Fernando Corbacho

1.0k citations
22 papers · 717 indexed · h-index 10

Impact in

Papers in

Fernando Corbacho

22 papers receiving 686 citations

Peers

Fernando Corbacho
Comparison fields: 5 of 83
  • Statistical and Nonlinear Physics 285
  • Cognitive Neuroscience 348
  • Computer Networks and Communications 228
  • Artificial Intelligence 143
  • Cellular and Molecular Neuroscience 80
Replace Ikuko Nishikawa with:
Ikuko Nishikawa Japan
J. Michael Herrmann United Kingdom
Sameet Sreenivasan United States
Nils Bertschinger Germany
Benedetta Franceschiello Switzerland
Christian Bick United Kingdom
Chad Giusti United States
Changgui Gu China
Wei Zou China
Márton Pósfai United States
Fernando Corbacho relative to Ikuko Nishikawa Japan Ikuko Nishikawa's profile →
Citations per field
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Ikuko Nishikawa · 1×
Citations per year

Countries citing papers authored by Fernando Corbacho

Since Specialization
Citations

This map shows the geographic impact of Fernando Corbacho'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 Fernando Corbacho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando Corbacho more than expected).

Fields of papers citing papers by Fernando Corbacho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fernando Corbacho. 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 Fernando Corbacho. The network helps show where Fernando Corbacho may publish in the future.

Co-authors

The 17 scholars most cited alongside Fernando Corbacho, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Fernando Corbacho Line = papers co-authored together Fernando Corbacho links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20114
2 201111
3 200924
4 20058
5 20054
6 20043
7 20023
8 20026
9 200125
10 200145
11 2000442
12 200010
13 200010
14 19983
15 199851
16 19982
17
A Neural Schema Architecture for Autonomous Robots
19984
18 19977
19 199527
20 19936

About Fernando Corbacho

Fernando Corbacho is a scholar working on Cognitive Neuroscience, Artificial Intelligence, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition and Sensory Systems, having authored 22 papers that have together received 717 indexed citations. Recurring topics across this work include Neural dynamics and brain function (8 papers), Neural Networks and Applications (6 papers), Neurobiology and Insect Physiology Research (3 papers), Complex Systems and Time Series Analysis (3 papers), Zebrafish Biomedical Research Applications (3 papers), Plant and Biological Electrophysiology Studies (2 papers), Stock Market Forecasting Methods (2 papers) and Complex Network Analysis Techniques (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (285 citations), Cognitive Neuroscience (348 citations), Computer Networks and Communications (228 citations), Artificial Intelligence (143 citations) and Cellular and Molecular Neuroscience (80 citations). Fernando Corbacho has collaborated with scholars based in Spain, United States and France. Frequent co-authors include Luis F. Lago-Fernández, Ramón Huerta, Juan A. Sigüenza, Michael A. Arbib, A. Sierra, José A. Macías, Alex Guazzelli, Mihail Bota, Charles Elkan and Manuel Sánchez-Montañés. Their work appears in journals such as Biological Cybernetics, Adaptive Behavior, Vision Research, Neurocomputing and Neural Networks.

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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