Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Finite-time scaling on low-dimensional map bifurcations

Daniel A. Martin1,2,*, Qian-Yuan Tang3,†, and Dante R. Chialvo1,2,3

  • *Contact author: dmartin@unsam.edu.ar
  • †Contact author: tangqy@hkbu.edu.hk

Phys. Rev. Research 7, 043241 – Published 2 December, 2025

DOI: https://doi.org/10.1103/73cc-j6mx

Abstract

Recent work has introduced the concept of finite-time scaling to characterize bifurcation diagrams at finite times in deterministic discrete dynamical systems, drawing an analogy with finite-size scaling used to study critical behavior in finite systems. In this work, we extend the finite-time scaling approach in several key directions. First, we present numerical results for one-dimensional maps exhibiting period-doubling bifurcations and discontinuous transitions, analyzing selected paradigmatic examples. We then define two observables, the finite-time susceptibility and the finite-time Lyapunov exponent, that also display consistent scaling near bifurcation points. The method is further generalized to special cases of two-dimensional (2D) maps including the 2D Chialvo map, capturing its bifurcation between a fixed point and a periodic orbit, while accounting for discontinuities and asymmetric periodic orbits. These results underscore fundamental connections between temporal and spatial observables in complex systems, suggesting avenues for studying complex dynamical behavior.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (28)

  1. R. May, Simple mathematical models with very complicated dynamics, Nature (London) 261, 459 (1976).
  2. E. Ott, Chaos in Dynamical Systems (Cambridge University Press, Cambridge, 1993).
  3. K. Kaneko, Theory and Applications of Coupled Map Lattices. Nonlinear Science: Theory and Applications (John Wiley & Sons, Chinchester, 1993).
  4. K. Kaneko, Period-doubling of kink-antikink patterns, quasiperiodicity in antiferro-like structures and spatial intermittency in coupled logistic lattice: Towards a prelude of a “field theory of chaos”, Prog. Theor. Phys. 72, 480 (1984).
  5. L. Glass and M. C. Mackey, From Clocks to Chaos: The Rhythms of Life (Princeton University Press, Princeton, New Jersey, 1988).
  6. B. Ibarz, J. M. Casado, and M. A. F. Sanjuán, Map-based models in neuronal dynamics, Phys. Rep. 501, 1 (2011).
  7. M. Girardi-Schappo, M. H. R. Tragtenberg, and O. Kinouchi, A brief history of excitable map-based neurons and neural networks, J. Neurosci. Methods 220, 116 (2013).
  8. A. Corral, L. Alsedà, and J. J. Sardanyés, Finite-time scaling in local bifurcations, Sci. Rep. 8, 11783 (2018).
  9. A. Corral, Universal finite-time scaling in the transcritical, saddle-node, and pitchfork discrete and continuous bifurcations, Chaos: Interdiscip. J. Nonlinear Sci. 35, 013106 (2025).
  10. C. Falcó and A. Corral, Finite-time scaling for epidemic processes with power-law superspreading events, Phys. Rev. E 105, 064122 (2022).
  11. R. Garcia-Millan, F. Font-Clos, and A. A. Corral, Finite-size scaling of survival probability in branching processes, Phys. Rev. E 91, 042122 (2015).
  12. A. Corral, R. Garcia-Millan, and F. F. Font-Clos, Exact derivation of a finite-size scaling law and corrections to scaling in the geometric Galton-Watson process, PLoS ONE 11, e0161586 (2016).
  13. D. R. Chialvo, Generic excitable dynamics on a two-dimensional map, Chaos Solit. Fractals 5, 461 (1995).
  14. For Q > 0 and/or κ even, there is also an unstable solution 2+μQk−1.
  15. S. H. Strogatz, Nonlinear Dynamics and Chaos, 2nd ed. (CRC Press, Boca Raton, Florida, 2018).
  16. Recall that the fixed point satisfies peμ−p=1, and the bifurcation occurs when f′(p*)=−1.
  17. J. R. Chasnov, Applied Linear Algebra and Differential Equations, 4th ed. (LibreTexts, California, 2025).
  18. A. Attanasi, A. Cavagna, L. Del Castello, I. Giardina, S. Melillo, L. Parisi, O. Pohl, B. Rossaro, E. Shen, E. Silvestr, and M. Viale, Finite-size scaling as a way to probe near-criticality in natural swarms, Phys. Rev. Lett. 113, 238102 (2014).
  19. Q. Y. Tang, Y. Y. Zhang, J. Wang, W. Wang, and D. R. Chialvo, Critical fluctuations in the native state of proteins, Phys. Rev. Lett. 118, 088102 (2017).
  20. P. C. Hohenberg and B. I. Halperin, Theory of dynamic critical phenomena, Rev. Mod. Phys. 49, 435 (1977).
  21. T. S. Grigera, Correlation functions as a tool to study collective phenomena in biological systems, J. Phys. Complex. 2, 045016 (2021).
  22. M. Kardar, G. Parisi, and Y.-C. Zhang, Dynamic scaling of growing interfaces, Phys. Rev. Lett. 56, 889 (1986).
  23. A. Cavagna, D. Conti, C. Creato, L. Del Castello, I. Giardina, T. S. Grigera, S. Melillo, L. Parisi, and M. Viale, Dynamic scaling in natural swarms, Nat. Phys. 13, 914 (2017).
  24. S. Camargo, D. A. Martin, E. J. A. Trejo, A. de Florian, M. A. Nowak, S. A. Cannas, T. S Grigera, and D. R. Chialvo, Scale-free correlations in the dynamics of a small (N=10000) cortical network, Phys. Rev. E 108, 034302 (2023).
  25. M. Scheffer, J. Bascompte, W. A. Brock, V. Brovkin, S. R. Carpenter, V. Dakos, H. Held, E. H. van Nes, M. Rietkerk, and G. Sugihara, Early-warning signals for critical transitions, Nature (London) 461, 53 (2009).
  26. S. V George, S. Kachhara, and G. Ambika, Early warning signals for critical transitions in complex systems, Phys. Scr. 98, 072002 (2023).
  27. https://github.com/DanielAlejandroMartin/Finite-Time-Maps.
  28. xκ−1=(κ−1)μ±(κ−1)2μ2+8μκ2κQ.

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation