Mark D. McDonnell
Mark D. McDonnell
Computational Learning Systems Laboratory, University of South Australia
Verified email at unisa.edu.au - Homepage
TitleCited byYear
What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology
MD McDonnell, D Abbott
PLoS computational biology 5 (5), e1000348, 2009
5192009
The benefits of noise in neural systems: bridging theory and experiment
MD McDonnell, LM Ward
Nature Reviews Neuroscience 12 (7), 415, 2011
3802011
Mathematical methods for spatially cohesive reserve design
MD McDonnell, HP Possingham, IR Ball, EA Cousins
Environmental Modeling & Assessment 7 (2), 107-114, 2002
2802002
Stochastic resonance
MD McDonnell, NG Stocks, CEM Pearce, D Abbott
Stochastic Resonance, by Mark D. McDonnell, Nigel G. Stocks, Charles EM …, 2008
2432008
Stochastic resonance: from suprathreshold stochastic resonance to stochastic signal quantization
MD McDonnell, NG Stocks, CEM Pearce, D Abbott
Cambridge University Press, 2008
243*2008
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
1712016
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
772006
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
772002
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, 2011
612011
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
592002
Deep extreme learning machines: supervised autoencoding architecture for classification
MD Tissera, MD McDonnell
Neurocomputing 174, 42-49, 2016
532016
Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations
MD McDonnell, NG Stocks
Physical review letters 101 (5), 058103, 2008
532008
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
472007
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
432015
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
412015
Neural population coding is optimized by discrete tuning curves
AP Nikitin, NG Stocks, RP Morse, MD McDonnell
Physical review letters 103 (13), 138101, 2009
402009
Is electrical noise useful?[point of view]
MD McDonnell
Proceedings of the IEEE 99 (2), 242-246, 2011
342011
Quantization in the presence of large amplitude threshold noise
MD McDonnell, NG STOCKS, CEM PEARCE, D ABBOTT
Fluctuation and Noise Letters 5 (03), L457-L468, 2005
312005
Stochastic pooling networks
MD McDonnell, PO Amblard, NG Stocks
Journal of Statistical Mechanics: Theory and Experiment 2009 (01), P01012, 2009
272009
An introductory review of information theory in the context of computational neuroscience
MD McDonnell, S Ikeda, JH Manton
Biological cybernetics 105 (1), 55, 2011
262011
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Articles 1–20