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However, random phase variation of rapidly fluctuating background signals is generally ignored in classical VR or SR studies. Here, the rapidly fluctuating background signals are modeled by bounded noise with random rapidly fluctuating phase derived from Wiener process. Then, the influences of bounded noise on the weak signal detection are discussed in the FitzHugh\u2013Nagumo (FHN) neuron. Numerical results reveal the occurrence of bounded noise\u2010induced single\u2010 and biresonance as well as a transition between them. Randomness in phase can enhance the adaptability of neurons, but at the cost of signal detection performance so that neurons can work in more complex environments with a wider frequency range. More interestingly, bounded noise with appropriate parameters can not only optimize information transmission but also simultaneously reduce energy consumption. Finally, the potential mechanism of bounded noise is explained.<\/jats:p>","DOI":"10.1155\/2018\/5632650","type":"journal-article","created":{"date-parts":[[2018,2,20]],"date-time":"2018-02-20T23:31:23Z","timestamp":1519169483000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Subthreshold Periodic Signal Detection by Bounded Noise\u2010Induced Resonance in the FitzHugh\u2013Nagumo 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