Mark D. McDonnell

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Derek AbbottProfessor of Electrical & Electronic Engineering, University of Adelaide, AustraliaVerified email at adelaide.edu.au
nigel g stocksprofessor, university of warwickVerified email at eng.warwick.ac.uk
Charles E. M. PearceElder Chair of Mathematics, University of AdelaideVerified email at adelaide.edu.au
Lawrence M WardProfessor of Psychology, University of British ColumbiaVerified email at psych.ubc.ca
Sebastien WongDefence Science and TechnologyVerified email at dst.defence.gov.au
Victor StamatescuVerified email at adelaide.edu.au
Dr. Demi X. GaoBionics Institute, The University of MelbourneVerified email at unimelb.edu.au
Amblard POCNRS, GIPSAlab, GAIAVerified email at gipsa-lab.grenoble-inp.fr
David GraydenDept. of Biomedical Engineering, University of MelbourneVerified email at unimelb.edu.au
André van SchaikDirector, International Centre for Neuromorphic Systems, Western Sydney UniversityVerified email at westernsydney.edu.au
Hugh PossinghamUniversity of QueenslandVerified email at uq.edu.au
Jonathan TapsonChief Technology Officer at IONA TechVerified email at iona.tech
Guillermo GomezGroup Leader at Centre for Cancer Biology, SA Pathology and University of South AustraliaVerified email at unisa.edu.au
Priscilla GreenwoodUniversity of British ColumbiaVerified email at math.ubc.ca
Elina HypponenProfessor in Nutritional and Genetic Epidemiology, University of South Australia, AdelaideVerified email at unisa.edu.au
Eriita JonesUniversity of South Australia; Curtin UniversityVerified email at unisa.edu.au
Philip de ChazalThe University of SydneyVerified email at sydney.edu.au
Minh-Son ToFlinders Health and Medical Research Institute, Flinders UniversityVerified email at flinders.edu.au
Anthony N. BurkittUniversity of MelbourneVerified email at unimelb.edu.au
Hamish MeffinAssociate Professor, The University of MelbourneVerified email at unimelb.edu.au

Mark D. McDonnell

The University of Adelaide

Verified email at ieee.org - Homepage

Machine Learning Computer Vision Computational Neuroscience


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Understanding data augmentation for classification: when to warp? SC Wong, A Gatt, V Stamatescu, MD McDonnell 2016 international conference on digital image computing: techniques and …, 2016	1148	2016
What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology MD McDonnell, D Abbott PLoS computational biology 5 (5), e1000348, 2009	869	2009
The benefits of noise in neural systems: bridging theory and experiment MD McDonnell, LM Ward Nature Reviews Neuroscience 12 (7), 415-425, 2011	775	2011
Stochastic resonance MD McDonnell, NG Stocks, CEM Pearce, D Abbott Stochastic Resonance, 2008	365	2008
Mathematical methods for spatially cohesive reserve design MD McDonnell, HP Possingham, IR Ball, EA Cousins Environmental Modeling & Assessment 7, 107-114, 2002	345	2002
Deep extreme learning machines: supervised autoencoding architecture for classification MD Tissera, MD McDonnell Neurocomputing 174, 42-49, 2016	128	2016
Methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists BJ Prettejohn, MJ Berryman, MD McDonnell Frontiers in computational neuroscience 5, 11, 2011	125	2011
Enhanced image classification with a fast-learning shallow convolutional neural network MD McDonnell, T Vladusich 2015 International Joint Conference on Neural Networks (IJCNN), 1-7, 2015	120	2015
Acoustic scene classification using deep residual networks with late fusion of separated high and low frequency paths MD McDonnell, W Gao ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020	100	2020
Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance MD McDonnell, NG Stocks, CEM Pearce, D Abbott Physics Letters A 352 (3), 183-189, 2006	91	2006
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the ‘extreme learning machine’algorithm MD McDonnell, MD Tissera, T Vladusich, A van Schaik, J Tapson PloS one 10 (8), e0134254, 2015	88	2015
Training wide residual networks for deployment using a single bit for each weight MD McDonnell arXiv preprint arXiv:1802.08530, 2018	83	2018
An analysis of noise enhanced information transmission in an array of comparators MD McDonnell, D Abbott, CEM Pearce Microelectronics Journal 33 (12), 1079-1089, 2002	81	2002
Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations MD McDonnell, NG Stocks Physical review letters 101 (5), 058103, 2008	68	2008
A characterization of suprathreshold stochastic resonance in an array of comparators by correlation coefficient MD Mcdonnell, D Abbott, CEM Pearce Fluctuation and noise letters 2 (03), L205-L220, 2002	68	2002
Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance MD McDonnell, NG Stocks, D Abbott Physical Review E 75 (6), 061105, 2007	58	2007
Neural population coding is optimized by discrete tuning curves AP Nikitin, NG Stocks, RP Morse, MD McDonnell Physical review letters 103 (13), 138101, 2009	56	2009
An introductory review of information theory in the context of computational neuroscience MD McDonnell, S Ikeda, JH Manton Biological cybernetics 105, 55-70, 2011	45	2011
Too good to be true: when overwhelming evidence fails to convince LJ Gunn, F Chapeau-Blondeau, MD McDonnell, BR Davis, A Allison, ... Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2016	44	2016
The application of deep convolutional neural networks to brain cancer images: a survey A Zadeh Shirazi, E Fornaciari, MD McDonnell, M Yaghoobi, Y Cevallos, ... Journal of personalized medicine 10 (4), 224, 2020	40	2020

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