Biologically Inspired Architecture of Feedforward Networks for Signal Classification

  • Šarūnas Raudys
  • Minija Tamošiūnaitė
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)


The hypothesis is that in the lowest hidden layers of biological systems “local subnetworks” are smoothing an input signal. The smoothing accuracy may serve as a feature to feed the subsequent layers of the pattern classification network. The present paper suggests a multistage supervised and “unsupervised” training approach for design and training of multilayer feed-forward networks. Following to the methodology used in the statistical pattern recognition systems we split functionally the decision making process into two stages. In an initial stage, we smooth the input signal in a number of different ways and, in the second stage, we use the smoothing accuracy as a new feature to perform a final classification.


Hide Layer Ventricular Fibrillation Feedforward Network High Frequency Oscillation Radial Basic Function 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Šarūnas Raudys
    • 1
  • Minija Tamošiūnaitė
    • 1
  1. 1.Institute of Mathematics and InformaticsVytautas Magnus UniversityKaunasLithuania

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