Abstract
Noise-induced transitions from chaos to order in nonlinear systems with crisis bifurcations are studied. In this study, a discrete-time Rulkov system is used as a conceptual model of the neuronal activity. We investigate probabilistic mechanisms of noise-induced transitions from chaotic spiking to quiescence in zones of crisis bifurcations. To analyze these transitions parametrically, we apply a mathematical technique based on the stochastic sensitivity functions and confidence domains. A stochastic phenomenon of the shifts of crisis bifurcation points and the expansion of the order window under increasing noise is discussed and analyzed. Using our analytical approach, we construct a parametric description of chaotic and regular regimes for the randomly forced Rulkov model.
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