Liam McDaid

2.4k total citations
121 papers, 1.6k citations indexed

About

Liam McDaid is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Liam McDaid has authored 121 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 93 papers in Electrical and Electronic Engineering, 61 papers in Cellular and Molecular Neuroscience and 61 papers in Cognitive Neuroscience. Recurrent topics in Liam McDaid's work include Advanced Memory and Neural Computing (76 papers), Neural dynamics and brain function (59 papers) and Neuroscience and Neural Engineering (49 papers). Liam McDaid is often cited by papers focused on Advanced Memory and Neural Computing (76 papers), Neural dynamics and brain function (59 papers) and Neuroscience and Neural Engineering (49 papers). Liam McDaid collaborates with scholars based in United Kingdom, Ireland and United States. Liam McDaid's co-authors include Jim Harkin, John Wade, Junxiu Liu, Liam Maguire, Fearghal Morgan, Brian McGinley, Seamus Cawley, José Santos, Sandeep Dwarkanath Pande and S. Hall and has published in prestigious journals such as PLoS ONE, Scientific Reports and Sensors.

In The Last Decade

Liam McDaid

115 papers receiving 1.6k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Liam McDaid 1.2k 727 664 274 144 121 1.6k
Jim Harkin 1.0k 0.9× 720 1.0× 510 0.8× 240 0.9× 308 2.1× 122 2.0k
Jason K. Eshraghian 1.2k 1.0× 474 0.7× 527 0.8× 377 1.4× 76 0.5× 97 1.7k
Daniel Neil 1.5k 1.3× 370 0.5× 1.0k 1.5× 776 2.8× 54 0.4× 27 2.4k
Alejandro Linares-Barranco 1.7k 1.4× 845 1.2× 669 1.0× 428 1.6× 111 0.8× 132 2.3k
Francesco Galluppi 2.1k 1.8× 1.1k 1.5× 1.1k 1.7× 617 2.3× 61 0.4× 46 2.8k
Daniel Soudry 550 0.5× 576 0.8× 678 1.0× 744 2.7× 92 0.6× 41 2.2k
Ali Shoeb 460 0.4× 557 0.8× 1.3k 1.9× 137 0.5× 61 0.4× 26 1.9k
Karlheinz Meier 1.9k 1.6× 777 1.1× 1.1k 1.6× 683 2.5× 53 0.4× 75 2.2k
Jonathan Tapson 997 0.9× 376 0.5× 678 1.0× 1.0k 3.7× 117 0.8× 118 2.4k
Emre Neftci 2.2k 1.9× 584 0.8× 1.4k 2.1× 1.0k 3.7× 65 0.5× 74 2.7k

Countries citing papers authored by Liam McDaid

Since Specialization
Citations

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

Fields of papers citing papers by Liam McDaid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liam McDaid

This figure shows the co-authorship network connecting the top 25 collaborators of Liam McDaid. A scholar is included among the top collaborators of Liam McDaid 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 McDaid. Liam McDaid 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.
Ng, K.W., Sahil Sharma, Manmohan Singh, et al.. (2025). A locked aptamer-magnetic nanoparticle assay for cardiac troponin I classification to support myocardial infarction diagnosis in resource-limited environments. Biosensors and Bioelectronics. 286. 117590–117590. 3 indexed citations
2.
Wade, John, et al.. (2024). Mathematical Modeling of PI3K/Akt Pathway in Microglia. Neural Computation. 36(4). 645–676. 3 indexed citations
3.
Walter, Andrew, Shimeng Wu, Andy M. Tyrrell, et al.. (2023). Artificial Neural Microcircuits for use in Neuromorphic System Design.
4.
Ghani, Arfan, Thomas Dowrick, & Liam McDaid. (2023). OSPEN: an open source platform for emulating neuromorphic hardware. International Journal of Reconfigurable and Embedded Systems (IJRES). 12(1). 1–1. 3 indexed citations
5.
Wade, John, Alexei Verkhratsky, Mark Dallas, et al.. (2023). The influence of astrocytic leaflet motility on ionic signalling and homeostasis at active synapses. Scientific Reports. 13(1). 3050–3050. 7 indexed citations
6.
Harkin, Jim, Liam McDaid, Bryan Gardiner, et al.. (2022). Image-based computer modeling assessment of microwave ablation for treatment of adrenal tumors. International Journal of Hyperthermia. 39(1). 1264–1275. 4 indexed citations
7.
Wade, John, et al.. (2021). Mathematical modelling of human P2X-mediated plasma membrane electrophysiology and calcium dynamics in microglia. PLoS Computational Biology. 17(11). e1009520–e1009520. 5 indexed citations
8.
McDaid, Liam, et al.. (2021). A Computational Study of Astrocytic GABA Release at the Glutamatergic Synapse: EAAT-2 and GAT-3 Coupled Dynamics. Frontiers in Cellular Neuroscience. 15. 682460–682460. 7 indexed citations
9.
Liu, Junxiu, Liam McDaid, Alfonso Araque, et al.. (2019). GABA Regulation of Burst Firing in Hippocampal Astrocyte Neural Circuit: A Biophysical Model. Frontiers in Cellular Neuroscience. 13. 335–335. 7 indexed citations
10.
Wade, John, KongFatt Wong‐Lin, Jim Harkin, et al.. (2019). Calcium Microdomain Formation at the Perisynaptic Cradle Due to NCX Reversal: A Computational Study. Frontiers in Cellular Neuroscience. 13. 185–185. 14 indexed citations
11.
Millard, Alan G., et al.. (2018). ARDebug: An Augmented Reality Tool for Analysing and Debugging Swarm Robotic Systems. Frontiers in Robotics and AI. 5. 87–87. 14 indexed citations
12.
McDaid, Liam, et al.. (2018). A computational study of astrocytic glutamate influence on post-synaptic neuronal excitability. PLoS Computational Biology. 14(4). e1006040–e1006040. 27 indexed citations
13.
Wade, John, KongFatt Wong‐Lin, Jim Harkin, et al.. (2018). Potassium and sodium microdomains in thin astroglial processes: A computational model study. PLoS Computational Biology. 14(5). e1006151–e1006151. 48 indexed citations
14.
Wade, John, Liam McDaid, Jim Harkin, Vincenzo Crunelli, & Scott Kelso. (2013). Biophysically based computational models of astrocyte ~ neuron coupling and their functional significance. Frontiers in Computational Neuroscience. 7. 44–44. 11 indexed citations
15.
Harkin, Jim, Liam McDaid, Sandeep Dwarkanath Pande, et al.. (2012). Advancing interconnect density for spiking neural network hardware implementations using traffic-aware adaptive network-on-chip routers. Neural Networks. 33. 42–57. 50 indexed citations
16.
Harkin, Jim, Fearghal Morgan, S. Hall, et al.. (2009). Reconfigurable Platforms & the Challenges for Large-Scale Implementations of SNNs. 483–486. 1 indexed citations
17.
Beiu, Valeriu, et al.. (2009). On brain-inspired hierarchical network topologies. 202–205. 1 indexed citations
18.
Wade, John, et al.. (2007). A Biologically Inspired Training Algorithm for Spiking Neural Networks. OpenGrey (Institut de l'Information Scientifique et Technique). 2020. 7–12. 7 indexed citations
19.
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
20.
McDaid, Liam, S. Hall, W. Eccleston, & J. Alderman. (1990). The influence of substrate bias fixed charge in the buried insulator on the gain of the parasitic bipolar inherent in silicon-on-insulator MOSFETs. European Solid-State Device Research Conference. 429–432. 1 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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