David G. T. Barrett
- Artificial Intelligence top 10%
- Cognitive Neuroscience
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Cellular and Molecular Neuroscience
- Co-authors
- Sophie DenèveChristian K. MachensAri S. MorcosAdam SantoroTimothy LillicrapFelix HillSamuel SmithSoham De
- Topics
- Neural dynamics and brain function (3 papers)Neural Networks and Applications (3 papers)Advanced Memory and Neural Computing (3 papers)
- Partner nations
- United StatesUnited KingdomPortugal
In The Last Decade
David G. T. Barrett
11 papers receiving 188 citations
Peers
Comparison fields: 5 of 62
- Artificial Intelligence 110
- Cognitive Neuroscience 73
- Computer Vision and Pattern Recognition 65
- Electrical and Electronic Engineering 48
- Cellular and Molecular Neuroscience 27
Countries citing papers authored by David G. T. Barrett
This map shows the geographic impact of David G. T. Barrett'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 David G. T. Barrett with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David G. T. Barrett more than expected).
Fields of papers citing papers by David G. T. Barrett
This network shows the impact of papers produced by David G. T. Barrett. 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 David G. T. Barrett. The network helps show where David G. T. Barrett may publish in the future.
Co-authorship network of co-authors of David G. T. Barrett
This figure shows the co-authorship network connecting the top 25 collaborators of David G. T. Barrett. A scholar is included among the top collaborators of David G. T. Barrett 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 David G. T. Barrett. David G. T. Barrett is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 19 | |
| 2 | Learning to Make Analogies by Contrasting Abstract Relational Structure | 13 |
| 3 | On the importance of single directions for generalization | 15 |
| 4 | 3 | |
| 5 | 55 | |
| 6 | 13 | |
| 7 | 29 | |
| 8 | Firing rate predictions in optimal balanced networks | 10 |
| 9 | Learning optimal spike-based representations | 29 |
| 10 | 7 | |
| 11 | 10 |
About David G. T. Barrett
David G. T. Barrett is a scholar working on Developmental Biology, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 11 papers that have together received 203 indexed citations. Recurring topics across this work include Neural dynamics and brain function (3 papers), Neural Networks and Applications (3 papers) and Advanced Memory and Neural Computing (3 papers). The work is most often cited by research in Cognitive Neuroscience (73 citations), Artificial Intelligence (110 citations) and Computer Vision and Pattern Recognition (65 citations). David G. T. Barrett has collaborated with scholars based in United States, United Kingdom and Portugal. Frequent co-authors include Sophie Denève, Christian K. Machens, Ari S. Morcos, Adam Santoro, Timothy Lillicrap, Felix Hill, Samuel Smith, Soham De, Neil C. Rabinowitz and Matthew Botvinick. Their work appears in journals such as eLife, Network Computation in Neural Systems and Microgravity Science and Technology.
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.