Mathias Quoy

1.2k total citations
39 papers, 602 citations indexed

About

Mathias Quoy is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Mathias Quoy has authored 39 papers receiving a total of 602 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Cognitive Neuroscience, 20 papers in Artificial Intelligence and 9 papers in Electrical and Electronic Engineering. Recurrent topics in Mathias Quoy's work include Neural dynamics and brain function (19 papers), Neural Networks and Applications (16 papers) and Advanced Memory and Neural Computing (9 papers). Mathias Quoy is often cited by papers focused on Neural dynamics and brain function (19 papers), Neural Networks and Applications (16 papers) and Advanced Memory and Neural Computing (9 papers). Mathias Quoy collaborates with scholars based in France, Japan and United States. Mathias Quoy's co-authors include Bruno Cessac, Philippe Gaussier, B. Doyon, Manuel Samuelides, J.P. Banquet, Sorin Moga, Hugues Berry, Alexandre Pitti, Bruno Delord and Catherine Lavandier and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Scientific Reports.

In The Last Decade

Mathias Quoy

38 papers receiving 566 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mathias Quoy France 15 372 219 108 99 80 39 602
Christian W. Eurich Germany 17 633 1.7× 121 0.6× 212 2.0× 119 1.2× 203 2.5× 36 942
Vadim Grubov Russia 20 850 2.3× 160 0.7× 124 1.1× 74 0.7× 156 1.9× 96 1.1k
Anastasiya E. Runnova Russia 17 751 2.0× 134 0.6× 172 1.6× 74 0.7× 97 1.2× 99 975
Hans G. C. Tråvén Sweden 6 221 0.6× 158 0.7× 51 0.5× 58 0.6× 214 2.7× 7 574
Yakov Kazanovich Russia 15 379 1.0× 97 0.4× 123 1.1× 54 0.5× 112 1.4× 39 542
Thomas Voegtlin France 9 210 0.6× 211 1.0× 18 0.2× 77 0.8× 61 0.8× 17 444
Andreas Knoblauch Germany 18 565 1.5× 193 0.9× 43 0.4× 225 2.3× 216 2.7× 47 762
Guanghui Yan China 14 254 0.7× 66 0.3× 52 0.5× 92 0.9× 23 0.3× 73 521
Juan Gao China 14 388 1.0× 66 0.3× 39 0.4× 32 0.3× 41 0.5× 39 762
Dongchuan Yu China 16 499 1.3× 62 0.3× 341 3.2× 53 0.5× 73 0.9× 54 981

Countries citing papers authored by Mathias Quoy

Since Specialization
Citations

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

Fields of papers citing papers by Mathias Quoy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mathias Quoy

This figure shows the co-authorship network connecting the top 25 collaborators of Mathias Quoy. A scholar is included among the top collaborators of Mathias Quoy 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 Mathias Quoy. Mathias Quoy 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.
Torcini, Alessandro, et al.. (2025). Emergence and maintenance of modularity in neural networks with Hebbian and anti-Hebbian inhibitory STDP. PLoS Computational Biology. 21(4). e1012973–e1012973.
2.
3.
Torcini, Alessandro, et al.. (2023). Inhibitory neurons control the consolidation of neural assemblies via adaptation to selective stimuli. Scientific Reports. 13(1). 6949–6949. 4 indexed citations
4.
Pitti, Alexandre, et al.. (2022). Digital computing through randomness and order in neural networks. Proceedings of the National Academy of Sciences. 119(33). e2115335119–e2115335119. 3 indexed citations
5.
Pitti, Alexandre, et al.. (2022). In Search of a Neural Model for Serial Order: A Brain Theory for Memory Development and Higher Level Cognition. IEEE Transactions on Cognitive and Developmental Systems. 14(2). 279–291. 4 indexed citations
6.
Pitti, Alexandre, Mathias Quoy, Sofiane Boucenna, & Catherine Lavandier. (2021). Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization. PLoS Computational Biology. 17(2). e1008566–e1008566. 3 indexed citations
7.
Gaussier, Philippe, Nicolas Cuperlier, Vincent Hok, et al.. (2020). Time as the fourth dimension in the hippocampus. Progress in Neurobiology. 199. 101920–101920. 18 indexed citations
8.
Pitti, Alexandre, et al.. (2020). Autonomous learning and chaining of motor primitives using the Free\n Energy Principle. arXiv (Cornell University). 2 indexed citations
9.
Gaussier, Philippe, J.P. Banquet, Nicolas Cuperlier, et al.. (2019). Merging information in the entorhinal cortex: what can we learn from robotics experiments and modeling?. Journal of Experimental Biology. 222(Suppl_1). 15 indexed citations
10.
Pitti, Alexandre, Mathias Quoy, Catherine Lavandier, & Sofiane Boucenna. (2019). Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE). Neural Networks. 121. 242–258. 10 indexed citations
11.
Pitti, Alexandre, Philippe Gaussier, & Mathias Quoy. (2017). Iterative free-energy optimization for recurrent neural networks (INFERNO). PLoS ONE. 12(3). e0173684–e0173684. 7 indexed citations
12.
Gaussier, Philippe, et al.. (2013). The hippocampo-cortical loop: Spatio-temporal learning and goal-oriented planning in navigation. Neural Networks. 43. 8–21. 18 indexed citations
13.
Pitti, Alexandre, Yasuo Kuniyoshi, Mathias Quoy, & Philippe Gaussier. (2013). Modeling the Minimal Newborn's Intersubjective Mind: The Visuotopic-Somatotopic Alignment Hypothesis in the Superior Colliculus. PLoS ONE. 8(7). e69474–e69474. 17 indexed citations
14.
Pitti, Alexandre, et al.. (2013). Neural model for learning-to-learn of novel task sets in the motor domain. Frontiers in Psychology. 4. 771–771. 7 indexed citations
15.
Quoy, Mathias, et al.. (2010). A Circuit-Level Model of Hippocampal Place Field Dynamics Modulated by Entorhinal Grid and Suppression-Generating Cells. Frontiers in Neural Circuits. 4. 122–122. 11 indexed citations
16.
Quoy, Mathias, et al.. (2007). Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons. Journal of Physiology-Paris. 101(1-3). 136–148. 47 indexed citations
17.
Quoy, Mathias, et al.. (2002). Learning and motivational couplings promote smarter behaviors of an animat in an unknown world. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
18.
Quoy, Mathias, et al.. (1998). Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning. Neural Networks. 11(3). 521–533. 31 indexed citations
19.
Doyon, B., Bruno Cessac, Mathias Quoy, & Manuel Samuelides. (1995). Mean-field equations, bifurcation map and chaos in discrete time, continuous state, random neural networks. Acta Biotheoretica. 43(1-2). 169–175. 5 indexed citations
20.
Doyon, B., Bruno Cessac, Mathias Quoy, & Manuel Samuelides. (1992). Destabilization and Route to Chaos in Neural Networks with Random Connectivity. Neural Information Processing Systems. 5. 549–555. 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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