Tamás Roska
- Computer Networks and Communications top 0.5%
- Neural Networks Stability and Synchronization 42
- Artificial Intelligence top 1%
- Neural Networks and Applications 34
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- Cellular Automata and Applications 26
- Cognitive Neuroscience top 5%
- Neural dynamics and brain function 13
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- Advanced Memory and Neural Computing 40
- CCD and CMOS Imaging Sensors 19
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- Neuroscience and Neural Engineering 7
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- Robotics and Sensor-Based Localization 7
- Co-authors
- Leon O. ChuaHyongsuk KimChangju YangMaheshwar Pd. SahFrank S. WerblinCsaba RekeczkyÁkos ZarándyD. Bálya
- Cited by
- Computer Networks and CommunicationsStatistical and Nonlinear PhysicsArtificial Intelligence
- Journals
- Proceedings of the IEEE (1 paper)Annals of the New York Academy of Sciences (1 paper)Sensors and Actuators A Physical (1 paper)
- Partner nations
- HungaryUnited StatesSpain
In The Last Decade
Tamás Roska
125 papers receiving 2.5k citations
Peers
Comparison fields: 5 of 85
- Computer Networks and Communications 1.5k
- Statistical and Nonlinear Physics 547
- Artificial Intelligence 845
- Computational Theory and Mathematics 403
- Cognitive Neuroscience 464
Countries citing papers authored by Tamás Roska
This map shows the geographic impact of Tamás Roska'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 Tamás Roska with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tamás Roska more than expected).
Fields of papers citing papers by Tamás Roska
This network shows the impact of papers produced by Tamás Roska. 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 Tamás Roska. The network helps show where Tamás Roska may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tamás Roska, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | An overview on emerging spatial wave logic for spatial-temporal events via cellular wave computers on flows and patterns | 2008 | 2 |
| 2 | Holistic feature extraction from handwritten words on wave computers | 2004 | 4 |
| 3 | Immune response inspired CNN algorithms for many-target detection | 2003 | 3 |
| 4 | Intimate integration of shape codes and linguistic framework in handwriting recognition via wave computers | 2003 | 1 |
| 5 | Stability of multi-layer cellular neural/non-linear networks including a 2-layer complex cell CNN-UM | 2003 | 1 |
| 6 | Proactive, adaptive, cellular sensory-computer architecture via extending the CNN univesal machine | 2003 | 3 |
| 7 | An advanced joint Fourier transform correlator (JTC) | 2001 | 7 |
| 8 | Programmable opto-electronic CNN implementation provides a new and powerful tool for image processing applications. (Research report of the Analogical and Neural Computing Laboratory DNS-9-2001.) | 2001 | 1 |
| 9 | 20 msec focal plane image processing | 2000 | 1 |
| 10 | Dennis Gabor as the initiator of optical computing: Importance and prospects of optical computing and an optical implementation of the CNN-UM computer | 2000 | 3 |
| 11 | Analogic CNN algorithms in bronchogenic carcinoma analysis | 1995 | 3 |
| 12 | Classes of analogic CNN algorithms and their practical use in complex image processing tasks | 1995 | 11 |
| 13 | Smart image scanning algorithms for the CNN universal machine | 1995 | 17 |
| 14 | Translating neuromorphic CNN visual models to the analogic visual microprocessors | 1995 | 1 |
| 15 | Novel types of analogic CNN algorithms for recognizing bank-notes. (Memorandum UCB/ERL M94/29.) | 1994 | 1 |
| 16 | The use of CNN models in the visual parthway. Part II: The amacrine cell in the modified retina model, simple LGN effects and motion related illusions. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-9-1992.) | 1992 | 2 |
| 17 | The CNN universal machine Part 1: The architecture | 1992 | 9 |
| 18 | Stability of cellular neural networks with dominant nonlinear and delay-type templates. (Memo UCB/ERL No. M92/121.) | 1992 | 1 |
| 19 | Programmable cellular neural networks - a state-of-the-art. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-1-1992.) | 1992 | 1 |
| 20 | DUALCOMP dual CNN compiler to CNN-HAC1 board. Version 2.0. 1992. User's guide | 1992 | 1 |
About Tamás Roska
Tamás Roska is a scholar working on Computational Theory and Mathematics, Computer Networks and Communications and Artificial Intelligence, having authored 130 papers that have together received 2.6k indexed citations. Recurring topics across this work include Neural Networks Stability and Synchronization (42 papers), Advanced Memory and Neural Computing (40 papers), Neural Networks and Applications (34 papers), Cellular Automata and Applications (26 papers), CCD and CMOS Imaging Sensors (19 papers), Neural dynamics and brain function (13 papers), Neuroscience and Neural Engineering (7 papers) and Robotics and Sensor-Based Localization (7 papers). The work is most often cited by research in Computer Networks and Communications (1.5k citations), Statistical and Nonlinear Physics (547 citations) and Artificial Intelligence (845 citations). Tamás Roska has collaborated with scholars based in Hungary, United States and Spain. Frequent co-authors include Leon O. Chua, Hyongsuk Kim, Changju Yang, Maheshwar Pd. Sah, Frank S. Werblin, Csaba Rekeczky, Ákos Zarándy, D. Bálya, Akio Ushida and Tamás Zsedrovits. Their work appears in journals such as Proceedings of the IEEE, Annals of the New York Academy of Sciences and Sensors and Actuators A Physical.
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.