Mark D. McDonnell

6.9k total citations · 3 hit papers
113 papers, 4.3k citations indexed

About

Mark D. McDonnell is a scholar working on Cognitive Neuroscience, Statistical and Nonlinear Physics and Artificial Intelligence. According to data from OpenAlex, Mark D. McDonnell has authored 113 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Cognitive Neuroscience, 55 papers in Statistical and Nonlinear Physics and 36 papers in Artificial Intelligence. Recurrent topics in Mark D. McDonnell's work include stochastic dynamics and bifurcation (50 papers), Neural dynamics and brain function (48 papers) and Neural Networks and Applications (17 papers). Mark D. McDonnell is often cited by papers focused on stochastic dynamics and bifurcation (50 papers), Neural dynamics and brain function (48 papers) and Neural Networks and Applications (17 papers). Mark D. McDonnell collaborates with scholars based in Australia, United Kingdom and United States. Mark D. McDonnell's co-authors include Derek Abbott, Nigel G. Stocks, C. E. M. Pearce, Lawrence M. Ward, Ian R. Ball, Hugh P. Possingham, Tony Vladusich, Wei Gao, Matthew J. Berryman and Xiao Gao and has published in prestigious journals such as Physical Review Letters, Nature reviews. Neuroscience and PLoS ONE.

In The Last Decade

Mark D. McDonnell

110 papers receiving 4.1k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Stochastic Resonance

2008 1.2k citations Mark D. McDonnell, Derek Abbott et al. arXiv (Cornell University) profile →
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

2009 553 citations Mark D. McDonnell, Derek Abbott PLoS Computational Biology profile →
The benefits of noise in neural systems: bridging theory and experiment

2011 546 citations Mark D. McDonnell, Lawrence M. Ward Nature reviews. Neuroscience profile →

Peers

Countries citing papers authored by Mark D. McDonnell

Since Specialization

Citations

This map shows the geographic impact of Mark D. McDonnell's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mark D. McDonnell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark D. McDonnell more than expected).

Fields of papers citing papers by Mark D. McDonnell

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark D. McDonnell. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark D. McDonnell. The network helps show where Mark D. McDonnell may publish in the future.

Co-authorship network of co-authors of Mark D. McDonnell

This figure shows the co-authorship network connecting the top 25 collaborators of Mark D. McDonnell. A scholar is included among the top collaborators of Mark D. McDonnell based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mark D. McDonnell. Mark D. McDonnell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Planet Four: A Neural Network’s search for polar spring-time fans on Mars 2022 ·Icarus ·Mark D. McDonnell, Eriita Jones, Megan E. Schwamb, (unknown), Ganna Portyankina, C. J. Hansen	2
2	A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma 2021 ·British Journal of Cancer ·(unknown), Mark D. McDonnell, (unknown), (unknown), (unknown), Michael S. Samuel, Mahdi Yaghoobi, Rebecca J. Ormsby, Santosh Poonnoose, Damon J. Tumes, Guillermo A. Gómez	31
3	Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants 2021 ·PLoS ONE ·Xiao Gao, David B. Grayden, Mark D. McDonnell	3
4	Acoustic Scene Classification Using Deep Residual Networks with Late Fusion of Separated High and Low Frequency Paths 2020 ·Mark D. McDonnell, Wei Gao	66
5	Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis 2019 ·Neuroscience ·Bahar Moezzi, (unknown), Brenton Hordacre, Lynton Graetz, Carolyn Berryman, Louise Lavrencic, Michael C. Ridding, Hannah A. D. Keage, Mark D. McDonnell, Mitchell R. Goldsworthy	38
6	Ion channel noise can explain firing correlation in auditory nerves 2016 ·Journal of Computational Neuroscience ·Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell	9
7	Fast, simple and accurate handwritten digit classification using extreme learning machines with shaped input-weights. 2014 ·arXiv (Cornell University) ·Mark D. McDonnell, (unknown), André van Schaik, Jonathan Tapson	1
8	A Unified Account of Perceptual Layering and Surface Appearance in Terms of Gamut Relativity 2014 ·PLoS ONE ·Tony Vladusich, Mark D. McDonnell	1
9	Small Modifications to Network Topology Can Induce Stochastic Bistable Spiking Dynamics in a Balanced Cortical Model 2014 ·PLoS ONE ·Mark D. McDonnell, Lawrence M. Ward	8
10	Motif-Role-Fingerprints: The Building-Blocks of Motifs, Clustering-Coefficients and Transitivities in Directed Networks 2014 ·PLoS ONE ·Mark D. McDonnell, Ömer Nebil Yaveroğlu, (unknown), Nicolangelo Iannella, Lawrence M. Ward	13
11	Optimal sensor selection for noisy binary detection in stochastic pooling networks 2013 ·Physical Review E ·Mark D. McDonnell, Feng Li, Pierre‐Olivier Amblard, Alex Grant	4
12	Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model 2012 ·PubMed ·(unknown), Mark D. McDonnell, Anthony N. Burkitt, David B. Grayden, Hamish Meffin	5
13	The benefits of noise in neural systems: bridging theory and experiment breakdown → 2011 ·Nature reviews. Neuroscience ·Mark D. McDonnell, Lawrence M. Ward	546
14	Information capacity of stochastic pooling networks is achieved by discrete inputs 2009 ·Physical Review E ·Mark D. McDonnell	12
15	Signal acquisition via polarization modulation in single photon sources 2009 ·Physical Review E ·Mark D. McDonnell, Adrian P. Flitney	2
16	Neural Population Coding Is Optimized by Discrete Tuning Curves 2009 ·Physical Review Letters ·Alexander P. Nikitin, Nigel G. Stocks, Robert P. Morse, Mark D. McDonnell	34
17	What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology breakdown → 2009 ·PLoS Computational Biology ·Mark D. McDonnell, Derek Abbott	553
18	Stochastic Resonance breakdown → 2008 ·arXiv (Cornell University) ·Mark D. McDonnell, Derek Abbott, Nigel G. Stocks, C. E. M. Pearce	1223
19	Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations 2008 ·Physical Review Letters ·Mark D. McDonnell, Nigel G. Stocks	47
20	Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons with Quasi-Poisson Variability 2008 ·arXiv (Cornell University) ·Mark D. McDonnell, Nigel G. Stocks	0

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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