Thomas P. Vogl
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Control and Systems Engineering top 5%
- Biomedical Engineering
- Co-authors
- A. K. RiglerDaniel L. AlkonNour-Eldin A. Nour-EldinBita PanahiKim T. BlackwellJohn M. IrvineN NaguibRenate Hammerstingl
- Topics
- Neural Networks and Applications (12 papers)Advanced Memory and Neural Computing (4 papers)Neural dynamics and brain function (4 papers)
- Partner nations
- United StatesGermanyRomania
In The Last Decade
Thomas P. Vogl
36 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 149
- Artificial Intelligence 506
- Computer Vision and Pattern Recognition 213
- Electrical and Electronic Engineering 175
- Control and Systems Engineering 168
- Biomedical Engineering 160
Countries citing papers authored by Thomas P. Vogl
This map shows the geographic impact of Thomas P. Vogl'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 Thomas P. Vogl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas P. Vogl more than expected).
Fields of papers citing papers by Thomas P. Vogl
This network shows the impact of papers produced by Thomas P. Vogl. 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 Thomas P. Vogl. The network helps show where Thomas P. Vogl may publish in the future.
Co-authorship network of co-authors of Thomas P. Vogl
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas P. Vogl. A scholar is included among the top collaborators of Thomas P. Vogl 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 Thomas P. Vogl. Thomas P. Vogl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Physical Attacks in Dermoscopy: An Evaluation of Robustness for clinical Deep-Learning | 4 |
| 2 | 116 | |
| 3 | 4 | |
| 4 | 15 | |
| 5 | 19 | |
| 6 | 21 | |
| 7 | Biological plausibility of artificial neural networks: learning by non-Hebbian synapses | 2 |
| 8 | 10 | |
| 9 | 9 | |
| 10 | 2 | |
| 11 | 36 | |
| 12 | 1 | |
| 13 | Computer Modeling of Associative Learning | 5 |
| 14 | Accelerating the convergence of the back-propagation methodbreakdown → | 812 |
| 15 | 9 | |
| 16 | 5 | |
| 17 | Recent advances in optimization techniques : proceedings | 1 |
| 18 | 9 | |
| 19 | 15 | |
| 20 | 5 |
About Thomas P. Vogl
Thomas P. Vogl is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 38 papers that have together received 1.4k indexed citations. Recurring topics across this work include Neural Networks and Applications (12 papers), Advanced Memory and Neural Computing (4 papers) and Neural dynamics and brain function (4 papers). The work is most often cited by research in Artificial Intelligence (506 citations), Signal Processing (124 citations) and Computer Vision and Pattern Recognition (213 citations). Thomas P. Vogl has collaborated with scholars based in United States, Germany and Romania. Frequent co-authors include A. K. Rigler, Daniel L. Alkon, Nour-Eldin A. Nour-Eldin, Bita Panahi, Kim T. Blackwell, John M. Irvine, N Naguib, Renate Hammerstingl, Susan A. Werness and Wolfgang Hohenforst‐Schmidt. Their work appears in journals such as Technometrics, Pattern Recognition and Review of Scientific Instruments.
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.