Laurent Perrinet

2.3k total citations
70 papers, 1.2k citations indexed

About

Laurent Perrinet is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Laurent Perrinet has authored 70 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Cognitive Neuroscience, 22 papers in Electrical and Electronic Engineering and 16 papers in Computer Vision and Pattern Recognition. Recurrent topics in Laurent Perrinet's work include Neural dynamics and brain function (52 papers), Visual perception and processing mechanisms (33 papers) and Advanced Memory and Neural Computing (19 papers). Laurent Perrinet is often cited by papers focused on Neural dynamics and brain function (52 papers), Visual perception and processing mechanisms (33 papers) and Advanced Memory and Neural Computing (19 papers). Laurent Perrinet collaborates with scholars based in France, United States and United Kingdom. Laurent Perrinet's co-authors include Guillaume S. Masson, Karl Friston, Rick A. Adams, Michael Breakspear, Simon J. Thorpe, Manuel Samuelides, Arnaud Delorme, Anna Montagnini, Gabriel Cristóbal and Rafael Redondo and has published in prestigious journals such as Nature Communications, Journal of Neuroscience and Nature Neuroscience.

In The Last Decade

Laurent Perrinet

67 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laurent Perrinet France 18 992 252 251 195 102 70 1.2k
Thomas Wächtler Germany 20 945 1.0× 386 1.5× 164 0.7× 113 0.6× 58 0.6× 77 1.5k
Pietro Berkes United States 12 1.2k 1.3× 286 1.1× 177 0.7× 118 0.6× 295 2.9× 19 1.6k
Ethan A. Solomon United States 10 1.8k 1.9× 329 1.3× 150 0.6× 441 2.3× 263 2.6× 19 2.2k
Odelia Schwartz United States 18 1.8k 1.8× 571 2.3× 136 0.5× 246 1.3× 171 1.7× 40 2.1k
Yoonsuck Choe United States 14 468 0.5× 143 0.6× 224 0.9× 122 0.6× 306 3.0× 108 924
James A. Bednar United Kingdom 21 829 0.8× 188 0.7× 106 0.4× 96 0.5× 206 2.0× 61 1.1k
Roland Baddeley United Kingdom 16 1.3k 1.3× 226 0.9× 89 0.4× 738 3.8× 98 1.0× 22 1.7k
Alan A. Stocker United States 20 1.6k 1.6× 201 0.8× 93 0.4× 244 1.3× 124 1.2× 60 2.0k
Bart De Bruyn Belgium 19 928 0.9× 114 0.5× 399 1.6× 133 0.7× 334 3.3× 193 1.8k
Ben D. B. Willmore United Kingdom 21 1.2k 1.2× 390 1.5× 93 0.4× 104 0.5× 126 1.2× 34 1.5k

Countries citing papers authored by Laurent Perrinet

Since Specialization
Citations

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

Fields of papers citing papers by Laurent Perrinet

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laurent Perrinet

This figure shows the co-authorship network connecting the top 25 collaborators of Laurent Perrinet. A scholar is included among the top collaborators of Laurent Perrinet 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 Laurent Perrinet. Laurent Perrinet 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.
Zhu, Ruijie, et al.. (2025). A predictive approach to enhance time-series forecasting. Nature Communications. 16(1). 8645–8645.
2.
Ieng, Sio-Hoï, et al.. (2024). A robust event-driven approach to always-on object recognition. Neural Networks. 178. 106415–106415. 1 indexed citations
3.
Martinet, Jean, et al.. (2023). Stakes of neuromorphic foveation: a promising future for embedded event cameras. Biological Cybernetics. 117(4-5). 389–406.
4.
Chavane, Frédéric, et al.. (2023). Cortical recurrence supports resilience to sensory variance in the primary visual cortex. Communications Biology. 6(1). 667–667. 2 indexed citations
5.
Calatroni, Luca, et al.. (2023). Beyond ℓ1 sparse coding in V1. PLoS Computational Biology. 19(9). e1011459–e1011459. 2 indexed citations
6.
Perrinet, Laurent, et al.. (2023). Learning heterogeneous delays in a layer of spiking neurons for fast motion detection. Biological Cybernetics. 117(4-5). 373–387. 5 indexed citations
7.
Perrinet, Laurent, et al.. (2023). Ultrafast Image Categorization in Biology and Neural Models. Vision. 7(2). 29–29. 1 indexed citations
8.
Martinet, Jean, et al.. (2022). Precise Spiking Motifs in Neurobiological and Neuromorphic Data. Brain Sciences. 13(1). 68–68. 4 indexed citations
9.
Chavane, Frédéric, et al.. (2022). Pooling strategies in V1 can account for the functional and structural diversity across species. PLoS Computational Biology. 18(7). e1010270–e1010270. 5 indexed citations
10.
Fleuriet, Jérôme, et al.. (2022). A Behavioral Receptive Field for Ocular Following in Monkeys: Spatial Summation and Its Spatial Frequency Tuning. eNeuro. 9(4). ENEURO.0374–21.2022. 2 indexed citations
11.
Montagnini, Anna, et al.. (2020). Humans adapt their anticipatory eye movements to the volatility of visual motion properties. PLoS Computational Biology. 16(4). e1007438–e1007438. 12 indexed citations
12.
Perrinet, Laurent. (2019). An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features. Vision. 3(3). 47–47. 4 indexed citations
13.
Chemla, Sandrine, Alexandre Reynaud, Matteo di Volo, et al.. (2019). Suppressive Traveling Waves Shape Representations of Illusory Motion in Primary Visual Cortex of Awake Primate. Journal of Neuroscience. 39(22). 4282–4298. 31 indexed citations
14.
Perrinet, Laurent, et al.. (2019). Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli. Scientific Reports. 9(1). 456–456. 8 indexed citations
15.
Ruffier, Franck, et al.. (2019). Meaningful representations emerge from Sparse Deep Predictive Coding. arXiv (Cornell University). 1 indexed citations
16.
Khoei, Mina A., Guillaume S. Masson, & Laurent Perrinet. (2017). The Flash-Lag Effect as a Motion-Based Predictive Shift. PLoS Computational Biology. 13(1). e1005068–e1005068. 36 indexed citations
17.
Montagnini, Anna, et al.. (2012). Effect of image statistics on fixational eye movements. Journal of Vision. 12(9). 1014–1014. 1 indexed citations
18.
Bednar, James A., et al.. (2011). Edge statistics in natural images versus laboratory animal environments: Implications for understanding lateral connectivity in V1. Neuroscience. 1 indexed citations
19.
Bogadhi, Amarender R., Anna Montagnini, Pascal Mamassian, Laurent Perrinet, & Guillaume S. Masson. (2010). Pursuing motion illusions: A realistic oculomotor framework for Bayesian inference. Vision Research. 51(8). 867–880. 15 indexed citations
20.
Voges, Nicole & Laurent Perrinet. (2009). Phase space analysis of networks based on biologically realistic parameters. Journal of Physiology-Paris. 104(1-2). 51–60. 10 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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