Mark D. McDonnell

发表

Bio-inspired communication: performance limits for information transmission and compression in stochastic pooling networks with binary quantizing nodes

Mark D. McDonnell, Pierre-Olivier Amblard, Nigel G. Stocks, 2010 .

Inferring the dynamic range of electrode current by using an information theoretic model of cochlear implant stimulation

Mark D. McDonnell, Xiao Gao, David B. Grayden, 2014, 2014 IEEE Information Theory Workshop (ITW 2014).

Performance of macro-scale molecular communications with sensor cleanse time

Mark D. McDonnell, Siyi Wang, Weisi Guo, 2014, 2014 21st International Conference on Telecommunications (ICT).

Natural moment-to-moment signal variability and stochastic facilitation

Mark D. McDonnell, Lawrence M. Ward, 2011, Nature Reviews Neuroscience.

Acoustic Scene Classification Using Deep Residual Networks with Late Fusion of Separated High and Low Frequency Paths

Mark D. McDonnell, Wei Gao, Wei Gao, 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Editorial: Neuronal Stochastic Variability: Influences on Spiking Dynamics and Network Activity

Mark D. McDonnell, Benjamin Lindner, Joshua H. Goldwyn, 2016, Front. Comput. Neurosci..

Transmit pulse shaping for molecular communications

Mark D. McDonnell, Siyi Wang, Weisi Guo, 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

Modeling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics

Mark D. McDonnell, Bahar Moezzi, Nicolangelo Iannella, 2014, Front. Comput. Neurosci..

An introductory review of information theory in the context of computational neuroscience

Mark D. McDonnell, Shiro Ikeda, Jonathan H. Manton, 2011, Biological Cybernetics.

A Channel Model for Inferring the Optimal Number of Electrodes for Future Cochlear Implants

Mark D. McDonnell, Alex J. Grant, David B. Grayden, 2010, IEEE Transactions on Information Theory.

Deep Extreme Learning Machines for Classification

Mark D. McDonnell, Migel D. Tissera, M. McDonnell, 2015 .

Enhanced image classification with a fast-learning shallow convolutional neural network

Mark D. McDonnell, Tony Vladusich, T. Vladusich, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

A model of neurobiologically plausible least-squares learning in visual cortex

Mark D. McDonnell, Samya Bagchi, M. McDonnell, 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

Modeling Electrode Place Discrimination in Cochlear Implant Stimulation

Mark D. McDonnell, Xiao Gao, David B. Grayden, 2017, IEEE Transactions on Biomedical Engineering.

Applying Stochastic Signal Quantization Theory to the Robust Digitization of Noisy Analog Signals

Mark D. McDonnell, 2009 .

A Neurobiologically Plausible Vector Symbolic Architecture

Mark D. McDonnell, Daniel E. Padilla, M. McDonnell, 2014, 2014 IEEE International Conference on Semantic Computing.

Efficient computation of the Levenberg-Marquardt algorithm for feedforward networks with linear outputs

Mark D. McDonnell, Philip de Chazal, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Optimal quantization for energy-efficient information transfer in a population of neuron-like devices

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2004, SPIE International Symposium on Fluctuations and Noise.

Diagnosing Convolutional Neural Networks using Their Spectral Response

Mark D. McDonnell, Victor Stamatescu, 2018, 2018 Digital Image Computing: Techniques and Applications (DICTA).

Pooling networks for a discrimination task: noise-enhanced detection

Mark D. McDonnell, Pierre-Olivier Amblard, Nigel G. Stocks, 2007, SPIE International Symposium on Fluctuations and Noise.

Semi-supervised convolutional extreme learning machine

Mahmood Yousefi-Azar, Mark D. McDonnell, M. McDonnell, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists

Mark D. McDonnell, Matthew J. Berryman, Brenton J. Prettejohn, 2011, Front. Comput. Neurosci..

25th Annual Computational Neuroscience Meeting: CNS-2016

Morten L. Kringelbach, Majid Nili Ahmadabadi, Anders M. Dale, 2016, BMC Neuroscience.

Fast, Automatic and Scalable Learning to Detect Android Malware

Mahmood Yousefi-Azar, Vijay Varadharajan, Len Hamey, 2017, ICONIP.

Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation

Mark D. McDonnell, Christian Stricker, Ashutosh Mohan, 2013, Front. Comput. Neurosci..

Interaction of short-term depression and firing dynamics in shaping single neuron encoding

Mark D. McDonnell, Christian Stricker, Ashutosh Mohan, 2013, Front. Comput. Neurosci..

Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition

Mark D. McDonnell, Ivan Lee, David Kearney, 2017, IEEE Transactions on Image Processing.

Stochastic Resonance: Preface

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2008 .

Using convex optimization to compute channel capacity in a channel model of cochlear implant stimulation

Mark D. McDonnell, Xiao Gao, David B. Grayden, 2014, 2014 IEEE International Symposium on Information Theory.

Suprathreshold stochastic resonance

Mark D. McDonnell, Nigel G. Stocks, M. McDonnell, 2009, Scholarpedia.

Signal acquisition via polarization modulation in single photon sources

Mark D. McDonnell, Adrian P. Flitney, M. McDonnell, 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

Motif-Role-Fingerprints: The Building-Blocks of Motifs, Clustering-Coefficients and Transitivities in Directed Networks

Mark D. McDonnell, Lawrence M. Ward, Nicolangelo Iannella, 2014, PloS one.

Engineering intelligent electronic systems based on computational neuroscience [scanning the issue]

Terrence J. Sejnowski, Mark D. McDonnell, Auke Jan Ijspeert, 2014, Proc. IEEE.

Too good to be true: when overwhelming evidence fails to convince

Derek Abbott, Mark D. McDonnell, François Chapeau-Blondeau, 2016, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Distance distributions for real cellular networks

Mark D. McDonnell, Siyi Wang, Weisi Guo, 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning

Mark D. McDonnell, Sebastien C. Wong, Eriita G. Jones, 2020, Remote. Sens..

Using noise to break the noise barrier in circuits

Derek Abbott, Mark D. McDonnell, Antonio Rubio, 2005, SPIE Micro + Nano Materials, Devices, and Applications.

Using Style-Transfer to Understand Material Classification for Robotic Sorting of Recycled Beverage Containers

Mark D. McDonnell, Bahar Moezzi, Russell S. A. Brinkworth, 2019, 2019 Digital Image Computing: Techniques and Applications (DICTA).

Open Questions For Suprathreshold Stochastic Resonance In Sensory Neural Models for Motion Detection Using Artificial Insect Vision

Derek Abbott, Mark D. McDonnell, D. Abbott, 2003 .

Enhancing deep extreme learning machines by error backpropagation

Mark D. McDonnell, Migel D. Tissera, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Learned filters for object detection in multi-object visual tracking

Mark D. McDonnell, David Kearney, Victor Stamatescu, 2016, SPIE Defense + Security.

Signal reconstruction via noise through a system of parallel threshold nonlinearities

Derek Abbott, Mark D. McDonnell, 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Optimal quantization in neural coding

Derek Abbott, Mark D. McDonnell, 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

Performance of a hierarchical temporal memory network in noisy sequence learning

Mark D. McDonnell, Daniel E. Padilla, Russell Brinkworth, 2013, 2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM).

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

Silvestro Micera, Johannes Schemmel, Szabolcs Káli, 2017, BMC Neuroscience.

Enabling 'Question Answering' in the MBAT Vector Symbolic Architecture by Exploiting Orthogonal Random Matrices

Mark D. McDonnell, Migel D. Tissera, M. McDonnell, 2014, 2014 IEEE International Conference on Semantic Computing.

Deep extreme learning machines: supervised autoencoding architecture for classification

Mark D. McDonnell, Migel D. Tissera, M. McDonnell, 2016, Neurocomputing.

Sensor selection for distributed detection via multiaccess channels

Mark D. McDonnell, Feng Li, Alex J. Grant, 2009, 2009 Australian Communications Theory Workshop.

High-resolution optimal quantization for stochastic pooling networks

Derek Abbott, Mark D. McDonnell, Pierre-Olivier Amblard, 2006, SPIE Micro + Nano Materials, Devices, and Applications.

Thai Handwritten Recognition on Text Block-Based from Thai Archive Manuscripts

Mark D. McDonnell, Wei Gao, Rapeeporn Chamchong, 2019, 2019 International Conference on Document Analysis and Recognition (ICDAR).

A Kuramoto coupling of quasi-cycle oscillators with application to neural networks

Mark D. McDonnell, Lawrence M. Ward, Priscilla E. Greenwood, 2016 .

Downlink interference estimation without feedback for heterogeneous network interference avoidance

Mark D. McDonnell, Siyi Wang, Weisi Guo, 2014, 2014 21st International Conference on Telecommunications (ICT).

Optimal coding of a random stimulus by a population of parallel neuron models

Derek Abbott, Mark D. McDonnell, Nigel G. Stocks, 2007, SPIE International Symposium on Fluctuations and Noise.

Analysis of Gradient Degradation and Feature Map Quality in Deep All-Convolutional Neural Networks Compared to Deep Residual Networks

Wei Gao, Mark D. McDonnell, 2017, ICONIP.

An analysis of noise enhanced information transmission in an array of comparators

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2002 .

Phase changes in neuronal postsynaptic spiking due to short term plasticity

Mark D. McDonnell, Bruce P. Graham, B. Graham, 2017, PLoS Comput. Biol..

Ion channel noise can explain firing correlation in auditory nerves

Mark D. McDonnell, Bahar Moezzi, Nicolangelo Iannella, 2016, Journal of Computational Neuroscience.

Anomaly Detection in Satellite Communications Systems using LSTM Networks

Mark D. McDonnell, Lachlan J. Gunn, Edward Arbon, 2018, 2018 Military Communications and Information Systems Conference (MilCIS).

Predicting Financial Well-Being Using Observable Features and Gradient Boosting

Mark D. McDonnell, Belinda A. Chiera, Iqbal Madakkatel, 2019, Australasian Conference on Artificial Intelligence.

Stochastic Resonance: Large N suprathreshold stochastic resonance

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2008 .

A biologically inspired model for signal compression

Derek Abbott, Mark D. McDonnell, 2006, SPIE Micro + Nano Materials, Devices, and Applications.

Identifying positive roles for endogenous stochastic noise during computation in neural systems

Mark D. McDonnell, L. M. Ward, Lawrence M. Ward, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Is electrical noise useful? [Point of View]

Mark D. McDonnell, M. McDonnell, 2011, Proceedings of the IEEE.

Generalized noise resonance: using noise for signal enhancement

Derek Abbott, Mark D. McDonnell, Antonio Rubio, 2004, SPIE International Symposium on Fluctuations and Noise.

Degradation of Performance in Reinforcement Learning with State Measurement Uncertainty

Mark D. McDonnell, Mark McKenzie, M. McDonnell, 2019, 2019 Military Communications and Information Systems Conference (MilCIS).

Neural mechanisms for analog-to-digital conversion

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2004, SPIE Micro + Nano Materials, Devices, and Applications.

A Unified Account of Perceptual Layering and Surface Appearance in Terms of Gamut Relativity

Mark D. McDonnell, Tony Vladusich, T. Vladusich, 2014, PloS one.

The role of stochasticity in an information-optimal neural population code

Mark D. McDonnell, Robert P. Morse, Nigel G. Stocks, 2009 .

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm

Mark D. McDonnell, André van Schaik, Tony Vladusich, 2015, PloS one.

Stochastic Resonance: List of figures

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2008 .

Noise may be music to bionic ears

Mark D. McDonnell, Robert P. Morse, 2008 .

Information theoretic optimization of cochlear implant electrode usage probabilities

Mark D. McDonnell, Xiao Gao, David B. Grayden, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Communication of uncoded sensor measurements through nanoscale binary-node stochastic pooling networks

Mark D. McDonnell, Pierre-Olivier Amblard, Nigel G. Stocks, 2010, Nano Commun. Networks.

Stochastic Resonance: Index

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2008 .

Maximizing information transfer through nonlinear noisy devices

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2002, SPIE Micro + Nano Materials, Devices, and Applications.

Regularized training of the extreme learning machine using the conjugate gradient method

Mark D. McDonnell, Philip de Chazal, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Data processing inequality and stochastic resonance

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2003, SPIE International Symposium on Fluctuations and Noise.

End-to-End Phoneme Recognition using Models from Semantic Image Segmentation

Mark D. McDonnell, Wei Gao, Ahmad Hashemi-Sakhtsari, 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

Stochastic Resonance: References

Derek Abbott, Mark D. McDonnell, Nigel G. Stocks, 2008 .

Understanding Data Augmentation for Classification: When to Warp?

Mark D. McDonnell, Victor Stamatescu, Sebastien C. Wong, 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

On the importance of pair-wise feature correlations for image classification

Mark D. McDonnell, Philip de Chazal, Robby G. McKilliam, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Derek Abbott, Mark D. McDonnell, D. Abbott, 2009, PLoS Comput. Biol..

Training wide residual networks for deployment using a single bit for each weight pdf

Mark D. McDonnell, M. McDonnell, 2018, ICLR.

How to use noise to reduce complexity in quantization

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2005, SPIE Micro + Nano Materials, Devices, and Applications.

Metabolic cost of neuronal information in an empirical stimulus-response model

Mark D. McDonnell, Petr Lánský, Lubomir Kostal, 2013, Biological Cybernetics.

Signal compression in biological sensory systems: information theoretic performance limits

Mark D. McDonnell, M. McDonnell, 2007, SPIE Micro + Nano Materials, Devices, and Applications.

Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning

Mark D. McDonnell, Daniel E. Padilla, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Effect of network topology in opinion formation models

Mark D. McDonnell, Brenton J. Prettejohn, 2009, CARE 2009.

Modeling electrode place discrimination in cochlear implants: Analysis of the influence of electrode array insertion depth

Mark D. McDonnell, Xiao Gao, David B. Grayden, 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).

The benefits of noise in neural systems: bridging theory and experiment

Mark D. McDonnell, Lawrence M. Ward, L. M. Ward, 2011, Nature Reviews Neuroscience.

Modular expansion of the hidden layer in Single Layer Feedforward neural Networks

Mark D. McDonnell, Migel D. Tissera, M. McDonnell, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model

Mark D. McDonnell, David B. Grayden, Anthony N. Burkitt, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

SNDR enhancement in noisy sinusoidal signals by non-linear processing elements

Derek Abbott, Mark D. McDonnell, Antonio Rubio, 2007, SPIE International Symposium on Fluctuations and Noise.

Optimal quantization and suprathreshold stochastic resonance

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2005, SPIE International Symposium on Fluctuations and Noise.

Neuronal Stochastic Variability: Influences on Spiking Dynamics and Network Activity

Mark D. McDonnell, Benjamin Lindner, Joshua H. Goldwyn, 2016 .

Stochastic Resonance: Introduction and motivation

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2008 .

Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems

Mark D. McDonnell, Hesham Mostafa, Runchun Wang, 2019, 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS).

Fast, simple and accurate handwritten digit classification using extreme learning machines with shaped input-weights pdf

Mark D. McDonnell, André van Schaik, Jonathan Tapson, 2014, ArXiv.

Neural information transfer in a noisy environment

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2001, SPIE Micro + Nano Materials, Devices, and Applications.

Analog-to-digital conversion using suprathreshold stochastic resonance

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2005, SPIE Micro + Nano Materials, Devices, and Applications.

Abstract PO-004: A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma

Mark D. McDonnell, Mahdi Yaghoobi, Amin Zadeh Shirazi, 2021 .

A model of the effects of authority on consensus formation in adaptive networks: Impact on network topology and robustness

Mark D. McDonnell, Matthew J. Berryman, Brenton J. Prettejohn, 2013 .

Randomized switching in the two-envelope problem

Derek Abbott, Mark D. McDonnell, D. Abbott, 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons with Quasi-Poisson Variability

Mark D. McDonnell, Nigel G. Stocks, 2008 .

Optimal weighted suprathreshold stochastic resonance with multigroup saturating sensors

Derek Abbott, Mark D. McDonnell, Fabing Duan, 2016 .

M-ary suprathreshold stochastic resonance in multilevel threshold systems with signal-dependent noise

Mark D. McDonnell, Xiao Gao, Bingchang Zhou, 2017 .

Mathematical Methods for Spatially Cohesive Reserve Design

Mark D. McDonnell, Hugh P. Possingham, H. Possingham, 2002 .

Optimising threshold levels for information transmission in binary threshold networks: Independent multiplicative noise on each threshold

Mark D. McDonnell, Bingchang Zhou, M. McDonnell, 2015 .

A characterization of suprathreshold stochastic resonance in an array of comparators by correlation coefficient

Derek Abbott, Mark D. McDonnell, Charles E. M. Pearce, 2002 .

Stochastic pooling networks

Mark D. McDonnell, Nigel G. Stocks, Pierre-Olivier Amblard, 2009, 0901.3644.

Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance

Derek Abbott, Mark D. McDonnell, Nigel G. Stocks, 2006 .

Gain from the two-envelope problem via information asymmetry: on the suboptimality of randomized switching

Derek Abbott, Mark D. McDonnell, Alex J. Grant, 2011, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Point singularities and suprathreshold stochastic resonance in optimal coding

Derek Abbott, Mark D. McDonnell, Nigel G. Stocks, 2004 .

Small Modifications to Network Topology Can Induce Stochastic Bistable Spiking Dynamics in a Balanced Cortical Model

Mark D. McDonnell, Lawrence M. Ward, L. M. Ward, 2014, PloS one.

Input-rate modulation of gamma oscillations is sensitive to network topology, delays and short-term plasticity

Mark D. McDonnell, Lawrence M. Ward, Christian Stricker, 2012, Brain Research.

M-ary suprathreshold stochastic resonance: Generalization and scaling beyond binary threshold nonlinearities

Mark D. McDonnell, Xiao Gao, 2014 .

Stochastic resonance and data processing inequality

Derek Abbott, Mark D. McDonnell, Nigel G. Stocks, 2003 .

Quantization in the presence of large amplitude threshold noise

Derek Abbott, Mark D. McDonnell, Nigel G. Stocks, 2005 .

Decoding suprathreshold stochastic resonance with optimal weights

Derek Abbott, Mark D. McDonnell, Fabing Duan, 2015 .

Channel-noise-induced stochastic facilitation in an auditory brainstem neuron model.

Mark D. McDonnell, Brett A. Schmerl, Mark D McDonnell, 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.