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Spiking dynamics of individual neurons reflect changes in the structure and function of neuronal networks

Author

Listed:
  • Ruochen Yang

    (University of Southern California)

  • Heng Ping

    (University of Southern California)

  • Xiongye Xiao

    (University of Southern California
    University of Tennessee, Knoxville (UTK))

  • Roozbeh Kiani

    (New York University
    New York University)

  • Paul Bogdan

    (University of Southern California
    University of Southern California (USC))

Abstract

Brain networks exhibit diverse topological structures to adapt and support brain functions. The changes in neuronal network architecture can lead to alterations in neuronal spiking activity, yet how individual neuronal behavior reflects network structure remains unexplored. Therefore, mathematical tools to decode and infer neuronal network structure and role from spiking behavior need to be developed to relate the neuronal firing activity with topology and goal of underlying network. Toward this end, we perform a comprehensive multifractal analysis of the neuronal interspike intervals to characterize their non-linear, non-stationary and non-Markovian dynamics. We explore the relationship of neuronal network connectivity with the multifractal spiking pattern and show that such a measure is sensitive to network structure while relatively consistent to stimulus. In addition, we reveal that the observed multifractal profile is not influenced by the activity of unobserved neuronal ensembles. To mimic neurons performing specific functions, we further train spiking neural networks to generate goal-directed architectures and demonstrate that multifractal analysis also enables differentiating networks with diverse tasks.

Suggested Citation

  • Ruochen Yang & Heng Ping & Xiongye Xiao & Roozbeh Kiani & Paul Bogdan, 2025. "Spiking dynamics of individual neurons reflect changes in the structure and function of neuronal networks," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62202-1
    DOI: 10.1038/s41467-025-62202-1
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    References listed on IDEAS

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    1. Rambaldi, Sandro & Pinazza, Ombretta, 1994. "An accurate fractional Brownian motion generator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 208(1), pages 21-30.
    2. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    3. Joshua H. Siegle & Xiaoxuan Jia & Séverine Durand & Sam Gale & Corbett Bennett & Nile Graddis & Greggory Heller & Tamina K. Ramirez & Hannah Choi & Jennifer A. Luviano & Peter A. Groblewski & Ruweida , 2021. "Survey of spiking in the mouse visual system reveals functional hierarchy," Nature, Nature, vol. 592(7852), pages 86-92, April.
    4. James Trousdale & Yu Hu & Eric Shea-Brown & Krešimir Josić, 2012. "Impact of Network Structure and Cellular Response on Spike Time Correlations," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-15, March.
    5. Stojan Jovanović & Stefan Rotter, 2016. "Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-28, June.
    6. Lu Bin Liu & Attila Losonczy & Zhenrui Liao, 2022. "tension: A Python package for FORCE learning," PLOS Computational Biology, Public Library of Science, vol. 18(12), pages 1-12, December.
    7. Gabriel Koch Ocker & Krešimir Josić & Eric Shea-Brown & Michael A Buice, 2017. "Linking structure and activity in nonlinear spiking networks," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-47, June.
    8. Arian Ashourvan & Qawi K Telesford & Timothy Verstynen & Jean M Vettel & Danielle S Bassett, 2019. "Multi-scale detection of hierarchical community architecture in structural and functional brain networks," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-36, May.
    9. Wilten Nicola & Claudia Clopath, 2017. "Supervised learning in spiking neural networks with FORCE training," Nature Communications, Nature, vol. 8(1), pages 1-15, December.
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