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Stochastic Resonance with Colored Noise for Neural Signal Detection

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  • Fabing Duan
  • François Chapeau-Blondeau
  • Derek Abbott

Abstract

We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity–a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. For a parallel array of nonlinear elements, approximating neurons, we demonstrate even broader conditions allowing noise-enhanced detection, via a form of suprathreshold stochastic resonance.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0091345
    DOI: 10.1371/journal.pone.0091345
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    References listed on IDEAS

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    1. 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.
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