Timothée Masquelier

7.6k total citations · 4 hit papers
57 papers, 4.7k citations indexed

About

Timothée Masquelier is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering and Cellular and Molecular Neuroscience. According to data from OpenAlex, Timothée Masquelier has authored 57 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Cognitive Neuroscience, 38 papers in Electrical and Electronic Engineering and 19 papers in Cellular and Molecular Neuroscience. Recurrent topics in Timothée Masquelier's work include Neural dynamics and brain function (46 papers), Advanced Memory and Neural Computing (37 papers) and CCD and CMOS Imaging Sensors (13 papers). Timothée Masquelier is often cited by papers focused on Neural dynamics and brain function (46 papers), Advanced Memory and Neural Computing (37 papers) and CCD and CMOS Imaging Sensors (13 papers). Timothée Masquelier collaborates with scholars based in France, Spain and Iran. Timothée Masquelier's co-authors include Simon J. Thorpe, Saeed Reza Kheradpisheh, ‎Mohammad Ganjtabesh, Amirhossein Tavanaei, Anthony S. Maida, Masoud Ghodrati, Teresa Serrano‐Gotarredona, B. Linares-Barranco, Rudy Guyonneau and Milad Mozafari and has published in prestigious journals such as Neuron, Journal of Neuroscience and PLoS ONE.

In The Last Decade

Timothée Masquelier

55 papers receiving 4.6k citations

Hit Papers

Deep learning in spiking neural networks 2017 2026 2020 2023 2018 2017 2021 2023 250 500 750
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.

  1. Deep learning in spiking neural networks
    2018 814 citations Amirhossein Tavanaei, Masoud Ghodrati et al. Neural Networks profile →
  2. STDP-based spiking deep convolutional neural networks for object recognition
    2017 508 citations Saeed Reza Kheradpisheh, ‎Mohammad Ganjtabesh et al. Neural Networks profile →
  3. Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
    2021 356 citations Zhaofei Yu, Yanqi Chen et al. profile →
  4. SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
    2023 159 citations Wei Fang, Yanqi Chen et al. Science Advances profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Timothée Masquelier France 27 3.7k 2.8k 1.5k 1.3k 232 57 4.7k
Robert Legenstein Austria 23 2.0k 0.5× 1.7k 0.6× 922 0.6× 1.1k 0.9× 253 1.1× 62 3.1k
Steven K. Esser United States 11 3.6k 1.0× 1.6k 0.6× 1.5k 1.0× 1.3k 1.0× 244 1.1× 12 4.4k
Andrew S. Cassidy United States 18 4.8k 1.3× 1.8k 0.6× 1.7k 1.1× 1.7k 1.3× 287 1.2× 40 5.3k
Rodrigo Alvarez-Icaza United States 8 4.6k 1.3× 1.8k 0.6× 1.7k 1.1× 1.6k 1.3× 200 0.9× 11 5.0k
Brian Taba United States 8 4.8k 1.3× 1.8k 0.6× 1.6k 1.0× 1.7k 1.3× 435 1.9× 8 5.3k
Paul Merolla United States 17 5.9k 1.6× 2.3k 0.8× 2.2k 1.5× 2.0k 1.6× 324 1.4× 24 6.4k
Filipp Akopyan United States 8 4.2k 1.1× 1.5k 0.5× 1.6k 1.0× 1.4k 1.1× 190 0.8× 9 4.5k
Nabil Imam United States 10 4.0k 1.1× 1.5k 0.5× 1.5k 1.0× 1.4k 1.0× 186 0.8× 16 4.4k
Rathinakumar Appuswamy United States 8 3.3k 0.9× 1.1k 0.4× 1.2k 0.8× 1.2k 0.9× 239 1.0× 13 3.6k
Jun Sawada United States 8 3.9k 1.1× 1.3k 0.5× 1.4k 0.9× 1.3k 1.0× 171 0.7× 9 4.2k

Countries citing papers authored by Timothée Masquelier

Since Specialization
Citations

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

Fields of papers citing papers by Timothée Masquelier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Timothée Masquelier

This figure shows the co-authorship network connecting the top 25 collaborators of Timothée Masquelier. A scholar is included among the top collaborators of Timothée Masquelier 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 Timothée Masquelier. Timothée Masquelier 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.
Masquelier, Timothée, et al.. (2025). Temporal recurrence as a general mechanism to explain neural responses in the auditory system. Communications Biology. 8(1). 1456–1456.
2.
Masquelier, Timothée, et al.. (2024). A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses. PLoS Computational Biology. 20(8). e1012288–e1012288. 1 indexed citations
3.
Fang, Wei, Yanqi Chen, Jianhao Ding, et al.. (2023). SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence. Science Advances. 9(40). eadi1480–eadi1480. 159 indexed citations breakdown →
4.
Ezoji, Mehdi, et al.. (2023). Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification. Journal of Computational Neuroscience. 51(4). 475–490. 1 indexed citations
5.
Gautrais, Jacques, et al.. (2022). Analyzing time-to-first-spike coding schemes: A theoretical approach. Frontiers in Neuroscience. 16. 971937–971937. 11 indexed citations
6.
Cottereau, Benoit R., et al.. (2021). Event-Based Trajectory Prediction Using Spiking Neural Networks. Frontiers in Computational Neuroscience. 15. 658764–658764. 14 indexed citations
7.
Mozafari, Milad, ‎Mohammad Ganjtabesh, Abbas Nowzari-Dalini, & Timothée Masquelier. (2019). SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron. Frontiers in Neuroscience. 13. 625–625. 78 indexed citations
8.
Tavanaei, Amirhossein, Timothée Masquelier, & Anthony S. Maida. (2018). Representation learning using event-based STDP. Neural Networks. 105. 294–303. 18 indexed citations
9.
Mozafari, Milad, ‎Mohammad Ganjtabesh, Abbas Nowzari-Dalini, Simon J. Thorpe, & Timothée Masquelier. (2018). Bio-Inspired Digit Recognition Using Spike-Timing-Dependent Plasticity (STDP) and Reward-Modulated STDP in Deep Convolutional Networks. arXiv (Cornell University). 3 indexed citations
10.
Tavanaei, Amirhossein, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothée Masquelier, & Anthony S. Maida. (2018). Deep learning in spiking neural networks. Neural Networks. 111. 47–63. 814 indexed citations breakdown →
11.
Masquelier, Timothée, et al.. (2018). Convis: A Toolbox to Fit and Simulate Filter-Based Models of Early Visual Processing. Frontiers in Neuroinformatics. 12. 9–9. 4 indexed citations
12.
Kheradpisheh, Saeed Reza, ‎Mohammad Ganjtabesh, Simon J. Thorpe, & Timothée Masquelier. (2017). STDP-based spiking deep convolutional neural networks for object recognition. Neural Networks. 99. 56–67. 508 indexed citations breakdown →
13.
Portelli, Geoffrey, John M. Barrett, Gerrit Hilgen, et al.. (2016). Rank Order Coding: a Retinal Information Decoding Strategy Revealed by Large-Scale Multielectrode Array Retinal Recordings. eNeuro. 3(3). ENEURO.0134–15.2016. 84 indexed citations
14.
Kheradpisheh, Saeed Reza, ‎Mohammad Ganjtabesh, Simon J. Thorpe, & Timothée Masquelier. (2016). STDP-based spiking deep neural networks for object recognition.. arXiv (Cornell University). 33 indexed citations
15.
Masquelier, Timothée & Gustavo Deco. (2013). Network Bursting Dynamics in Excitatory Cortical Neuron Cultures Results from the Combination of Different Adaptive Mechanism. PLoS ONE. 8(10). e75824–e75824. 33 indexed citations
16.
Deco, Gustavo, et al.. (2011). The Role of Rhythmic Neural Synchronization in Rest and Task Conditions. Frontiers in Human Neuroscience. 5. 4–4. 33 indexed citations
17.
Masquelier, Timothée & Simon J. Thorpe. (2010). Learning to recognize objects using waves of spikes and Spike Timing-Dependent Plasticity. 1–8. 31 indexed citations
18.
Masquelier, Timothée, Etienne Hugues, Gustavo Deco, & Simon J. Thorpe. (2009). Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme. Journal of Neuroscience. 29(43). 13484–13493. 116 indexed citations
19.
Masquelier, Timothée, Rudy Guyonneau, & Simon J. Thorpe. (2008). Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains. PLoS ONE. 3(1). e1377–e1377. 208 indexed citations
20.
Masquelier, Timothée & Simon J. Thorpe. (2007). Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity. PLoS Computational Biology. 3(2). e31–e31. 370 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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