Luke Marris
Impact in
- Cognitive Neuroscience top 5%
- Neural dynamics and brain function
- EEG and Brain-Computer Interfaces
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Neural Networks and Applications
- Artificial Intelligence in Games
- Neural Networks and Reservoir Computing
Papers in
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- Artificial Intelligence in Games 1
- Evolutionary Algorithms and Applications 1
- Neural Networks and Reservoir Computing 1
- Reinforcement Learning in Robotics 1
- Neural Networks and Applications 1
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- Advanced Memory and Neural Computing 2
- Ferroelectric and Negative Capacitance Devices 1
- Co-authors
- Timothy Lillicrap (2 shared papers)Geoffrey E. Hinton (2 shared papers)Adam Santoro (2 shared papers)Colin J. Akerman (1 shared paper)Charles Beattie (1 shared paper)Neil C. Rabinowitz (1 shared paper)David Silver (1 shared paper)Demis Hassabis (1 shared paper)
- Journals
- Nature reviews. Neuroscience (1 paper)Science (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- CanadaUnited KingdomUnited States
In The Last Decade
Luke Marris
3 papers receiving 835 citations
Luke Marris's Hit Papers
Peers
Comparison fields: 5 of 117
- Cognitive Neuroscience 261
- Artificial Intelligence 403
- Health Informatics 8
- Cellular and Molecular Neuroscience 76
- Computer Vision and Pattern Recognition 82
Countries citing papers authored by Luke Marris
This map shows the geographic impact of Luke Marris'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 Luke Marris with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luke Marris more than expected).
Fields of papers citing papers by Luke Marris
This network shows the impact of papers produced by Luke Marris. 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 Luke Marris. The network helps show where Luke Marris may publish in the future.
Co-authors
The 22 scholars most cited alongside Luke Marris, 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 | Backpropagation and the brain Hit paper breakdown → | 2020 | 462 |
| 2 | Human-level performance in 3D multiplayer games with population-based reinforcement learning Hit paper breakdown → | 2019 | 358 |
| 3 | 2018 | 37 |
About Luke Marris
Luke Marris is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering, Cognitive Neuroscience, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 857 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (2 papers), Artificial Intelligence in Games (1 paper), Evolutionary Algorithms and Applications (1 paper), Ferroelectric and Negative Capacitance Devices (1 paper), Neural Networks and Reservoir Computing (1 paper), Neural dynamics and brain function (1 paper), Reinforcement Learning in Robotics (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Cognitive Neuroscience (261 citations), Artificial Intelligence (403 citations), Health Informatics (8 citations), Cellular and Molecular Neuroscience (76 citations) and Computer Vision and Pattern Recognition (82 citations). Luke Marris has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include Timothy Lillicrap, Geoffrey E. Hinton, Adam Santoro, Colin J. Akerman, Charles Beattie, Neil C. Rabinowitz, David Silver, Demis Hassabis, Wojciech Marian Czarnecki and Koray Kavukcuoglu. Their work appears in journals such as Nature reviews. Neuroscience, Science and arXiv (Cornell University).
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.