Paul Masset

635 total citations
11 papers, 280 citations indexed

About

Paul Masset is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Sensory Systems. According to data from OpenAlex, Paul Masset has authored 11 papers receiving a total of 280 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cognitive Neuroscience, 3 papers in Cellular and Molecular Neuroscience and 2 papers in Sensory Systems. Recurrent topics in Paul Masset's work include Neural dynamics and brain function (7 papers), Visual perception and processing mechanisms (2 papers) and EEG and Brain-Computer Interfaces (2 papers). Paul Masset is often cited by papers focused on Neural dynamics and brain function (7 papers), Visual perception and processing mechanisms (2 papers) and EEG and Brain-Computer Interfaces (2 papers). Paul Masset collaborates with scholars based in United States, Germany and United Kingdom. Paul Masset's co-authors include Torben Ott, Ádám Kepecs, Junya Hirokawa, Alexander Vaughan, Armin Lak, Naoshige Uchida, Shanshan Qin, Emily Hueske, Susumu Tonegawa and Jacob A. Zavatone-Veth and has published in prestigious journals such as Nature, Cell and Neuron.

In The Last Decade

Paul Masset

10 papers receiving 279 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Paul Masset United States	7	232	74	26	26	22	11	280
Diogo Peixoto United States	8	334 1.4×	109 1.5×	34 1.3×	17 0.7×	13 0.6×	9	388
Kenneth W. Latimer United States	8	284 1.2×	95 1.3×	23 0.9×	30 1.2×	14 0.6×	12	323
Katharina A. Wilmes United Kingdom	8	267 1.2×	121 1.6×	20 0.8×	21 0.8×	8 0.4×	14	297
Romain D. Cazé United Kingdom	7	243 1.0×	158 2.1×	32 1.2×	38 1.5×	19 0.9×	10	316
Norman H. Lam United States	7	190 0.8×	50 0.7×	13 0.5×	15 0.6×	8 0.4×	11	258
Alex T. Piet United States	7	136 0.6×	51 0.7×	23 0.9×	12 0.5×	8 0.4×	9	170
Dominic Standage Canada	12	279 1.2×	58 0.8×	8 0.3×	18 0.7×	8 0.4×	25	339
Ruben S. van Bergen Netherlands	7	366 1.6×	38 0.5×	9 0.3×	23 0.9×	18 0.8×	10	409
Jay A. Hennig United States	7	301 1.3×	99 1.3×	17 0.7×	26 1.0×	9 0.4×	9	326
Shiva Farashahi United States	8	232 1.0×	38 0.5×	16 0.6×	31 1.2×	7 0.3×	13	292

Countries citing papers authored by Paul Masset

Since Specialization

Citations

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

Fields of papers citing papers by Paul Masset

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Masset

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Masset. A scholar is included among the top collaborators of Paul Masset 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 Paul Masset. Paul Masset is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Matias, Sara, Hao Wu, Simona Temereanca, et al.. (2025). Interpretable deep learning for deconvolutional analysis of neural signals. Neuron. 113(8). 1151–1168.e13. 2 indexed citations

2.

Masset, Paul, et al.. (2025). Multi-timescale reinforcement learning in the brain. Nature. 642(8068). 682–690. 4 indexed citations

3.

Masset, Paul, et al.. (2023). Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb. 64793–64828.

4.

Masset, Paul, Shanshan Qin, & Jacob A. Zavatone-Veth. (2022). Drifting neuronal representations: Bug or feature?. Biological Cybernetics. 116(3). 253–266. 15 indexed citations

5.

Ott, Torben, Paul Masset, Thiago S. Gouvêa, & Ádám Kepecs. (2022). Apparent sunk cost effect in rational agents. Science Advances. 8(6). eabi7004–eabi7004. 6 indexed citations

6.

Ott, Torben, Paul Masset, Thiago S. Gouvêa, & Ádám Kepecs. (2022). Apparent sunk cost effect in rational agents. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

7.

Masset, Paul, Torben Ott, Armin Lak, Junya Hirokawa, & Ádám Kepecs. (2020). Behavior- and Modality-General Representation of Confidence in Orbitofrontal Cortex. Cell. 182(1). 112–126.e18. 54 indexed citations

8.

Lak, Armin, Emily Hueske, Junya Hirokawa, et al.. (2020). Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon. eLife. 9. 58 indexed citations

9.

Hirokawa, Junya, Alexander Vaughan, Paul Masset, Torben Ott, & Ádám Kepecs. (2019). Frontal cortex neuron types categorically encode single decision variables. Nature. 576(7787). 446–451. 118 indexed citations

10.

Ott, Torben, Paul Masset, & Ádám Kepecs. (2018). The Neurobiology of Confidence: From Beliefs to Neurons. Cold Spring Harbor Symposia on Quantitative Biology. 83. 9–16. 7 indexed citations

11.

Chalk, Matthew, Paul Masset, Sophie Denève, & Boris Gutkin. (2017). Sensory noise predicts divisive reshaping of receptive fields. PLoS Computational Biology. 13(6). e1005582–e1005582. 15 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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