K. Pyragas

8.1k total citations · 1 hit paper
105 papers, 6.1k citations indexed

About

K. Pyragas is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications and Cognitive Neuroscience. According to data from OpenAlex, K. Pyragas has authored 105 papers receiving a total of 6.1k indexed citations (citations by other indexed papers that have themselves been cited), including 79 papers in Statistical and Nonlinear Physics, 77 papers in Computer Networks and Communications and 22 papers in Cognitive Neuroscience. Recurrent topics in K. Pyragas's work include Nonlinear Dynamics and Pattern Formation (75 papers), Chaos control and synchronization (59 papers) and stochastic dynamics and bifurcation (30 papers). K. Pyragas is often cited by papers focused on Nonlinear Dynamics and Pattern Formation (75 papers), Chaos control and synchronization (59 papers) and stochastic dynamics and bifurcation (30 papers). K. Pyragas collaborates with scholars based in Lithuania, Germany and Russia. K. Pyragas's co-authors include A. Tamaševičius, Viktoras Pyragas, A. Namajūnas, A. Kittel, Jürgen Parisi, Peter A. Tass, John L. Hudson, István Z. Kiss, Antanas Čenys and G. Mykolaitis and has published in prestigious journals such as Physical Review Letters, Physical review. B, Condensed matter and Scientific Reports.

In The Last Decade

K. Pyragas

101 papers receiving 5.8k citations

Hit Papers

Continuous control of chaos by self-controlling feedback 1992 2026 2003 2014 1992 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Continuous control of chaos by self-controlling feedback
    1992 2.5k citations K. Pyragas Physics Letters A profile →

Peers

K. Pyragas
Tomasz Kapitaniak Poland
Christos Volos Greece
Thomas L. Carroll United States
В. С. Анищенко Russia
Н. В. Кузнецов Russia
Viet–Thanh Pham Vietnam
Santo Banerjee Italy
Louis M. Pecora United States
Sajad Jafari Iran
Bernd Krauskopf United Kingdom
Tomasz Kapitaniak Poland
K. Pyragas
Citations per year, relative to K. Pyragas K. Pyragas (= 1×) peers Tomasz Kapitaniak

Countries citing papers authored by K. Pyragas

Since Specialization
Citations

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

Fields of papers citing papers by K. Pyragas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of K. Pyragas

This figure shows the co-authorship network connecting the top 25 collaborators of K. Pyragas. A scholar is included among the top collaborators of K. Pyragas 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 K. Pyragas. K. Pyragas 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.
Pyragas, Viktoras & K. Pyragas. (2024). Mean-field models of neural populations with Gaussian noise and non-Cauchy heterogeneities. Physical review. E. 110(6). 64211–64211.
2.
Pyragas, Viktoras & K. Pyragas. (2023). Effect of Cauchy noise on a network of quadratic integrate-and-fire neurons with non-Cauchy heterogeneities. Physics Letters A. 480. 128972–128972. 11 indexed citations
3.
Pyragas, Viktoras & K. Pyragas. (2022). Mean-field equations for neural populations with q-Gaussian heterogeneities. Physical review. E. 105(4). 13 indexed citations
4.
Pyragas, K., et al.. (2022). Interplay of different synchronization modes and synaptic plasticity in a system of class I neurons. Scientific Reports. 12(1). 19631–19631. 2 indexed citations
5.
Pyragas, K., et al.. (2021). Multistability in a star network of Kuramoto-type oscillators with synaptic plasticity. Scientific Reports. 11(1). 9840–9840. 13 indexed citations
6.
Pyragas, K., et al.. (2021). Suppression of synchronous spiking in two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons. Physical review. E. 104(1). 14203–14203. 10 indexed citations
7.
Pyragas, K. & Peter A. Tass. (2017). Suppression of spontaneous oscillations in high-frequency stimulated neuron models. Lithuanian Journal of Physics. 56(4). 223–238. 8 indexed citations
8.
Pyragas, K., et al.. (2017). Symmetry breaking in two interacting populations of quadratic integrate-and-fire neurons. Physical review. E. 96(4). 42212–42212. 16 indexed citations
9.
Pyragas, K., et al.. (2016). Macroscopic self-oscillations and aging transition in a network of synaptically coupled quadratic integrate-and-fire neurons. Physical review. E. 94(3). 32215–32215. 41 indexed citations
10.
Pyragas, K., et al.. (2013). Mechanism of suppression of sustained neuronal spiking under high-frequency stimulation. Biological Cybernetics. 107(6). 669–684. 32 indexed citations
11.
Pyragas, K., et al.. (2012). Pulse propagation and failure in the discrete FitzHugh-Nagumo model subject to high-frequency stimulation. Physical Review E. 86(4). 46211–46211. 10 indexed citations
12.
Pyragas, K., et al.. (2009). Stochastic resonance in the flow of test particles in the field of a Kerr black hole. Gravitation and Cosmology. 15(3). 247–249.
13.
Pyragas, K. & Viktoras Pyragas. (2009). Using ergodicity of chaotic systems for improving the global properties of the delayed feedback control method. Physical Review E. 80(6). 67201–67201. 14 indexed citations
14.
Pyragas, K., et al.. (2008). Coupling design for a long-term anticipating synchronization of chaos. Physical Review E. 78(4). 46217–46217. 36 indexed citations
15.
Tamaševičius, A., G. Mykolaitis, Viktoras Pyragas, & K. Pyragas. (2007). Delayed feedback control of periodic orbits without torsion in nonautonomous chaotic systems: Theory and experiment. Physical Review E. 76(2). 26203–26203. 46 indexed citations
16.
Benner, H., et al.. (2007). Global Properties in an Experimental Realization of Time-Delayed Feedback Control with an Unstable Control Loop. Physical Review Letters. 98(21). 214102–214102. 30 indexed citations
17.
Pyragas, Viktoras & K. Pyragas. (2006). Delayed feedback control of the Lorenz system: An analytical treatment at a subcritical Hopf bifurcation. Physical Review E. 73(3). 36215–36215. 60 indexed citations
18.
Pyragas, K., Viktoras Pyragas, István Z. Kiss, & John L. Hudson. (2004). Adaptive control of unknown unstable steady states of dynamical systems. Physical Review E. 70(2). 26215–26215. 77 indexed citations
19.
Pyragas, K., Viktoras Pyragas, István Z. Kiss, & John L. Hudson. (2002). Stabilizing and Tracking Unknown Steady States of Dynamical Systems. Physical Review Letters. 89(24). 244103–244103. 66 indexed citations
20.
Pyragas, K.. (2001). Control of Chaos via an Unstable Delayed Feedback Controller. Physical Review Letters. 86(11). 2265–2268. 193 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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