• DocumentCode
    1928718
  • Title

    Almost all noise types can improve the mutual information of threshold neurons that detect subthreshold signals

  • Author

    Kosko, Bart ; Mitaim, Sanya

  • Author_Institution
    Signal & Image Process. Inst., Univ. of Southern California, Los Angeles, CA, USA
  • Volume
    4
  • fYear
    2003
  • fDate
    20-24 July 2003
  • Firstpage
    2740
  • Abstract
    Two new theorems show that small amounts of noise can increase the mutual information of threshold neurons that detect subthreshold signals. The first theorem shows that this "stochastic resonance" effect holds for all finite-variance noise probability density functions that obey a simple mean constraint that the user can control. The second theorem shows that this effect holds for all infinite-variance noise types in the broad class of stable distributions. Stable bell curves can model extremely impulsive noise environments. So the second theorem shows that this stochastic-resonance effect is robust against violent fluctuations in the additive noise process.
  • Keywords
    information theory; neural nets; noise; probability; signal detection; additive noise process; finite-variance noise probability density functions; impulsive noise environments; infinite-variance noise types; mutual information; stable bell curves; stochastic resonance; stochastic resonance effect; subthreshold signal detection; threshold neurons; Additive noise; Constraint theory; Fluctuations; Mutual information; Neurons; Noise robustness; Probability density function; Signal detection; Stochastic resonance; Working environment noise;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1224001
  • Filename
    1224001