Thomas Mesquida

406 citations
14 papers · 237 · h-index 7

Impact in

Papers in

Thomas Mesquida

13 papers receiving 235 citations

Peers

Thomas Mesquida
Comparison fields: 5 of 42
  • Cognitive Neuroscience 66
  • Electrical and Electronic Engineering 184
  • Cellular and Molecular Neuroscience 49
  • Artificial Intelligence 78
  • Hardware and Architecture 7
Replace Abhishek Moitra with:
Abhishek Moitra United States
Adarsh Kumar Kosta United States
Amar Shrestha United States
Gregor Lenz France
Bernhard Vogginger Germany
Jyotibdha Acharya Singapore
Renyuan Zhang Japan
Fernando Perez‐Peña Spain
I.E. Opris United States
Christopher Johansson Sweden
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Citations per field
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Abhishek Moitra · 1×
Citations per year

Countries citing papers authored by Thomas Mesquida

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Mesquida

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Thomas Mesquida, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Thomas Mesquida Line = papers co-authored together Thomas Mesquida links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 201965
2 202351
3 202241
4 202235
5 202210
6 20237
7 20237
8 20206
9 20245
10 20235
11 20232
12 20162
13 20221
14 20250

About Thomas Mesquida

Thomas Mesquida is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience, Cellular and Molecular Neuroscience, Artificial Intelligence and Information Systems, having authored 14 papers that have together received 237 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (12 papers), Ferroelectric and Negative Capacitance Devices (6 papers), Neural dynamics and brain function (6 papers), Neuroscience and Neural Engineering (3 papers), Neural Networks and Applications (2 papers), Neural Networks and Reservoir Computing (2 papers), Underwater Vehicles and Communication Systems (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Cognitive Neuroscience (66 citations), Electrical and Electronic Engineering (184 citations), Cellular and Molecular Neuroscience (49 citations), Artificial Intelligence (78 citations) and Hardware and Architecture (7 citations). Thomas Mesquida has collaborated with scholars based in France, Italy and Switzerland. Frequent co-authors include Alexandre Valentian, Lorena Anghel, Elisa Vianello, C. Reita, Olivier Bichler, Thomas Dalgaty, Filippo Moro, N. Castellani, David Esseni and Melika Payvand. Their work appears in journals such as Nature Communications, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) and HAL (Le Centre pour la Communication Scientifique Directe).

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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