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Volume 2018 (2018), Article ID 5632650, 10 pages
Research Article

Subthreshold Periodic Signal Detection by Bounded Noise-Induced Resonance in the FitzHugh–Nagumo Neuron

1Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, China
2Institute of Applied Physics, Huazhong Agricultural University, Wuhan, China
3Department of Physics and Institute of Biophysics, Central China Normal University, Wuhan, China
4Nonlinear Research Institute, Baoji University of Arts and Sciences, Baoji, China

Correspondence should be addressed to Ming Yi; moc.qq@486212663

Received 27 October 2017; Revised 12 January 2018; Accepted 24 January 2018; Published 20 February 2018

Academic Editor: Dimitri Volchenkov

Copyright © 2018 Yuangen Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Neurons can detect weak target signals from complex background signals through stochastic resonance (SR) and vibrational resonance (VR) mechanisms. 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–Nagumo (FHN) neuron. Numerical results reveal the occurrence of bounded noise-induced single- 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.