Fernando Perez‐Peña

495 total citations
40 papers, 295 citations indexed

About

Fernando Perez‐Peña is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Fernando Perez‐Peña has authored 40 papers receiving a total of 295 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Electrical and Electronic Engineering, 23 papers in Cellular and Molecular Neuroscience and 18 papers in Cognitive Neuroscience. Recurrent topics in Fernando Perez‐Peña's work include Advanced Memory and Neural Computing (27 papers), Neuroscience and Neural Engineering (22 papers) and Neural dynamics and brain function (16 papers). Fernando Perez‐Peña is often cited by papers focused on Advanced Memory and Neural Computing (27 papers), Neuroscience and Neural Engineering (22 papers) and Neural dynamics and brain function (16 papers). Fernando Perez‐Peña collaborates with scholars based in Spain, Germany and Switzerland. Fernando Perez‐Peña's co-authors include Alejandro Linares-Barranco, Ángel Jiménez-Fernández, Arturo Morgado‐Estévez, G. Jiménez, Elisabetta Chicca, Juan P. Dominguez‐Morales, F. Gómez-Rodríguez, Daniel Gutiérrez-Galán, J. López-Coronado and Pedro L. Galindo and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.

In The Last Decade

Fernando Perez‐Peña

38 papers receiving 288 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fernando Perez‐Peña Spain 10 224 113 109 49 31 40 295
Gianluca Susi Spain 10 119 0.5× 102 0.9× 52 0.5× 58 1.2× 17 0.5× 33 261
J. Camilo Vasquez Tieck Germany 9 110 0.5× 78 0.7× 49 0.4× 30 0.6× 30 1.0× 14 166
Lyes Khacef Switzerland 8 177 0.8× 122 1.1× 53 0.5× 75 1.5× 49 1.6× 13 268
Antonio Díaz Estrella Spain 10 145 0.6× 173 1.5× 79 0.7× 20 0.4× 50 1.6× 51 393
Stefan Scholze Germany 12 345 1.5× 143 1.3× 166 1.5× 83 1.7× 46 1.5× 32 416
Yaoyuan Wang China 11 261 1.2× 62 0.5× 118 1.1× 62 1.3× 10 0.3× 24 378
R. Paz-Vicente Spain 8 430 1.9× 211 1.9× 262 2.4× 62 1.3× 20 0.6× 21 469
Alan G. Millard United Kingdom 9 110 0.5× 71 0.6× 84 0.8× 36 0.7× 16 0.5× 18 211
Rodolphe Héliot France 11 281 1.3× 185 1.6× 196 1.8× 54 1.1× 110 3.5× 29 444

Countries citing papers authored by Fernando Perez‐Peña

Since Specialization
Citations

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

Fields of papers citing papers by Fernando Perez‐Peña

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fernando Perez‐Peña

This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Perez‐Peña. A scholar is included among the top collaborators of Fernando Perez‐Peña 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 Fernando Perez‐Peña. Fernando Perez‐Peña 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.
Perez‐Peña, Fernando, et al.. (2025). Modelling and Identification of a Tracked Mobile Robot: A Real-time Feasible Approach Using Particle Swarm Optimization and Information Criteria. International Journal of Control Automation and Systems. 23(9). 2687–2703. 1 indexed citations
2.
Dominguez‐Morales, Juan P., et al.. (2024). A Neuromorphic Vision and Feedback Sensor Fusion Based on Spiking Neural Networks for Real‐Time Robot Adaption. SHILAP Revista de lepidopterología. 6(5). 3 indexed citations
3.
Dominguez‐Morales, Juan P., et al.. (2024). Integrating a hippocampus memory model into a neuromorphic robotic-arm for trajectory navigation. 1–5. 1 indexed citations
4.
Blasé, Christopher, et al.. (2024). Modelling and simulation of a commercially available dielectric elastomer actuator. Smart Materials and Structures. 33(2). 25030–25030.
5.
Rau, Jens, et al.. (2024). Fuel saving effect and performance of velocity control for modern combustion-powered scooters. Control Engineering Practice. 145. 105849–105849. 1 indexed citations
6.
Perez‐Peña, Fernando, et al.. (2024). ETLP: event-based three-factor local plasticity for online learning with neuromorphic hardware. SHILAP Revista de lepidopterología. 4(3). 34006–34006. 3 indexed citations
7.
Sánchez, Clemente Cobos, et al.. (2023). Exploiting the PIR Sensor Analog Behavior as Thermoreceptor: Movement Direction Classification Based on Spiking Neurons. Sensors. 23(13). 5816–5816. 1 indexed citations
8.
Perez‐Peña, Fernando, et al.. (2023). A Portable Real-Time Test Bench for Dielectric Elastomer Actuators. Machines. 11(3). 380–380. 1 indexed citations
9.
Luna-Perejón, Francisco, et al.. (2023). Smart Shoe Insole Based on Polydimethylsiloxane Composite Capacitive Sensors. Sensors. 23(3). 1298–1298. 11 indexed citations
10.
Perez‐Peña, Fernando, et al.. (2023). Applying active learning by contextualizing robotic applications to historical heritage. Computer Applications in Engineering Education. 32(1). 1 indexed citations
11.
Dominguez‐Morales, Juan P., et al.. (2023). NESIM-RT: A real-time distributed spiking neural network simulator. SoftwareX. 22. 101349–101349. 3 indexed citations
12.
Ríos-Navarro, Antonio, et al.. (2023). Towards neuromorphic FPGA-based infrastructures for a robotic arm. Autonomous Robots. 47(7). 947–961. 4 indexed citations
13.
Linares-Barranco, Alejandro, Fernando Perez‐Peña, Ángel Jiménez-Fernández, & Elisabetta Chicca. (2020). ED-BioRob: A Neuromorphic Robotic Arm With FPGA-Based Infrastructure for Bio-Inspired Spiking Motor Controllers. Frontiers in Neurorobotics. 14. 590163–590163. 14 indexed citations
14.
Perez‐Peña, Fernando, et al.. (2019). Digital neuromorphic real-time platform. Neurocomputing. 371. 91–99. 11 indexed citations
15.
Gutiérrez-Galán, Daniel, Juan P. Dominguez‐Morales, Fernando Perez‐Peña, Ángel Jiménez-Fernández, & Alejandro Linares-Barranco. (2019). Neuropod: A real-time neuromorphic spiking CPG applied to robotics. Neurocomputing. 381. 10–19. 42 indexed citations
16.
17.
Donati, Elisa, Fernando Perez‐Peña, Chiara Bartolozzi, Giacomo Indiveri, & Elisabetta Chicca. (2018). Open-Loop Neuromorphic Controller Implemented on VLSI Devices. Data Archiving and Networked Services (DANS). 827–832. 12 indexed citations
18.
Corral, José María Rodríguez, et al.. (2016). Application of robot programming to the teaching of object-oriented computer languages. International journal of engineering education. 32(4). 1823–1832. 8 indexed citations
19.
Gómez-Rodríguez, F., Ángel Jiménez-Fernández, Fernando Perez‐Peña, et al.. (2016). ED-Scorbot: A robotic test-bed framework for FPGA-based neuromorphic systems. 237–242. 3 indexed citations
20.
Perez‐Peña, Fernando, Arturo Morgado‐Estévez, & Alejandro Linares-Barranco. (2014). Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA. Neurocomputing. 149. 496–504. 3 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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