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Article Dans Une Revue Frontiers in Applied Mathematics and Statistics Année : 2019

Phase coherence induced by additive Gaussian and non-Gaussian noise in excitable networks with application to burst suppression-like brain signals

Résumé

It is well-known that additive noise affects the stability of nonlinear systems. Using a network composed of two interacting populations , detailed stochastic and non-linear analysis demonstrates that increasing the intensity of iid additive noise induces a phase transition from a spectrally broad-band state to a phase-coherent oscillatory state. Corresponding coherence resonance-like system behaviour is described analytically for iid noise as well. Stochastic transitions and coherence resonance-like behaviour were also found to occur for non-iid additive noise induced by increased heterogeneity, corresponding analytical results complement the analysis. Finally, the results are applied to burst suppression-like patterns observed in electroencephalographic data under anaesthesia, providing strong evidence that these patterns reflect jumps between random and phase-coherent neural states induced by varying external additive noise levels.
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Dates et versions

hal-02428175 , version 1 (06-01-2020)

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  • HAL Id : hal-02428175 , version 1

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Axel Hutt, Jérémie Lefebvre, Darren Hight, Heiko A Kaiser. Phase coherence induced by additive Gaussian and non-Gaussian noise in excitable networks with application to burst suppression-like brain signals. Frontiers in Applied Mathematics and Statistics, In press. ⟨hal-02428175⟩

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