We propose a minimal and analytically tractable class of neural networks, the adaptive Ising class. By inferring the model’s parameters from resting-state brain activity recordings, we show that scale-specific oscillations and scale-free avalanches can coexist in resting brains close to a non-equilibrium critical point at the onset of self-sustained oscillations.
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This is a summary of: Lombardi, F. et al. Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00410-9 (2023).
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Oscillations and avalanches coexist in brain networks close to criticality. Nat Comput Sci 3, 194–195 (2023). https://doi.org/10.1038/s43588-023-00411-8
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DOI: https://doi.org/10.1038/s43588-023-00411-8
