Liam Maguire

5.5k total citations · 1 hit paper
174 papers, 3.6k citations indexed

About

Liam Maguire is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Liam Maguire has authored 174 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Cognitive Neuroscience, 63 papers in Electrical and Electronic Engineering and 52 papers in Artificial Intelligence. Recurrent topics in Liam Maguire's work include Neural dynamics and brain function (63 papers), Advanced Memory and Neural Computing (44 papers) and Neuroscience and Neural Engineering (20 papers). Liam Maguire is often cited by papers focused on Neural dynamics and brain function (63 papers), Advanced Memory and Neural Computing (44 papers) and Neuroscience and Neural Engineering (20 papers). Liam Maguire collaborates with scholars based in United Kingdom, United States and China. Liam Maguire's co-authors include T.M. McGinnity, Ammar Belatreche, Yuhua Li, Jim Harkin, Damien Coyle, Aboozar Taherkhani, Paul Humphreys, Ronan McIvor, Basabdatta Sen Bhattacharya and Junxiu Liu and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and NeuroImage.

In The Last Decade

Liam Maguire

170 papers receiving 3.5k citations

Hit Papers

A review of learning in biologically plausible spiking ne... 2019 2026 2021 2023 2019 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Liam Maguire United Kingdom 33 1.3k 1.3k 1.0k 575 329 174 3.6k
T.M. McGinnity United Kingdom 37 1.5k 1.1× 1.9k 1.4× 1.6k 1.6× 726 1.3× 564 1.7× 308 5.5k
Donald Gross Canada 41 1.4k 1.1× 2.1k 1.6× 255 0.2× 676 1.2× 308 0.9× 132 10.3k
Yuhua Li United Kingdom 28 641 0.5× 421 0.3× 1.7k 1.7× 205 0.4× 419 1.3× 139 3.8k
Amit Konar India 40 721 0.5× 1.2k 0.9× 3.5k 3.4× 390 0.7× 926 2.8× 366 6.9k
Chai Quek Singapore 42 587 0.4× 1.1k 0.9× 2.4k 2.4× 294 0.5× 664 2.0× 246 6.3k
Hava T. Siegelmann United States 27 787 0.6× 661 0.5× 2.2k 2.1× 177 0.3× 350 1.1× 118 4.6k
Suresh Sundaram Singapore 41 816 0.6× 739 0.6× 2.9k 2.9× 88 0.2× 828 2.5× 307 6.4k
Sen Wang China 35 678 0.5× 418 0.3× 1.2k 1.2× 157 0.3× 162 0.5× 236 4.7k
Atulya K. Nagar United Kingdom 27 466 0.4× 437 0.3× 1.4k 1.4× 125 0.2× 421 1.3× 338 3.7k
Kevin Warwick United Kingdom 34 364 0.3× 890 0.7× 893 0.9× 532 0.9× 1.1k 3.4× 289 3.8k

Countries citing papers authored by Liam Maguire

Since Specialization
Citations

This map shows the geographic impact of Liam Maguire'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 Liam Maguire with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liam Maguire more than expected).

Fields of papers citing papers by Liam Maguire

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liam Maguire. 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 Liam Maguire. The network helps show where Liam Maguire may publish in the future.

Co-authorship network of co-authors of Liam Maguire

This figure shows the co-authorship network connecting the top 25 collaborators of Liam Maguire. A scholar is included among the top collaborators of Liam Maguire 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 Liam Maguire. Liam Maguire is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ding, Xuemei, et al.. (2023). Convolutional AutoEncoders for Anomaly Detection in Semiconductor Manufacturing. Ulster University Research Portal (Ulster University). 1–6.
2.
Ding, Xuemei, et al.. (2022). Anomaly Detection in Batch Manufacturing Processes Using Localized Reconstruction Errors From 1-D Convolutional AutoEncoders. IEEE Transactions on Semiconductor Manufacturing. 36(1). 147–150. 12 indexed citations
3.
Bucholc, Magda, Xuemei Ding, Haiying Wang, et al.. (2019). A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. Expert Systems with Applications. 130. 157–171. 66 indexed citations
4.
Taherkhani, Aboozar, Ammar Belatreche, Yuhua Li, et al.. (2019). A review of learning in biologically plausible spiking neural networks. Neural Networks. 122. 253–272. 268 indexed citations breakdown →
5.
Ding, Xuemei, Magda Bucholc, Haiying Wang, et al.. (2018). A hybrid computational approach for efficient Alzheimer’s disease classification based on heterogeneous data. Scientific Reports. 8(1). 9774–9774. 38 indexed citations
6.
Bokde, Arun L.W., et al.. (2018). Metastable neural dynamics in Alzheimer's disease are disrupted by lesions to the structural connectome. NeuroImage. 183. 438–455. 32 indexed citations
7.
Youssofzadeh, Vahab, Bernadette McGuinness, Liam Maguire, & KongFatt Wong‐Lin. (2017). Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer’s Disease in AIBL Data: Group and Individual Analyses. Frontiers in Human Neuroscience. 11. 380–380. 32 indexed citations
8.
Taherkhani, Aboozar, Ammar Belatreche, Yuhua Li, & Liam Maguire. (2014). A new biologically plausible supervised learning method for spiking neurons. University of Salford Institutional Repository (University of Salford). 11–16. 9 indexed citations
9.
Li, Xingfeng, Damien Coyle, Liam Maguire, & T.M. McGinnity. (2012). A Least Trimmed Square Regression Method for Second Level fMRI Effective Connectivity Analysis. Neuroinformatics. 11(1). 105–118. 3 indexed citations
11.
Zou, Xin, Damien Coyle, KongFatt Wong‐Lin, & Liam Maguire. (2011). Computational Study of Hippocampal-Septal Theta Rhythm Changes Due to Beta-Amyloid-Altered Ionic Channels. PLoS ONE. 6(6). e21579–e21579. 25 indexed citations
12.
Bhattacharya, Basabdatta Sen, Damien Coyle, & Liam Maguire. (2010). A computational modelling approach to investigate alpha rhythm slowing associated with Alzheimer’s Disease. Ulster University Research Portal (Ulster University). 8 indexed citations
13.
McCann, Michael, et al.. (2008). Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control.. University of Salford Institutional Repository (University of Salford). 277–288. 11 indexed citations
14.
Wall, Julie, Liam McDaid, Liam Maguire, & T.M. McGinnity. (2008). Spiking neuron models of the medial and lateral superior olive for sound localisation. UEL Research Repository (University of East London). 15. 2641–2647. 6 indexed citations
15.
Callaghan, Michael, Jim Harkin, T.M. McGinnity, & Liam Maguire. (2007). Paradigms in Remote Experimentation. International Journal of Online and Biomedical Engineering (iJOE). 3(4). 23 indexed citations
16.
Wu, Qing, T.M. McGinnity, Liam Maguire, Ammar Belatreche, & Brendan Glackin. (2007). Edge Detection Based on Spiking Neural Network Model. 26–34. 15 indexed citations
17.
Callaghan, Michael, Jim Harkin, T.M. McGinnity, & Liam Maguire. (2006). Client-Server Architecture for Remote Experimentation for Embedded Systems. 2(4). 130–137. 21 indexed citations
18.
Belatreche, Ammar, Liam Maguire, T.M. McGinnity, & Qingxiang Wu. (2003). An Evolutionary Strategy for Supervised Training of Biologically Plausible Neural Networks. 106(6). 701–5. 7 indexed citations
19.
McGinnity, T.M., et al.. (2001). Fault Diagnosis of Electronic Circuit Boards Using Intelligent Techniques: A Review. IEEE Systems Man and Cybernetics Magazine. 31(3). 269–281. 2 indexed citations
20.
McGinnity, T.M., et al.. (1999). Flexible learning in a cross-border environment. International journal of engineering education. 15(2). 137–141. 2 indexed citations

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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