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Use of behavioural stochastic resonance by paddle fish for feeding

Author

Listed:
  • David F. Russell

    (Center for Neurodynamics, University of Missouri at St. Louis)

  • Lon A. Wilkens

    (Center for Neurodynamics, University of Missouri at St. Louis)

  • Frank Moss

    (Center for Neurodynamics, University of Missouri at St. Louis)

Abstract

Stochastic resonance is the phenomenon whereby the addition of an optimal level of noise to a weak information-carrying input to certain nonlinear systems can enhance the information content at their outputs1,2,3,4. Computer analysis of spike trains has been needed to reveal stochastic resonance in the responses of sensory receptors5,6,7 except for one study on human psychophysics8. But is an animal aware of, and can it make use of, the enhanced sensory information from stochastic resonance? Here, we show that stochastic resonance enhances the normal feeding behaviour of paddlefish (Polyodon spathula)9,10, which use passive electroreceptors11,12 to detect electrical signals from planktonic prey13. We demonstrate significant broadening of the spatial range for the detection of plankton when a noisy electric field of optimal amplitude is applied in the water. We also show that swarms of Daphnia plankton are a natural source of electrical noise. Our demonstration of stochastic resonance at the level of a vital animal behaviour, feeding, which has probably evolved for functional success, provides evidence that stochastic resonance in sensory nervous systems is an evolutionary adaptation14.

Suggested Citation

  • David F. Russell & Lon A. Wilkens & Frank Moss, 1999. "Use of behavioural stochastic resonance by paddle fish for feeding," Nature, Nature, vol. 402(6759), pages 291-294, November.
    • Handle: RePEc:nat:nature:v:402:y:1999:i:6759:d:10.1038_46279
    DOI: 10.1038/46279
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    Citations

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    Cited by:

    1. Shogo Yonekura & Yasuo Kuniyoshi, 2017. "Bodily motion fluctuation improves reaching success rate in a neurophysical agent via geometric-stochastic resonance," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
    2. Yu, Dong & Wang, Guowei & Ding, Qianming & Li, Tianyu & Jia, Ya, 2022. "Effects of bounded noise and time delay on signal transmission in excitable neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    3. Li, Tianyu & Wu, Yong & Yang, Lijian & Zhan, Xuan & Jia, Ya, 2022. "Spike-timing-dependent plasticity enhances chaotic resonance in small-world network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    4. Yu, Dong & Wu, Yong & Yang, Lijian & Zhao, Yunjie & Jia, Ya, 2023. "Effect of topology on delay-induced multiple resonances in locally driven systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    5. Fabing Duan & François Chapeau-Blondeau & Derek Abbott, 2014. "Stochastic Resonance with Colored Noise for Neural Signal Detection," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-7, March.
    6. Ueda, Michihito, 2010. "Improvement of signal-to-noise ratio by stochastic resonance in sigmoid function threshold systems, demonstrated using a CMOS inverter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 1978-1985.
    7. Xu, Yong & Wu, Juan & Du, Lin & Yang, Hui, 2016. "Stochastic resonance in a genetic toggle model with harmonic excitation and Lévy noise," Chaos, Solitons & Fractals, Elsevier, vol. 92(C), pages 91-100.

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