Neural Network Computations with Negative Triggering Thresholds

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


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.


Neural Network Algorithm Bidirectional Associative Memory Trigger Threshold Binarization Rule Neural Network Computation 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

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

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