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Metric framework of coherent activity patterns identification in spiking neuronal networks

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  • Radushev, Daniil
  • Dogonasheva, Olesia
  • Gutkin, Boris
  • Zakharov, Denis

Abstract

Partial synchronization plays a crucial role in the functioning of neuronal networks: selective, coordinated activation of neurons enables information processing that flexibly adapts to a changing computational context. Since the structure of coherent activity patterns reflects the network’s current state, developing automated tools to identify them is a key challenge in neurodynamics. Existing methods for analyzing neuronal dynamics tend to focus on global characteristics of the network, such as its aggregated synchrony level. While this approach can distinguish between the network’s main dynamical states, it cannot reveal the localization or properties of distinct coherent patterns.

Suggested Citation

  • Radushev, Daniil & Dogonasheva, Olesia & Gutkin, Boris & Zakharov, Denis, 2026. "Metric framework of coherent activity patterns identification in spiking neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:chsofr:v:203:y:2026:i:c:s0960077925016583
    DOI: 10.1016/j.chaos.2025.117645
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    References listed on IDEAS

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    1. Dogonasheva, Olesia & Kasatkin, Dmitry & Gutkin, Boris & Zakharov, Denis, 2021. "Robust universal approach to identify travelling chimeras and synchronized clusters in spiking networks," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
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    3. Amirhossein Sajadi & Rick Wallace Kenyon & Bri-Mathias Hodge, 2022. "Synchronization in electric power networks with inherent heterogeneity up to 100% inverter-based renewable generation," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
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