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Optimising threshold levels for information transmission in binary threshold networks: Independent multiplicative noise on each threshold

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  • Zhou, Bingchang
  • McDonnell, Mark D.

Abstract

The problem of optimising the threshold levels in multilevel threshold system subject to multiplicative Gaussian and uniform noise is considered. Similar to previous results for additive noise, we find a bifurcation phenomenon in the optimal threshold values, as the noise intensity changes. This occurs when the number of threshold units is greater than one. We also study the optimal thresholds for combined additive and multiplicative Gaussian noise, and find that all threshold levels need to be identical to optimise the system when the additive noise intensity is a constant. However, this identical value is not equal to the signal mean, unlike the case of additive noise. When the multiplicative noise intensity is instead held constant, the optimal threshold levels are not all identical for small additive noise intensity but are all equal to zero for large additive noise intensity. The model and our results are potentially relevant for sensor network design and understanding neurobiological sensory neurons such as in the peripheral auditory system.

Suggested Citation

  • Zhou, Bingchang & McDonnell, Mark D., 2015. "Optimising threshold levels for information transmission in binary threshold networks: Independent multiplicative noise on each threshold," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 659-667.
    • Handle: RePEc:eee:phsmap:v:419:y:2015:i:c:p:659-667
    DOI: 10.1016/j.physa.2014.10.074
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    References listed on IDEAS

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    1. Tuckwell, Henry C. & Jost, Jürgen, 2012. "Analysis of inverse stochastic resonance and the long-term firing of Hodgkin–Huxley neurons with Gaussian white noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5311-5325.
    2. Yong Xu & Juanjuan Li & Jing Feng & Huiqing Zhang & Wei Xu & Jinqiao Duan, 2013. "Lévy noise-induced stochastic resonance in a bistable system," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(5), pages 1-7, May.
    3. Hasegawa, Hideo, 2013. "Stochastic resonance in bistable systems with nonlinear dissipation and multiplicative noise: A microscopic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2532-2546.
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    Cited by:

    1. Liu, Jian & Cao, Jie & Wang, Youguo & Hu, Bing, 2019. "Asymmetric stochastic resonance in a bistable system driven by non-Gaussian colored noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 321-336.
    2. Liu, Jian & Wang, Youguo, 2018. "Performance investigation of stochastic resonance in bistable systems with time-delayed feedback and three types of asymmetries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 359-369.
    3. Xu, Liyan & Duan, Fabing & Abbott, Derek & McDonnell, Mark D., 2016. "Optimal weighted suprathreshold stochastic resonance with multigroup saturating sensors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 348-355.

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