Fernando J. Pineda
- Molecular Biology top 10%
- Artificial Intelligence top 2%
- Clinical Biochemistry top 2%
- Epidemiology
- Control and Systems Engineering top 5%
- Co-authors
- J. Marie HardwickSarah BermanJeffrey S. LinPlamen A. DemirevCatherine FenselauWen‐Chih ChengBing QiXinchen Teng
- Topics
- Neural Networks and Applications (11 papers)Neural dynamics and brain function (6 papers)Mitochondrial Function and Pathology (5 papers)
- Partner nations
- United StatesVenezuelaGermany
In The Last Decade
Fernando J. Pineda
40 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 158
- Molecular Biology 826
- Artificial Intelligence 761
- Clinical Biochemistry 259
- Epidemiology 205
- Control and Systems Engineering 196
Countries citing papers authored by Fernando J. Pineda
This map shows the geographic impact of Fernando J. Pineda'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 J. Pineda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando J. Pineda more than expected).
Fields of papers citing papers by Fernando J. Pineda
This network shows the impact of papers produced by Fernando J. Pineda. 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 J. Pineda. The network helps show where Fernando J. Pineda may publish in the future.
Co-authorship network of co-authors of Fernando J. Pineda
This figure shows the co-authorship network connecting the top 25 collaborators of Fernando J. Pineda. A scholar is included among the top collaborators of Fernando J. Pineda 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 J. Pineda. Fernando J. Pineda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 20 | |
| 2 | 65 | |
| 3 | 5 | |
| 4 | 128 | |
| 5 | 38 | |
| 6 | 17 | |
| 7 | 17 | |
| 8 | 175 | |
| 9 | 123 | |
| 10 | Temperature variations recorded during interinstitutional air shipments of laboratory mice. | 13 |
| 11 | 55 | |
| 12 | 6 | |
| 13 | 2 | |
| 14 | 75 | |
| 15 | Bangs, Clicks, Snaps, Thuds and Whacks: An Architecture for Acoustic Transient Processing | 3 |
| 16 | Recurrent backpropagation networks | 9 |
| 17 | An Analog Neural Network Inspired by Fractal Block Coding | 1 |
| 18 | Time Dependent Adaptive Neural Networks | 18 |
| 19 | 131 | |
| 20 | Generalization of Back propagation to Recurrent and Higher Order Neural Networks | 84 |
About Fernando J. Pineda
Fernando J. Pineda is a scholar working on Clinical Biochemistry, Artificial Intelligence and Signal Processing, having authored 40 papers that have together received 2.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (11 papers), Neural dynamics and brain function (6 papers) and Mitochondrial Function and Pathology (5 papers). The work is most often cited by research in Clinical Biochemistry (259 citations), Artificial Intelligence (761 citations) and Molecular Biology (826 citations). Fernando J. Pineda has collaborated with scholars based in United States, Venezuela and Germany. Frequent co-authors include J. Marie Hardwick, Sarah Berman, Jeffrey S. Lin, Plamen A. Demirev, Catherine Fenselau, Wen‐Chih Cheng, Bing Qi, Xinchen Teng, J. Michael McCaffery and Elizabeth A. Jonas. Their work appears in journals such as Physical Review Letters, The Journal of Cell Biology and Molecular Cell.
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.