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
  • Letter
  • Open Access

Quenched disorder-induced chaotic resonance: Optimal stimulus response near the edge of chaos

Cong Liu, Zhi-Xi Wu*, and Jian-Yue Guan

  • Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key Laboratory of Quantum Theory and Applications of MoE, Lanzhou University, Lanzhou, Gansu 730000, China and Institute of Computational Physics and Complex Systems, Lanzhou University, Lanzhou, Gansu 730000, China

  • *Contact author: wuzhx@lzu.edu.cn

Phys. Rev. Research 6, L042019 – Published 22 October, 2024

DOI: https://doi.org/10.1103/PhysRevResearch.6.L042019

Abstract

We study the collective stimulus response in two classical nonlinear dynamical models, the globally coupled bistable oscillators and the excitable FitzHugh-Nagumo neurons, where the coupling strength among the units are randomly distributed with fixed mean and varying variance. We find theoretically and numerically that in both models the ensemble response to a weak periodic stimulus exhibits a bell-shaped amplification manifesting a different type of resonance mechanism, namely, coupling-disorder-induced resonance, as the quenched disorder of coupling increases. Of particular interest is that the optimal collective response emerges near the order-chaos transition point, i.e., at the edge of chaos. As a promising application, we discuss briefly the potential reasonability for exploiting such a type of chaotic resonance to explain the optimal mutual information in the realistic inferior olive. Our results provide explicitly a clue that the disorderly coupled elements may utilize the coupling-disorder-induced chaos to optimize their stimulus response.

View figure in article

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (55)

  1. G. Hermann and J. Touboul, Heterogeneous connections induce oscillations in large-scale networks, Phys. Rev. Lett. 109, 018702 (2012).
  2. I. Aradi and I. Soltesz, Modulation of network behaviour by changes in variance in interneuronal properties, J. Physiol. 538, 227 (2002).
  3. C. Gu, J. Wang, and Z. Liu, Free-running period of neurons in the suprachiasmatic nucleus: Its dependence on the distribution of neuronal coupling strengths, Phys. Rev. E 80, 030904(R) (2009).
  4. G. Losapio, C. Schöb, P. P. A. Staniczenko, F. Carrara, G. M. Palamara, C. M. De Moraes, M. C. Mescher, R. W. Brooker, B. J. Butterfield, R. M. Callaway, L. A. Cavieres, Z. Kikvidze, C. J. Lortie, R. Michalet, F. I. Pugnaire, and J. Bascompte, Network motifs involving both competition and facilitation predict biodiversity in alpine plant communities, Proc. Natl. Acad. Sci. USA 118, e2005759118 (2021).
  5. G. Parisi, Nobel Lecture: Multiple equilibria, Rev. Mod. Phys. 95, 030501 (2023).
  6. S. F. Edwards and P. W. Anderson, Theory of spin glasses, J. Phys. F 5, 965 (1975).
  7. H. Daido, Quasientrainment and slow relaxation in a population of oscillators with random and frustrated interactions, Phys. Rev. Lett. 68, 1073 (1992).
  8. R. May, Will a large complex system be stable? Nature (London) 238, 413 (1972).
  9. H. Sompolinsky, A. Crisanti, and H. J. Sommers, Chaos in random neural networks, Phys. Rev. Lett. 61, 259 (1988).
  10. T. Toyoizumi and L. F. Abbott, Beyond the edge of chaos: Amplification and temporal integration by recurrent networks in the chaotic regime, Phys. Rev. E 84, 051908 (2011).
  11. J. Kadmon and H. Sompolinsky, Transition to chaos in random neuronal networks, Phys. Rev. X 5, 041030 (2015).
  12. J. Schuecker, S. Goedeke, and M. Helias, Optimal sequence memory in driven random networks, Phys. Rev. X 8, 041029 (2018).
  13. R. Legenstein and W. Maass, Edge of chaos and prediction of computational performance for neural circuit models, Neural Netw. 20, 323 (2007).
  14. D. Sussillo and L. F. Abbott, Generating coherent patterns of activity from chaotic neural networks, Neuron 63, 544 (2009).
  15. L. Molgedey, J. Schuchhardt, and H. G. Schuster, Suppressing chaos in neural networks by noise, Phys. Rev. Lett. 69, 3717 (1992).
  16. K. Rajan, L. F. Abbott, and H. Sompolinsky, Stimulus-dependent suppression of chaos in recurrent neural networks, Phys. Rev. E 82, 011903 (2010).
  17. V. Lucarini, Stochastic resonance for nonequilibrium systems, Phys. Rev. E 100, 062124 (2019).
  18. G. Li, R. LeFebre, A. Starman, P. Chappell, A. Mugler, and B. Sun, Temporal signals drive the emergence of multicellular information networks, Proc. Natl. Acad. Sci. USA 119, e2202204119 (2022).
  19. R. L. Badzey and P. Mohanty, Coherent signal amplification in bistable nanomechanical oscillators by stochastic resonance, Nature (London) 437, 995 (2005).
  20. F. Roy, G. Biroli, G. Bunin and C. Cammarota, Numerical implementation of dynamical mean field theory for disordered systems: Application to the Lotka–Volterra model of ecosystems, J. Phys. A: Math. Theor. 52, 484001 (2019).
  21. A. Longtin, A. Bulsara, and F. Moss, Time-interval sequences in bistable systems and the noise-induced transmission of information by sensory neurons, Phys. Rev. Lett. 67, 656 (1991).
  22. P. Jung and P. Hänggi, Amplification of small signals via stochastic resonance, Phys. Rev. A 44, 8032 (1991).
  23. C. J. Tessone, C. R. Mirasso, R. Toral, and J. D. Gunton, Diversity-induced resonance, Phys. Rev. Lett. 97, 194101 (2006).
  24. A. Crisanti and H. Sompolinsky, Path integral approach to random neural networks, Phys. Rev. E 98, 062120 (2018).
  25. X. Chen and R. Dzakpasu, Observed network dynamics from altering the balance between excitatory and inhibitory neurons in cultured networks, Phys. Rev. E 82, 031907 (2010).
  26. C. Liu and X. Liang, Resonance induced by coupling diversity in globally coupled bistable oscillators, Phys. Rev. E 100, 032206 (2019).
  27. R. Benzi, G. Parisi, A. Sutera, and A. Vulpiani, Stochastic resonance in climatic change, Tellus 34, 10 (1982).
  28. C. Nicolis, Stochastic aspects of climatic transitions–response to a periodic forcing, Tellus 34, 1 (1982).
  29. L. Gammaitoni, P. Hänggi, P. Jung, and F. Marchesoni, Stochastic resonance, Rev. Mod. Phys. 70, 223 (1998).
  30. T. L. Carroll and L. M. Pecora, Stochastic resonance and crises, Phys. Rev. Lett. 70, 576 (1993).
  31. T. L. Carroll and L. M. Pecora, Stochastic resonance as a crisis in a period-doubled circuit, Phys. Rev. E 47, 3941 (1993).
  32. See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevResearch.6.L042019, for the details of the linear stability analysis, the maximal Lyapunov exponent, the dynamical cavity method, and the linear response theory, the frequency-matching behavior, the fit of different D and τ0, the frequency effect, and the difference between coupling diversity-induced resonance and the quenched disorder-induced chaotic resonance, which includes [9, 12, 16, 20, 22, 26, 29, 33, 35, 36, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55].
  33. L. Y. Chew, C. Ting, and C. H. Lai, Chaotic resonance: Two-state model with chaos-induced escape over potential barrier, Phys. Rev. E 72, 036222 (2005).
  34. A. Crisanti, M. Falcioni, G. Paladin, and A. Vulpiani, Stochastic resonance in deterministic chaotic systems, J. Phys. A: Math. Gen. 27, L597 (1994).
  35. P. Hänggi, P. Jung, C. Zerbe, and F. Moss, Can colored noise improve stochastic resonance? J. Stat. Phys. 70, 25 (1993).
  36. P. Hänggi and P. Jung, Colored noise in dynamical systems, edited by I. Prigogine and S. A. Rice (Wiley & Sons, Inc., Hoboken, NJ, 1994), VOl. 89, pp. 239–326.
  37. X. Liang, M. Dhamala, L. Zhao, and Z. Liu, Phase-disorder-induced double resonance of neuronal activity, Phys. Rev. E 82, 010902(R) (2010).
  38. J. Zhu, Y. Kato, and H. Nakao, Phase dynamics of noise-induced coherent oscillations in excitable systems, Phys. Rev. Res. 4, L022041 (2022).
  39. E. I. Volkov, E. Ullner, A. A. Zaikin, and J. Kurths, Frequency-dependent stochastic resonance in inhibitory coupled excitable systems, Phys. Rev. E 68, 061112 (2003).
  40. S. Scialla, A. Loppini, M. Patriarca, and E. Heinsalu, Hubs, diversity, and synchronization in FitzHugh-Nagumo oscillator networks: Resonance effects and biophysical implications, Phys. Rev. E 103, 052211 (2021).
  41. Y. Kobayashi, K. Kawano, A. Takemura, Y. Inoue, T. Kitama, H. Gomi, and M. Kawato, Temporal firing patterns of Purkinje cells in the cerebellar ventral paraflocculus during ocular following responses in monkeys II. Complex spikes, J. Neurophysiol. 80, 832 (1998).
  42. N. Schweighofer, K. Doya, H. Fukai, J. V. Chiron, T. Furukawa, and M. Kawato, Chaos may enhance information transmission in the inferior olive, Proc. Natl. Acad. Sci. USA 101, 4655 (2004).
  43. Z. H. Liu, Chaotic Dynamics Foundation and Its Applications in Brain Functions (Beijing Science Press, Beijing, 2018).
  44. H. Gang, H. Haken, and X. Fagen, Stochastic resonance with sensitive frequency dependence in globally coupled continuous systems, Phys. Rev. Lett. 77, 1925 (1996).
  45. P. Jung, P. Hänggi, and F. Marchesoni, Colored-noise-driven bistable systems, Phys. Rev. A 40, 5447 (1989).
  46. V. Lucarini, G. A. Pavliotis, and N. Zagli, Response theory and phase transitions for the thermodynamic limit of interacting identical systems, Proc. R. Soc. A 476, 20200688 (2020).
  47. N. Zagli, V. Lucarini, and G. A. Pavliotis, Spectroscopy of phase transitions for multiagent systems, Chaos 31, 061103 (2021).
  48. J. F. Luciani and A. D. Verga, Functional integral approach to bistability in the presence of correlated noise, Europhys. Lett. 4, 255 (1987).
  49. P. Hänggi, P. Talkner, and M. Borkovec, Reaction-rate theory: Fifty years after Kramers, Rev. Mod. Phys. 62, 251 (1990).
  50. L. Gammaitoni, F. Marchesoni, and S. Santucci, Stochastic resonance as a bona fide resonance, Phys. Rev. Lett. 74, 1052 (1995).
  51. R. Benzi, Stochastic resonance: From climate to biology, Nonlin. Proc. Geophys. 17, 431 (2010).
  52. D. Nozaki and Y. Yamamoto, Enhancement of stochastic resonance in a FitzHugh-Nagumo neuronal model driven by colored noise, Phys. Lett. A 243, 281 (1998).
  53. C. Liu, Z.-X. Wu, C.-Y. Wang, H.-X. Yang, and J.-Y. Guan, Double resonance induced by group coupling with quenched disorder, Chaos 33, 013114 (2023).
  54. M. I. Dykman and D. Ryvkine, Activated escape of periodically modulated systems, Phys. Rev. Lett. 94, 070602 (2005).
  55. V. N. Smelyanskiy, M. I. Dykman, and B. Golding, Time oscillations of escape rates in periodically driven systems, Phys. Rev. Lett. 82, 3193 (1999).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation