2011, 'Gain from the twoenvelope problem via information asymmetry: on the suboptimality of randomized switching', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 467, no. 2134, pp. 28252851.
3
3

2011 
McDonnell, MD, Ikeda, S & Manton, JH 2011, 'An introductory review of information theory in the context of computational neuroscience', Biological Cybernetics: communication and control in organisms and automata, v. 105, no. 1, pp. 5570.
7

2011 
Prettejohn, BJ, Berryman, MJ & McDonnell, MD 2011, 'Methods for generating complex networks with selected structural properties for simulations : a review and tutorial for neuroscientists', Frontiers in Computational Neuroscience, v. 5, no. 11, pp. 118.
16
1

2010 
McDonnell, MD, Amblard, PO & Stocks, NG 2010, 'Bioinspired communication: performance limits for information transmission and compression in stochastic pooling networks with binary quantizing nodes', Journal of Computational and Theoretical Nanoscience, v. 7, no. 5, pp. 876883.
4

2010 
McDonnell, MD, Burkitt, AN, Grayden, DB, Meffin, H & Grant, AJ 2010, 'A channel model for inferring the optimal number of electrodes for future cochlear implants', IEEE Transactions on Information Theory, v. 56, no. 2, pp. 928940.
9

2010 
McDonnell, MD, Stocks, NG & Amblard, PO 2010, 'Communication of uncoded sensor measurements through nanoscale binarynode stochastic pooling networks', Nano Communication Networks, v. 1, no. 3, pp. 209223.
1

2009 
McDonnell, MD & Abbott, D 2009, 'Randomized switching in the twoenvelope problem', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 465, no. 2111, pp. 33093322.
6

2009 
McDonnell, MD & Abbott, D 2009, 'What is stochastic resonance? : definitions, misconceptions, debates, and its relevance to biology', PLoS Computational Biology, v. 5, no. 5, article no. e1000348.
203

2009 
McDonnell, MD & Fliney, AP 2009, 'Signal acquisition via polarization modulation in single photon sources', Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, v. 80, no. 6, pp. 060102.1060102.4.
1

2009 
McDonnell, MD & Stocks, N 2009, 'Suprathreshold stochastic resonance', Scholarpedia, v. 4, no. 6, pp. 19. 
2009 
McDonnell, MD 2009, 'Information capacity of stochastic pooling networks is achieved by discrete inputs', Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, v. 79, pp. 18.
10

2009 
McDonnell, MD, Amblard, PO & Stocks, NG 2009, 'Stochastic pooling networks', Journal of Statistical Mechanics: Theory and Experiment, v. 9, no. January.
10

2009 
Nikitin, AP, Stocks, NG, Morse, RP & McDonnell, MD 2009, 'Neural population coding is optimized by discrete tuning curves', Physical Review Letters, v. 103, no. 13, pp. 138101.1138101.4.
20

2008 
McDonnell, MD & Stocks, NG 2008, 'Maximally informative stimuli and tuning curves for sigmoidal ratecoding neurons and populations', Physical Review Letters, v. 101, no. 5, pp. 14.
28

2008 
McDonnell, MD & Stocks, NG 2008, 'Optimal sigmoidal tuning curves for intensity encoding sensory neurons with quasiPoisson variability', BMC Neuroscience, v. 9, no. s1, article no. P117. 
Year  Output 

2015 
Gao, X, Grayden, DB & McDonnell, MD 2015, 'Modeling electrode place discrimination in cochlear implants: analysis of the influence of electrode array insertion depth', 7th International IEEE/EMBS Conference on Neural Engineering, IEEE, pp. 691694. 
2015 
McDonnell, MD & Vladusich, T 2015, 'Enhanced image classification with a fastlearning shallow convolutional neural network', International Joint Conference on Neural Networks, IJCNN, IEEE, article no. 7280796, pp. 17. 
2014 
Gao, X, Grayden, D & McDonnell, MD 2014, 'Using convex optimization to compute channel capacity in a channel model of cochlear implant stimulation', International Symposium on Information Theory, IEEE Press, pp. 29192923.
1

2014 
Gao, X, Grayden, DB & McDonnell, MD 2014, 'Inferring the dynamic range of electrode current by using an information theoretic model of cochlear implant stimulation', IEEE Informational Theory Workshop, IEEE Press. 
2014 
Padilla, DE & McDonnell, MD 2014, 'A neurobiologically plausible vector symbolic architecture', IEEE International Conference on Semantic Computing, IEEE Press, pp. 242245. 
2014 
Qiu, S, Wang, S, Guo, W & McDonnell, MD 2014, 'Performance of macroscale molecular communications with sensor cleanse time', International Conference on Telecommunications, IEEE Press, pp. 363368. 
2014 
Tissera, MD & McDonnell, MD 2014, 'Enabling 'question answering' in the MBAT vector symbolic architecture by exploiting orthogonal random matrices', International Conference on Semantic Computing, IEEE, pp. 171174. 
2014 
Wang, S, Guo, W & McDonnell, MD 2014, 'Distance Distributions for Real Cellular Networks', Conference on Computer Communications Workshops, IEEE Press, pp. 181182. 
2014 
Wang, S, Guo, W & McDonnell, MD 2014, 'Downlink interference estimation without feedback for heterogeneous network interference avoidance', International Conference on Telecommunications, IEEE Press, pp. 8287. 
2014 
Wang, S, Guo, W & McDonnell, MD 2014, 'Transmit pulse shaping for molecular communications', Conference on Computer Communications Workshops, IEEE Press, pp. 209210.
1

2013 
Gao, X, Grayden, DB & McDonnell, MD 2013, 'Information theoretic optimization of cochlear implant electrode usage probabilities', 35th annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBS), IEEE Engineering in Medicine and Biology Society, pp. 59745977.
3

2013 
McDonnell, MD & Ward, LM 2013, 'Identifying positive roles for endogenous stochastic noise during computation in neural systems', 35th annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBS), IEEE Engineering in Medicine and Biology Society, pp. 52325235.
1
1

2013 
Padilla, DE, Brinkworth, R & McDonnell, MD 2013, 'Performance of a hierarchical temporal memory network in noisy sequence learning', 2013 IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom), IEEE, pp. 4551.
1

2012 
Moroz, AS, McDonnell, MD, Burkitt, AN, Grayden, DB & Meffin, H 2012, 'Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model', 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp. 29562968.
3

2011 
Prettejohn, BJ & McDonnell, MD 2011, 'Effect of network topology in opinion formation models', 2nd International Workshop on Collaborative Agents  Research and Development, CARE 2010, Held in Conjunction with the 2010 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT10, Springer Verlag, v. 6066, pp. 114124.
1

2009 
Li, F, McDonnell, MD, Amblard, PO & Grant, AJ 2009, 'Sensor selection for distributed detection via multiaccess channels', in J Yuan & I Collings (eds), Australian Communications Theory Workshop, IEEE, pp. 7782. 
2009 
Stocks, N, Nikitin, A, McDonnell, M & Morse, R 2009, 'The role of stochasticity in an informationoptimal neural population code', Institute of Physics Publishing, v. 197, pp. 012015. 
2008 
McDonnell, MD 2008, 'Reliable communication and sensing via parallel redundancy in noisy digital receivers', IEEE, pp. 2328.
1

2008 
McDonnell, MD 2008, 'Signal compression in biological sensory systems: information theoretic performance limits', BioMEMS and Nanotechnology III, SPIE, v. 6799, 6799139. 
2008 
McDonnell, MD, Amblard, PO & Stocks, NG 2008, 'Stochastic pooling networks: a biologically inspired model for robust signal detection and compression', BICTA 2008, IEEE, pp. 7582. 
Editorials
Research
Current Machine Learning Research:
Current Computational and Theoretical Neuroscience Research:
External engagement & recognition
Organisation  Country 

Aston University  UNITED KINGDOM 
Australian National University  AUSTRALIA 
Ecole Normale Suprieure  FRANCE 
GIPSAlab, Department of Images and Signals  FRANCE 
Humboldt University Berlin  GERMANY 
Institute of Statistical Mathematics  JAPAN 
Max Planck Institute for Mathematics in the Sciences  GERMANY 
Ohio State University  UNITED STATES 
Qingdao University  CHINA 
University of Adelaide  AUSTRALIA 
University of Angers  FRANCE 
University of British Columbia  CANADA 
University of California  UNITED STATES 
University of Melbourne  AUSTRALIA 
University of South Australia  AUSTRALIA 
University of Warwick  UNITED KINGDOM 
University of Western Sydney  AUSTRALIA 
External engagement & recognition
Year  Engagement/recognition 

2015 
Affiliate Member of ARC Centre of ExcellenceAustralian Research Council 
2015 
Invited speaker22nd IEEE International Conference on Telecommunications (ICT 2015) 
2012 
Endeavour AwardDepartment of Education 
2010 
Australian Research FellowshipAustralian Research Council 
Teaching & student supervision
Teaching & student supervision
Supervisions from 2010 shown
Thesis title  Student status 

Biologically realistic hierarchical and temporal pattern learning, applied to vision problems  Current 
Computational models of stochastic variability in auditory nerve and electromyography due to transcranial magnetic stimulation  Current 
Confidence and risk decision processing in reinforcement learning  Current 
Enhancing biological plausibility of large scale functional neural networks using spiketiming based dynamic representations  Current 
Modelling the impact of complex synaptic connectivity topologies on cortical neuronal dynamics  Current 
Multiscale wireless communications: from macroscale cities to nanoscale cells  Current 
Theoretical models of information transmission at the electroneural interface of cochlear implants  Current 
Analysis and spiking implementation of the hierarchical temporal memory model for pattern and sequence recognition  Completed 
Modeling of cognitive function during audio distracted driving using EEG  Completed 
Understanding the underlying similarities shared by complex systems: a study of consensus formation, and neuronal network dynamics, through the application of complex network simulations  Completed 