Advertisement

Neural Network Computations with Negative Triggering Thresholds

  • Petro Gopych
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

Recent binary signal detection theoryand neural network assembly memory model’s optimal data-decoding/memory-retrieval algorithm exists simultaneously in functionally equivalent neural network (NN), convolutional, and Hamming distance forms. In present paper this NN algorithm has been specified to provide decoding/retrieval probabilities at both positive and negative neuron triggering thresholds needed, in particular, for ROC curve computations. Examples of intact and damaged NNs are considered, model neuron receptive fields are introduced, a comparison between NN and analytic computations of decoding/retrieval probabilities is also performed.

Keywords

Neural Network Algorithm Bidirectional Associative Memory Trigger Threshold Binarization Rule Neural Network Computation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hopfield, J.J.: Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proc. Nat. Acad. Sci., USA 79, 2554–2558 (1982)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Kosko, B.: Bidirectional Associative Memories. IEEE Trans. Syst., Man, Cyber. 18, 49–60 (1988)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Grossberg, S.: How does a Brain Build a Cognitive Code? Psych. Rev. 1, 1–51 (1980)CrossRefGoogle Scholar
  4. 4.
    Gopych, P.M.: Determination of Memory Performance. JINR Rapid Communications 4[96]-99, 61–68 (1999) (in Russian)Google Scholar
  5. 5.
    Gopych, P.M.: Sensitivity and Bias within the Binary Signal Detection Theory, BSDT. Int. J. Inf. Theor. & Appl. 11, 318–328 (2004)Google Scholar
  6. 6.
    Gopych, P.M.: ROC Curves within the Framework of Neural Network Assembly Memory Model: Some Analytic Results. Int. J. Inf. Theor. & Appl. 10, 189–197 (2003)Google Scholar
  7. 7.
    Gopych, P.M.: A Neural Network Assembly Memory Model Based on an Optimal Binary Signal Detection Theory. Programming Problems (Kyiv, Ukraine) 2(3), 473–479 (2004)MathSciNetGoogle Scholar
  8. 8.
    Gopych, P.M.: Identification of Peaks in Line Spectra Using the Algorithm Imitating the Neural Network Operation. Instr. & Exper. Techn. 41, 341–346 (1998)Google Scholar
  9. 9.
    DeAngelis, G.C., Ohzava, I., Freeman, R.D.: Receptive-Field Dynamics in the Central Visual Pathways. Trends in Neurosci 18, 451–458 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Petro Gopych
    • 1
  1. 1.V.N. Karazin Kharkiv National UniversityKharkivUkraine

Personalised recommendations