Display options
Cite
Krauss P, Metzner C, Schilling A, et al. Adaptive stochastic resonance for unknown and variable input signals. Sci Rep. 2017;7(1):2450doi: 10.1038/s41598-017-02644-w.
Krauss, P., Metzner, C., Schilling, A., Schütz, C., Tziridis, K., Fabry, B., & Schulze, H. (2017). Adaptive stochastic resonance for unknown and variable input signals. Scientific reports, 7(1), 2450. https://doi.org/10.1038/s41598-017-02644-w
Krauss, Patrick, et al. "Adaptive stochastic resonance for unknown and variable input signals." Scientific reports vol. 7,1 (2017): 2450. doi: https://doi.org/10.1038/s41598-017-02644-w
Krauss P, Metzner C, Schilling A, Schütz C, Tziridis K, Fabry B, Schulze H. Adaptive stochastic resonance for unknown and variable input signals. Sci Rep. 2017 May 26;7(1):2450. doi: 10.1038/s41598-017-02644-w. PMID: 28550314; PMCID: PMC5446399.
Copy Download .nbib Format: NLM
Share it on

Sci Rep. 2017 May 26;7(1):2450. doi: 10.1038/s41598-017-02644-w.

Adaptive stochastic resonance for unknown and variable input signals.

Scientific reports

Patrick Krauss, Claus Metzner, Achim Schilling, Christian Schütz, Konstantin Tziridis, Ben Fabry, Holger Schulze

Affiliations

  1. Department of Otorhinolaryngology, University Erlangen, Nürnberg, Germany.
  2. Department of Physics, University Erlangen, Nürnberg, Germany.
  3. Department of Otorhinolaryngology, University Erlangen, Nürnberg, Germany. [email protected].

PMID: 28550314 PMCID: PMC5446399 DOI: 10.1038/s41598-017-02644-w

Abstract

All sensors have a threshold, defined by the smallest signal amplitude that can be detected. The detection of sub-threshold signals, however, is possible by using the principle of stochastic resonance, where noise is added to the input signal so that it randomly exceeds the sensor threshold. The choice of an optimal noise level that maximizes the mutual information between sensor input and output, however, requires knowledge of the input signal, which is not available in most practical applications. Here we demonstrate that the autocorrelation of the sensor output alone is sufficient to find this optimal noise level. Furthermore, we demonstrate numerically and analytically the equivalence of the traditional mutual information approach and our autocorrelation approach for a range of model systems. We furthermore show how the level of added noise can be continuously adapted even to highly variable, unknown input signals via a feedback loop. Finally, we present evidence that adaptive stochastic resonance based on the autocorrelation of the sensor output may be a fundamental principle in neuronal systems.

References

  1. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1993 Oct;48(4):2481-2489 - PubMed
  2. Proc Natl Acad Sci U S A. 1986 Nov;83(21):8400-4 - PubMed
  3. IEEE Trans Neural Netw. 2004 Nov;15(6):1526-40 - PubMed
  4. Phys Rev Lett. 2003 Mar 28;90(12):120602 - PubMed
  5. Nat Rev Neurosci. 2008 Apr;9(4):292-303 - PubMed
  6. Nature. 1995 Jan 5;373(6509):33-6 - PubMed
  7. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 2000 Apr;61(4 Pt B):4286-94 - PubMed
  8. Chaos. 2003 Dec;13(4):1226-30 - PubMed
  9. Nature. 1993 Sep 23;365(6444):337-40 - PubMed
  10. Nature. 1996 Mar 14;380(6570):165-8 - PubMed
  11. J Neurophysiol. 1996 Jul;76(1):642-5 - PubMed
  12. Nature. 1995 Jul 20;376(6537):236-8 - PubMed
  13. Clin Neurophysiol. 2004 Feb;115(2):267-81 - PubMed
  14. Biol Cybern. 2006 Jul;95(1):1-19 - PubMed
  15. Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Nov;70(5 Pt 1):051110 - PubMed
  16. Front Neurosci. 2016 Dec 27;10 :597 - PubMed
  17. Chaos. 1998 Sep;8(3):539-548 - PubMed
  18. Chemphyschem. 2002 Mar 12;3(3):285-90 - PubMed

Publication Types