- Physical Review Journals
All JournalsPhysics Magazine

Physical Review Research

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

Noise-induced maximization of integrated information in FitzHugh-Nagumo networks

Zonglun Li1,2,* and Alexey Zaikin1,3,4

  • *Contact author: zonglun.li.20@ucl.ac.uk
PDF

Phys. Rev. Research 8, L012020 – Published 22 January, 2026

DOI: https://doi.org/10.1103/dftc-vzvv

Abstract

Increasing evidence suggests that consciousness is linked to a system's ability to integrate information. Integrated information measures how much information a system holds beyond the sum of its independent parts. In this study, we explore how noise can maximize integrated information in small-scale FitzHugh-Nagumo neural networks. This noise-driven optimization occurs in both oscillatory and excitable regimes, as well as at the bifurcation point. Furthermore, we observe a noise-induced correlation between integrated information and coherence, and notably, the maximization of integrated information is accompanied by disparate coherence patterns in different regimes. Our findings provide insights into how noise can enhance information processing and potentially consciousness in neural systems and reveal a connection between information integration and the regularity of neural activity.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (37)

  1. E. R. John, The neurophysics of consciousness, Brain Res. Rev. 39, 1 (2002).
  2. G. Tononi and C. Koch, Consciousness: Here, there and everywhere? Philos. Trans. R. Soc. B 370, 20140167 (2015).
  3. A. K. Seth and T. Bayne, Theories of consciousness, Nat. Rev. Neurosci. 23, 439 (2022).
  4. D. Balduzzi and G. Tononi, Integrated information in discrete dynamical systems: Motivation and theoretical framework, PLoS Comput. Biol. 4, e1000091 (2008).
  5. C. Koch, M. Massimini, M. Boly, and G. Tononi, Neural correlates of consciousness: Progress and problems, Nat. Rev. Neurosci. 17, 307 (2016).
  6. G. Tononi, M. Boly, M. Massimini, and C. Koch, Integrated information theory: From consciousness to its physical substrate, Nat. Rev. Neurosci. 17, 450 (2016).
  7. P. A. Mediano, F. E. Rosas, D. Bor, A. K. Seth, and A. B. Barrett, The strength of weak integrated information theory, Trends Cognit. Sci. 26, 646 (2022).
  8. F. Ellia and R. Chis-Ciure, Consciousness and complexity: Neurobiological naturalism and integrated information theory, Conscious. Cognit. 100, 103281 (2022).
  9. J. D. Murray, A. Bernacchia, N. A. Roy, C. Constantinidis, R. Romo, and X.-J. Wang, Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex, Proc. Natl. Acad. Sci. USA 114, 394 (2017).
  10. F. Bouchacourt and T. J. Buschman, A flexible model of working memory, Neuron 103, 147 (2019).
  11. S. Jia, Z. Yu, A. Onken, Y. Tian, T. Huang, and J. K. Liu, Neural system identification with spike-triggered non-negative matrix factorization, IEEE Trans. Cybern. 52, 4772 (2021).
  12. Z. Li, A. Andreev, A. Hramov, O. Blyuss, and A. Zaikin, Novel efficient reservoir computing methodologies for regular and irregular time series classification, Nonlinear Dyn. 113, 4045 (2025).
  13. B. Feulner, M. G. Perich, L. E. Miller, C. Clopath, and J. A. Gallego, A neural implementation model of feedback-based motor learning, Nat. Commun. 16, 1805 (2025).
  14. O. Kanakov, S. Gordleeva, A. Ermolaeva, S. Jalan, and A. Zaikin, Astrocyte-induced positive integrated information in neuron-astrocyte ensembles, Phys. Rev. E 99, 012418 (2019).
  15. I. A. Nazhestkin and O. E. Svarnik, Integrated information coefficient estimated from neuronal activity in hippocampus-amygdala complex of rats as a measure of learning success, J. Integr. Neurosci. 21, 128 (2022).
  16. C. Consortium, O. Ferrante, U. Gorska-Klimowska, S. Henin, R. Hirschhorn, A. Khalaf, A. Lepauvre, L. Liu, D. Richter, Y. Vidal, et al., Adversarial testing of global neuronal workspace and integrated information theories of consciousness, Nature (London) 642, 133 (2025).
  17. S. Liu, M.-R. Li, S.-X. Zhang, S.-K. Jian, and H. Yao, Noise-induced phase transitions in hybrid quantum circuits, Phys. Rev. B 110, 064323 (2024).
  18. G. Lin, Z. Han, A. Shee, and C. Huepe, Noise-induced quenched disorder in dense active systems, Phys. Rev. Lett. 131, 168301 (2023).
  19. W. Zhang, P. Shi, M. Li, and D. Han, A novel stochastic resonance model based on bistable stochastic pooling network and its application, Chaos Solitons Fractals 145, 110800 (2021).
  20. T. Palabas, J. J. Torres, M. Perc, and M. Uzuntarla, Double stochastic resonance in neuronal dynamics due to astrocytes, Chaos Solitons Fractals 168, 113140 (2023).
  21. A. Zhao, A. Ermolaeva, E. Ullner, J. Kurths, S. Gordleeva, and A. Zaikin, Noise-induced artificial intelligence, Phys. Rev. Res. 4, 043069 (2022).
  22. A. S. Pikovsky and J. Kurths, Coherence resonance in a noise-driven excitable system, Phys. Rev. Lett. 78, 775 (1997).
  23. A. V. Andreev, V. V. Makarov, A. E. Runnova, A. N. Pisarchik, and A. E. Hramov, Coherence resonance in stimulated neuronal network, Chaos Solitons Fractals 106, 80 (2018).
  24. A. N. Pisarchik and A. E. Hramov, Coherence resonance in neural networks: Theory and experiments, Phys. Rep. 1000, 1 (2023).
  25. C. Rocşoreanu, A. Georgescu, and N. Giurgiţeanu, The FitzHugh-Nagumo Model: Bifurcation and Dynamics (Springer, Dordrecht, 2012), Vol. 10.
  26. P. A. Mediano, A. K. Seth, and A. B. Barrett, Measuring integrated information: Comparison of candidate measures in theory and simulation, Entropy 21, 17 (2018).
  27. L. Abrego, S. Gordleeva, O. Kanakov, M. Krivonosov, and A. Zaikin, Estimating integrated information in bidirectional neuron-astrocyte communication, Phys. Rev. E 103, 022410 (2021).
  28. M. Oizumi, S.-I. Amari, T. Yanagawa, N. Fujii, and N. Tsuchiya, Measuring integrated information from the decoding perspective, PLoS Comput. Biol. 12, e1004654 (2016).
  29. M. Oizumi, T. Ishii, K. Ishibashi, T. Hosoya, and M. Okada, Mismatched decoding in the brain, J. Neurosci. 30, 4815 (2010).
  30. S. Hidaka and M. Oizumi, Fast and exact search for the partition with minimal information loss, PLoS One 13, e0201126 (2018).
  31. M. Klincewicz, T. Cheng, M. Schmitz, M. Á. Sebastián, and J. S. Snyder, What makes a theory of consciousness unscientific? Nat. Neurosci. 28, 689 (2025).
  32. G. Tononi, L. Albantakis, L. Barbosa, M. Boly, C. Cirelli, R. Comolatti, F. Ellia, G. Findlay, A. G. Casali, M. Grasso, et al., Consciousness or pseudo-consciousness? A clash of two paradigms, Nat. Neurosci. 28, 694 (2025).
  33. A. Gomez-Marin and A. K. Seth, A science of consciousness beyond pseudo-science and pseudo-consciousness, Nat. Neurosci. 28, 703 (2025).
  34. F. Oschmann, H. Berry, K. Obermayer, and K. Lenk, From in silico astrocyte cell models to neuron-astrocyte network models: A review, Brain Res. Bull. 136, 76 (2018).
  35. Z. Li, Y. Tsybina, S. Gordleeva, and A. Zaikin, Impact of astrocytic coverage of synapses on the short-term memory of a computational neuron-astrocyte network, Mathematics 10, 3275 (2022).
  36. J. Lorenzo, J.-A. Rico-Gallego, S. Binczak, and S. Jacquir, Spiking neuron-astrocyte networks for image recognition, Neural Comput. 37, 635 (2025).
  37. https://github.com/zonglunli7515/Noise-induced-maximization-of-integrated-information

Outline

Information

Phys. Rev. Research 8, L012020– Published 22 January, 2026
  • Received 27 October 2025
  • Accepted 31 December 2025
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation