David Domínguez
Impact in
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- stochastic dynamics and bifurcation
- Complex Network Analysis Techniques
- Artificial Intelligence top 10%
- Neural Networks and Applications
- Neural Networks and Reservoir Computing
Papers in
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- Neural Networks and Applications 26
- Neural Networks and Reservoir Computing 6
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- Neural dynamics and brain function 16
- Co-authors
- Mario González (26 shared papers)Francisco B. Rodrı́guez (18 shared papers)W. K. Theumann (9 shared papers)Ángel Sánchez (12 shared papers)D. Bollé (4 shared papers)María del Mar Alonso‐Almeida (3 shared papers)Eduardo Serrano (4 shared papers)Шун-ичи Амари (1 shared paper)
In The Last Decade
David Domínguez
40 papers receiving 327 citations
Peers
Comparison fields: 5 of 71
- Statistical and Nonlinear Physics 71
- Artificial Intelligence 175
- Cognitive Neuroscience 88
- Condensed Matter Physics 32
- Signal Processing 31
Countries citing papers authored by David Domínguez
This map shows the geographic impact of David Domínguez'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 Domínguez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Domínguez more than expected).
Fields of papers citing papers by David Domínguez
This network shows the impact of papers produced by David Domínguez. 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 Domínguez. The network helps show where David Domínguez may publish in the future.
Co-authors
The 17 scholars most cited alongside David Domínguez, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1998 | 21 | |
| 2 | 2000 | 19 | |
| 3 | 2009 | 18 | |
| 4 | 2015 | 18 | |
| 5 | 2017 | 17 | |
| 6 | 2022 | 17 | |
| 7 | 2020 | 15 | |
| 8 | 1996 | 15 | |
| 9 | 2007 | 15 | |
| 10 | 2000 | 15 | |
| 11 | 2014 | 14 | |
| 12 | 2016 | 12 | |
| 13 | 1997 | 11 | |
| 14 | 2022 | 10 | |
| 15 | 2011 | 10 | |
| 16 | 2011 | 10 | |
| 17 | 2014 | 8 | |
| 18 | 2016 | 8 | |
| 19 | 2021 | 8 | |
| 20 | 2009 | 8 |
About David Domínguez
David Domínguez is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Statistical and Nonlinear Physics, Condensed Matter Physics and Economics and Econometrics, having authored 45 papers that have together received 343 indexed citations. Recurring topics across this work include Neural Networks and Applications (26 papers), Neural dynamics and brain function (16 papers), Neural Networks and Reservoir Computing (6 papers), Theoretical and Computational Physics (6 papers), stochastic dynamics and bifurcation (6 papers), Face and Expression Recognition (5 papers), Complex Network Analysis Techniques (5 papers) and Complex Systems and Time Series Analysis (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (71 citations), Artificial Intelligence (175 citations), Cognitive Neuroscience (88 citations), Condensed Matter Physics (32 citations) and Signal Processing (31 citations). David Domínguez has collaborated with scholars based in Spain, Ecuador and Brazil. Frequent co-authors include Mario González, Francisco B. Rodrı́guez, W. K. Theumann, Ángel Sánchez, D. Bollé, María del Mar Alonso‐Almeida, Eduardo Serrano, Шун-ичи Амари, Jürgen Dunkel and Sergio Garcia Magalhaes. Their work appears in journals such as Neurocomputing, Sustainability, Physica A Statistical Mechanics and its Applications, Expert Systems with Applications and Quality & Quantity.
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.