Christopher L. Scofield
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Computer Vision and Pattern Recognition
- Cognitive Neuroscience
- Water Science and Technology
- Co-authors
- D.P. MorganLeon N. CooperDouglas L. ReillyJohn AdcockRaymond D. RimeyEsteban RealLuis A. NúñezM. Höller
- Topics
- Neural Networks and Applications (8 papers)Visual perception and processing mechanisms (3 papers)Blind Source Separation Techniques (3 papers)
- Journals
- Proceedings of the National Academy of SciencesContemporary PhysicsNeural Information Processing Systems
- Partner nations
- United States
In The Last Decade
Christopher L. Scofield
10 papers receiving 176 citations
Peers
Comparison fields: 5 of 46
- Artificial Intelligence 128
- Signal Processing 79
- Computer Vision and Pattern Recognition 29
- Cognitive Neuroscience 23
- Water Science and Technology 19
Countries citing papers authored by Christopher L. Scofield
This map shows the geographic impact of Christopher L. Scofield'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 Christopher L. Scofield with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher L. Scofield more than expected).
Fields of papers citing papers by Christopher L. Scofield
This network shows the impact of papers produced by Christopher L. Scofield. 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 Christopher L. Scofield. The network helps show where Christopher L. Scofield may publish in the future.
Co-authorship network of co-authors of Christopher L. Scofield
This figure shows the co-authorship network connecting the top 25 collaborators of Christopher L. Scofield. A scholar is included among the top collaborators of Christopher L. Scofield 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 Christopher L. Scofield. Christopher L. Scofield is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 6 | |
| 5 | 5 | |
| 6 | 7 | |
| 7 | 9 | |
| 8 | 127 | |
| 9 | 17 | |
| 10 | A Mean Field Theory of Layer IV of Visual Cortex and Its Application to Artificial Neural Networks | 1 |
| 11 | 10 | |
| 12 | 13 |
About Christopher L. Scofield
Christopher L. Scofield is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 12 papers that have together received 196 indexed citations. Recurring topics across this work include Neural Networks and Applications (8 papers), Visual perception and processing mechanisms (3 papers) and Blind Source Separation Techniques (3 papers). The work is most often cited by research in Signal Processing (79 citations), Artificial Intelligence (128 citations) and Computer Vision and Pattern Recognition (29 citations). Christopher L. Scofield has collaborated with scholars based in United States. Frequent co-authors include D.P. Morgan, Leon N. Cooper, Douglas L. Reilly, John Adcock, Raymond D. Rimey, Esteban Real, Luis A. Núñez and M. Höller. Their work appears in journals such as Proceedings of the National Academy of Sciences, Contemporary Physics and Neural Information Processing Systems.
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.