Benjamin Scellier
Impact in
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function
- Artificial Intelligence top 10%
- Neural Networks and Reservoir Computing
- Neural Networks and Applications
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
Papers in
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- Neural Networks and Reservoir Computing 2
- Neural Networks and Applications 2
- Domain Adaptation and Few-Shot Learning 1
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- Advanced Memory and Neural Computing 3
- Co-authors
- Yoshua Bengio (2 shared papers)Arvind Murugan (1 shared paper)David S. Berman (1 shared paper)Thomas Fischbacher (1 shared paper)Gianluca Inverso (1 shared paper)Anirudh Goyal (1 shared paper)Thomas Mésnard (1 shared paper)Jonathan Binas (1 shared paper)
- Journals
- Nature Communications (1 paper)Journal of High Energy Physics (1 paper)Neural Computation (1 paper)Frontiers in Computational Neuroscience (1 paper)International Conference on Learning Representations (1 paper)
- Partner nations
- SwitzerlandUnited StatesFrance
In The Last Decade
Benjamin Scellier
5 papers receiving 237 citations
Peers
Comparison fields: 5 of 42
- Cognitive Neuroscience 113
- Artificial Intelligence 143
- Electrical and Electronic Engineering 131
- Computational Mathematics 1
- Cellular and Molecular Neuroscience 23
Countries citing papers authored by Benjamin Scellier
This map shows the geographic impact of Benjamin Scellier'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 Benjamin Scellier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benjamin Scellier more than expected).
Fields of papers citing papers by Benjamin Scellier
This network shows the impact of papers produced by Benjamin Scellier. 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 Benjamin Scellier. The network helps show where Benjamin Scellier may publish in the future.
Co-authors
The 8 scholars most cited alongside Benjamin Scellier, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 233 | |
| 2 | 2024 | 5 | |
| 3 | 2022 | 3 | |
| 4 | 2025 | 2 | |
| 5 | Extending the Framework of Equilibrium Propagation to General Dynamics | 2018 | 1 |
About Benjamin Scellier
Benjamin Scellier is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Cognitive Neuroscience, Astronomy and Astrophysics and Oceanography, having authored 5 papers that have together received 244 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (3 papers), Neural Networks and Reservoir Computing (2 papers), Neural dynamics and brain function (2 papers), Neural Networks and Applications (2 papers), Geophysics and Gravity Measurements (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Black Holes and Theoretical Physics (1 paper) and Cosmology and Gravitation Theories (1 paper). The work is most often cited by research in Cognitive Neuroscience (113 citations), Artificial Intelligence (143 citations), Electrical and Electronic Engineering (131 citations), Computational Mathematics (1 citation) and Cellular and Molecular Neuroscience (23 citations). Benjamin Scellier has collaborated with scholars based in Switzerland, United States and France. Frequent co-authors include Yoshua Bengio, Arvind Murugan, David S. Berman, Thomas Fischbacher, Gianluca Inverso, Anirudh Goyal, Thomas Mésnard and Jonathan Binas. Their work appears in journals such as Nature Communications, Journal of High Energy Physics, Neural Computation, Frontiers in Computational Neuroscience and International Conference on Learning Representations.
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.