Roman Rybka

580 total citations
54 papers, 330 citations indexed

About

Roman Rybka is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Cognitive Neuroscience. According to data from OpenAlex, Roman Rybka has authored 54 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Artificial Intelligence, 19 papers in Electrical and Electronic Engineering and 17 papers in Cognitive Neuroscience. Recurrent topics in Roman Rybka's work include Advanced Memory and Neural Computing (19 papers), Topic Modeling (18 papers) and Neural dynamics and brain function (17 papers). Roman Rybka is often cited by papers focused on Advanced Memory and Neural Computing (19 papers), Topic Modeling (18 papers) and Neural dynamics and brain function (17 papers). Roman Rybka collaborates with scholars based in Russia, China and Taiwan. Roman Rybka's co-authors include Alexander Sboev, Tatiana Litvinova, В. А. Демин, Nikolay A. Kudryashov, A. V. Emelyanov, А. В. Ситников, Anton Selivanov, K. E. Nikiruy, V. V. Rylkov and M. V. Kovalchuk and has published in prestigious journals such as Nanotechnology, Neural Networks and Chaos Solitons & Fractals.

In The Last Decade

Roman Rybka

49 papers receiving 320 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roman Rybka Russia 10 164 159 84 79 33 54 330
Yanghao Wang China 11 125 0.8× 398 2.5× 175 2.1× 139 1.8× 38 1.2× 19 506
Ethan Farquhar United States 10 83 0.5× 261 1.6× 111 1.3× 129 1.6× 6 0.2× 19 333
Yaoyu Tao China 11 79 0.5× 253 1.6× 30 0.4× 22 0.3× 20 0.6× 32 336
Maruan Al-Shedivat United States 9 165 1.0× 313 2.0× 134 1.6× 146 1.8× 9 0.3× 14 421
Luís F. Seoane Spain 10 73 0.4× 56 0.4× 33 0.4× 90 1.1× 3 0.1× 28 246
Hogun Park South Korea 9 52 0.3× 18 0.1× 14 0.2× 25 0.3× 29 0.9× 31 214
Giovanni E. Pazienza Hungary 8 53 0.3× 162 1.0× 65 0.8× 51 0.6× 3 0.1× 34 244
Raqibul Hasan United States 16 158 1.0× 585 3.7× 253 3.0× 100 1.3× 4 0.1× 36 650
Peng Qu China 8 88 0.5× 242 1.5× 82 1.0× 65 0.8× 17 0.5× 18 320
Zebang Shen China 11 112 0.7× 228 1.4× 46 0.5× 18 0.2× 81 2.5× 34 454

Countries citing papers authored by Roman Rybka

Since Specialization
Citations

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

Fields of papers citing papers by Roman Rybka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roman Rybka

This figure shows the co-authorship network connecting the top 25 collaborators of Roman Rybka. A scholar is included among the top collaborators of Roman Rybka 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 Roman Rybka. Roman Rybka 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
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Rybka, Roman, et al.. (2024). Spiking neural network with local plasticity and sparse connectivity for audio classification. Izvestiya VUZ Applied Nonlinear Dynamics.
3.
Sboev, Alexander, et al.. (2024). Analysis of neural network methods for obtaining soliton solutions of the nonlinear Schrödinger equation. Chaos Solitons & Fractals. 192. 115943–115943. 3 indexed citations
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Sboev, Alexander, et al.. (2023). A deep learning method based on language models for processing natural language Russian commands in human robot interaction. Open Access Repository (Belgorod State National Research University). 9(1). 1 indexed citations
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Миннеханов, А. А., et al.. (2023). Memristor-based spiking neural network with online reinforcement learning. Neural Networks. 166. 512–523. 13 indexed citations
9.
Rybka, Roman, et al.. (2023). Named Entity-Oriented Sentiment Analysis with text2text Generation Approach. 3 indexed citations
10.
Sboev, Alexander, et al.. (2022). On the accuracy of Covid-19 forecasting methods in Russia for two years. Procedia Computer Science. 213. 428–434. 1 indexed citations
11.
Sboev, Alexander, et al.. (2022). Data-driven model for identifying related pharmaceutically-significant entities in clinical texts. AIP conference proceedings. 2425. 340003–340003. 1 indexed citations
12.
Sboev, Alexander, et al.. (2021). Baseline Accuracies of Forecasting COVID-19 Cases in Russian Regions on a Year in Retrospect Using Basic Statistical and Machine Learning Methods. Procedia Computer Science. 193. 276–284. 1 indexed citations
13.
Sboev, Alexander, et al.. (2021). Baseline accuracy of forecasting COVID-19 cases in Moscow region on a year in retrospect using basic statistical and machine learning methods. Journal of Physics Conference Series. 2036(1). 12029–12029. 1 indexed citations
14.
Emelyanov, A. V., K. E. Nikiruy, А. В. Ситников, et al.. (2019). Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights. Nanotechnology. 31(4). 45201–45201. 68 indexed citations
15.
Sboev, Alexander, et al.. (2018). To the role of the choice of the neuron model in spiking network learning on base of Spike-Timing-Dependent Plasticity. Procedia Computer Science. 123. 432–439. 6 indexed citations
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Sboev, Alexander, et al.. (2018). Spiking neural network reinforcement learning method based on temporal coding and STDP. Procedia Computer Science. 145. 458–463. 5 indexed citations
17.
Sboev, Alexander, et al.. (2018). Automatic gender identification of author of Russian text by machine learning and neural net algorithms in case of gender deception. Procedia Computer Science. 123. 417–423. 15 indexed citations
18.
Kudryashov, Nikolay A., Roman Rybka, & Alexander Sboev. (2017). Analytical properties of the perturbed FitzHugh–Nagumo model. Applied Mathematics Letters. 76. 142–147. 24 indexed citations
19.
Sboev, Alexander, et al.. (2017). On the effect of stabilizing mean firing rate of a neuron due to STDP. Procedia Computer Science. 119. 166–173. 3 indexed citations
20.
Sboev, Alexander, et al.. (2016). On the applicability of STDP-based learning mechanisms to spiking neuron network models. AIP Advances. 6(11). 5 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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