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Stochastic Resonance in Recurrent Neural Network with Hopfield-Type Memory

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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.

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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

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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
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  2. Haruhiko Nishimura
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Correspondence to Naofumi Katada.

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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

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

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