Neurons can detect weak target signals from complex background signals through stochastic resonance (SR) and vibrational\nresonance (VR) mechanisms. However, random phase variation of rapidly fluctuating background signals is generally ignored\nin classical VR or SR studies. Here, the rapidly fluctuating background signals are modeled by bounded noise with random rapidly\nfluctuating phase derived fromWiener process. Then, the influences of bounded noise on the weak signal detection are discussed in\nthe FitzHughââ?¬â??Nagumo (FHN) neuron. Numerical results reveal the occurrence of bounded noise-induced single- and biresonance\nas well as a transition between them. Randomness in phase can enhance the adaptability of neurons, but at the cost of signal\ndetection performance so that neurons can work in more complex environments with a wider frequency range.More interestingly,\nbounded noise with appropriate parameters can not only optimize information transmission but also simultaneously reduce energy\nconsumption. Finally, the potential mechanism of bounded noise is explained.
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