Mark D. McDonnell
Mark D. McDonnell
Computational Learning Systems Laboratory, University of South Australia
Verified email at unisa.edu.au - Homepage
Title
Cited by
Cited by
Year
What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology
MD McDonnell, D Abbott
PLoS Comput Biol 5 (5), e1000348, 2009
6112009
The benefits of noise in neural systems: bridging theory and experiment
MD McDonnell, LM Ward
Nature Reviews Neuroscience 12 (7), 415-425, 2011
4932011
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
4312016
Mathematical methods for spatially cohesive reserve design
MD McDonnell, HP Possingham, IR Ball, EA Cousins
Environmental Modeling & Assessment 7 (2), 107-114, 2002
3042002
Stochastic resonance
MD McDonnell, NG Stocks, CEM Pearce, D Abbott
Stochastic Resonance, by Mark D. McDonnell, Nigel G. Stocks, Charles EM …, 2008
2812008
Stochastic resonance: from suprathreshold stochastic resonance to stochastic signal quantization
MD McDonnell, NG Stocks, CEM Pearce, D Abbott
Cambridge University Press, 2008
281*2008
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
842011
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
822006
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
792002
Deep extreme learning machines: supervised autoencoding architecture for classification
MD Tissera, MD McDonnell
Neurocomputing 174, 42-49, 2016
752016
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
732015
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
652015
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
612002
Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations
MD McDonnell, NG Stocks
Physical review letters 101 (5), 058103, 2008
572008
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
522007
Neural population coding is optimized by discrete tuning curves
AP Nikitin, NG Stocks, RP Morse, MD McDonnell
Physical review letters 103 (13), 138101, 2009
472009
Is electrical noise useful?[point of view]
MD McDonnell
Proceedings of the IEEE 99 (2), 242-246, 2011
382011
Training wide residual networks for deployment using a single bit for each weight
MD McDonnell
arXiv preprint arXiv:1802.08530, 2018
362018
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
322005
An introductory review of information theory in the context of computational neuroscience
MD McDonnell, S Ikeda, JH Manton
Biological cybernetics 105 (1), 55, 2011
312011
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Articles 1–20