Skip to main content
SpringerLink
Log in

Stochastic Resonance in Recurrent Neural Network with Hopfield-Type Memory

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Stochastic resonance (SR) is known as a phenomenon in which the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. In this paper, we investigate how SR behavior can be observed in practical autoassociative neural networks with the Hopfield-type memory under the stochastic dynamics. We focus on SR responses in two systems which consist of three and 156 neurons. These cases are considered as effective double-well and multi-well models. It is demonstrated that the neural network can enhance weak subthreshold signals composed of the stored pattern trains and have higher coherence abilities between stimulus and response.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Wiesenfeld K, Moss F (1995) Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs. Nature 373: 33–36

    Article  Google Scholar 

  2. Moss F, Wiesenfeld K (1995) The benefits of background noise. Sci Am 273: 66

    Article  Google Scholar 

  3. Gammaitoni L, Hänggi P, Jung P, Marchesoni F (1998) Stochastic resonance. Rev Mod Phys 70: 223

    Article  Google Scholar 

  4. Longtin A (1993) Stochastic resonance in neuron models. J Stat Phys 70: 309–327

    Article  MATH  Google Scholar 

  5. Hänggi P (2002) Stochastic resonance in biology. Chem Phys Chem 3: 285–290

    Google Scholar 

  6. Douglass JK, Wilkens L, Pantazelou E, Moss F (1993) Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonance. Nature 365: 337–340

    Article  Google Scholar 

  7. Collins JJ, Chow CC, Imhoff TT (1995) Stochastic resonance without tuning. Nature 376: 236–238

    Article  Google Scholar 

  8. Chialvo DR, Longtin A, Müller-Gerking J (1997) Stochastic resonance in models of neuronal ensembles. Phys Rev E 55: 1798–1808

    Article  Google Scholar 

  9. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79: 2554–2558

    Article  MathSciNet  Google Scholar 

  10. Inchiosa ME, Bulsara AR (1995) Coupling enhanced stochastic resonance in nonlinear dynamic elements driven by a sinusoid plus noise. Phys Lett A 200: 283–288

    Article  Google Scholar 

  11. Inchiosa ME, Bulsara AR (1996) Signal detection statistics of stochastic resonators. Phys Rev E 53: 2021–2024

    Article  Google Scholar 

  12. Riani M, Simonotto E (1994) Stochastic resonance in the perceptual interpretation of ambiguous figures: a neural network model. Phys Rev Lett 72: 3120–3123

    Article  Google Scholar 

  13. Kim YJ, Grabowecky M, Suzuki S (2006) Stochastic resonance in binocular rivalry. Vis Res 46: 392–406

    Article  Google Scholar 

  14. Nishimura H, Katada N, Aihara K (2000) SR-type responses in autoassociative neural networks. In: Proceedings of the 7th international conference on neural information processing (ICONIP-2000), vol 1, pp 335–340

  15. Diederich S, Opper M (1987) Learning of correlated patterns in spin-glass networks by local learning rules. Phys Rev Lett 58: 949–952

    Article  MathSciNet  Google Scholar 

  16. Müller B, Reinhardt J (1990) Neural networks, an introduction. Springer, Dordrecht

    MATH  Google Scholar 

  17. Hebb D (1949) Organization of behaviour. Wiley, New York

    Google Scholar 

  18. Collins JJ, Chow CC, Imhoff TT (1995) Aperiodic stochastic resonance in excitable systems. Phys Rev E 52: 3321–3324

    Article  Google Scholar 

  19. Moss F, Ward LM, Sannita WG (2004) Stochastic resonance and sensory information processing: a tutorial and review of application. Clin Neurophysiol 115: 267–281

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Applied Informatics, University of Hyogo, Chuo-ku, Kobe, Hyogo, 650-0044, Japan

    Naofumi Katada & Haruhiko Nishimura

Authors
  1. Naofumi Katada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haruhiko Nishimura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naofumi Katada.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Katada, N., Nishimura, H. Stochastic Resonance in Recurrent Neural Network with Hopfield-Type Memory. Neural Process Lett 30, 145–154 (2009). https://doi.org/10.1007/s11063-009-9115-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-009-9115-3

Keywords

Search

Navigation