William W. Lytton
- Cognitive Neuroscience top 0.5%
- Cellular and Molecular Neuroscience top 1%
- Electrical and Electronic Engineering top 10%
- Molecular Biology
- Statistical and Nonlinear Physics top 2%
- Co-authors
- Terrence J. SejnowskiSamuel A. NeymotinMichael L. HinesSalvador Durá-BernalAndré A. FentonRobert A. McDougalCliff C. KerrDaniel J. Uhlrich
- Topics
- Neural dynamics and brain function (89 papers)Neuroscience and Neuropharmacology Research (50 papers)Neuroscience and Neural Engineering (41 papers)
- Partner nations
- United StatesAustraliaCanada
In The Last Decade
William W. Lytton
142 papers receiving 3.4k citations
Hit Papers
Peers
Comparison fields: 5 of 172
- Cognitive Neuroscience 2.3k
- Cellular and Molecular Neuroscience 1.8k
- Electrical and Electronic Engineering 522
- Molecular Biology 449
- Statistical and Nonlinear Physics 261
Countries citing papers authored by William W. Lytton
This map shows the geographic impact of William W. Lytton'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 William W. Lytton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William W. Lytton more than expected).
Fields of papers citing papers by William W. Lytton
This network shows the impact of papers produced by William W. Lytton. 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 William W. Lytton. The network helps show where William W. Lytton may publish in the future.
Co-authorship network of co-authors of William W. Lytton
This figure shows the co-authorship network connecting the top 25 collaborators of William W. Lytton. A scholar is included among the top collaborators of William W. Lytton 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 William W. Lytton. William W. Lytton is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 5 | |
| 4 | 8 | |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 12 | |
| 9 | 65 | |
| 10 | 88 | |
| 11 | Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciencesbreakdown → | 405 |
| 12 | 13 | |
| 13 | 1 | |
| 14 | 6 | |
| 15 | 1 | |
| 16 | 1 | |
| 17 | 6 | |
| 18 | 92 | |
| 19 | 114 | |
| 20 | Neural Network Analysis of Event Related Potentials and Electroencephalogram Predicts Vigilance | 4 |
About William W. Lytton
William W. Lytton is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Biophysics, having authored 146 papers that have together received 3.5k indexed citations. Recurring topics across this work include Neural dynamics and brain function (89 papers), Neuroscience and Neuropharmacology Research (50 papers) and Neuroscience and Neural Engineering (41 papers). The work is most often cited by research in Cognitive Neuroscience (2.3k citations), Cellular and Molecular Neuroscience (1.8k citations) and Biophysics (130 citations). William W. Lytton has collaborated with scholars based in United States, Australia and Canada. Frequent co-authors include Terrence J. Sejnowski, Samuel A. Neymotin, Michael L. Hines, Salvador Durá-Bernal, André A. Fenton, Robert A. McDougal, Cliff C. Kerr, Daniel J. Uhlrich, Joseph T. Francis and Jie Zhu. Their work appears in journals such as Journal of Neuroscience, Nature reviews. Neuroscience and PLoS ONE.
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.