David Domínguez

430 total citations
36 papers, 241 citations indexed

About

David Domínguez is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Statistical and Nonlinear Physics. According to data from OpenAlex, David Domínguez has authored 36 papers receiving a total of 241 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 15 papers in Cognitive Neuroscience and 10 papers in Statistical and Nonlinear Physics. Recurrent topics in David Domínguez's work include Neural Networks and Applications (22 papers), Neural dynamics and brain function (15 papers) and Neural Networks and Reservoir Computing (5 papers). David Domínguez is often cited by papers focused on Neural Networks and Applications (22 papers), Neural dynamics and brain function (15 papers) and Neural Networks and Reservoir Computing (5 papers). David Domínguez collaborates with scholars based in Spain, Brazil and Ecuador. David Domínguez's co-authors include Mario González, W. K. Theumann, Francisco B. Rodrı́guez, D. Bollé, Ángel Sánchez, María del Mar Alonso‐Almeida, Eduardo Serrano, Шун-ичи Амари, Jürgen Dunkel and S. G. Magalhães and has published in prestigious journals such as Physical Review Letters, Physical review. B, Condensed matter and Expert Systems with Applications.

In The Last Decade

David Domínguez

32 papers receiving 229 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Domínguez Spain 11 124 69 54 34 25 36 241
Juan I. Perotti Argentina 11 39 0.3× 25 0.4× 125 2.3× 9 0.3× 30 1.2× 19 274
Federico Musciotto Italy 9 47 0.4× 15 0.2× 178 3.3× 30 0.9× 43 1.7× 22 320
Xingqin Qi China 10 87 0.7× 13 0.2× 217 4.0× 21 0.6× 48 1.9× 31 409
Daniel J. Fenn United States 5 26 0.2× 17 0.2× 93 1.7× 10 0.3× 11 0.4× 7 326
K. Kavitha India 5 126 1.0× 11 0.2× 27 0.5× 36 1.1× 26 1.0× 14 290
Mark Kröll Austria 9 153 1.2× 22 0.3× 13 0.2× 25 0.7× 8 0.3× 32 277
Xiangyi Meng United States 12 98 0.8× 9 0.1× 149 2.8× 13 0.4× 18 0.7× 31 333
Alcides Viamontes Esquivel Sweden 4 50 0.4× 16 0.2× 190 3.5× 15 0.4× 38 1.5× 6 261
Konstantin Kuzmin United States 7 107 0.9× 17 0.2× 244 4.5× 31 0.9× 57 2.3× 9 329
Rui Xiao China 9 64 0.5× 28 0.4× 182 3.4× 5 0.1× 91 3.6× 23 310

Countries citing papers authored by David Domínguez

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of David Domínguez

This figure shows the co-authorship network connecting the top 25 collaborators of David Domínguez. A scholar is included among the top collaborators of David Domínguez 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 Domínguez. David Domínguez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
González, Mario, Ángel Sánchez, David Domínguez, & Francisco B. Rodrı́guez. (2025). A constructive heuristic for pattern assignment in an ensemble of attractor neural networks to increase storage capacity. Expert Systems with Applications. 279. 127351–127351.
2.
Domínguez, David, et al.. (2020). Panama Papers' offshoring network behavior. Heliyon. 6(6). e04293–e04293. 13 indexed citations
3.
Domínguez, David, et al.. (2019). Soft-Computing Modeling and Prediction of Gender Equality. 31. 242–248.
4.
González, Mario, María del Mar Alonso‐Almeida, & David Domínguez. (2017). Mapping global sustainability report scoring: a detailed analysis of Europe and Asia. Quality & Quantity. 52(3). 1041–1055. 14 indexed citations
5.
González, Mario, et al.. (2016). Structured patterns retrieval using a metric attractor network: Application to fingerprint recognition. Physica A Statistical Mechanics and its Applications. 457. 424–436. 3 indexed citations
6.
González, Mario, David Domínguez, Francisco B. Rodrı́guez, & Ángel Sánchez. (2014). RETRIEVAL OF NOISY FINGERPRINT PATTERNS USING METRIC ATTRACTOR NETWORKS. International Journal of Neural Systems. 24(7). 1450025–1450025. 7 indexed citations
7.
González, Mario, David Domínguez, & Ángel Sánchez. (2011). Learning sequences of sparse correlated patterns using small-world attractor neural networks: An application to traffic videos. Neurocomputing. 74(14-15). 2361–2367. 4 indexed citations
8.
Domínguez, David, et al.. (2011). Structured information in sparse-code metric neural networks. Physica A Statistical Mechanics and its Applications. 391(3). 799–808. 6 indexed citations
9.
Domínguez, David, Mario González, Eduardo Serrano, & Francisco B. Rodrı́guez. (2009). Structured information in small-world neural networks. Physical Review E. 79(2). 21909–21909. 12 indexed citations
10.
González, Mario, David Domínguez, & Francisco B. Rodrı́guez. (2009). Block attractor in spatially organized neural networks. Neurocomputing. 72(16-18). 3795–3801. 7 indexed citations
11.
Domínguez, David, et al.. (2007). Information and Topology in Attractor Neural Networks. Neural Computation. 19(4). 956–973. 14 indexed citations
12.
Domínguez, David, et al.. (2006). Associative Memory: Information and Topology. Research in computing science. 21. 39–48. 1 indexed citations
13.
Bollé, D., et al.. (2003). Time evolution of the extremely diluted Blume-Emery-Griffiths neural network. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 68(6). 62901–62901. 2 indexed citations
14.
Domínguez, David, et al.. (2000). Three-state neural network: From mutual information to the Hamiltonian. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 62(2). 2620–2628. 16 indexed citations
15.
Bollé, D., David Domínguez, & Шун-ичи Амари. (2000). Mutual information of sparsely coded associative memory with self-control and ternary neurons. Neural Networks. 13(4-5). 455–462. 10 indexed citations
16.
Theumann, W. K., et al.. (1999). Categorization in fully connected multistate neural network models. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 60(6). 7321–7331. 2 indexed citations
17.
Domínguez, David. (1998). Information capacity of a hierarchical neural network. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 58(4). 4811–4815. 6 indexed citations
18.
Domínguez, David & W. K. Theumann. (1997). Generalization and chaos in a layered neural network. Journal of Physics A Mathematical and General. 30(5). 1403–1414. 11 indexed citations
19.
Domínguez, David. (1996). Inference and chaos by a network of nonmonotonic neurons. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 54(4). 4066–4070. 7 indexed citations
20.
Fontanari, José F., W. K. Theumann, & David Domínguez. (1989). Potts model in a random field. Physical review. B, Condensed matter. 39(10). 7132–7139. 2 indexed citations

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.

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