Morphological Neural Networks with Dendrite Computation: A Geometrical Approach

  • Ricardo Barrón
  • Humberto Sossa
  • Héctor Cortés
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)


Morphological neural networks consider that the information entering a neuron is affected additively by a conductivity factor called synaptic weight. They also suppose that the input channels account with a saturation level mathematically modeled by a MAX or MIN operator. This, from a physiological point of view, appears closer to reality than the classical neural model, where the synaptic weight interacts with the input signal by means of a product; the input channel forms an average of the input signals. In this work we introduce some geometrical aspects of dendrite processing that easily allow visualizing the classification regions, providing also an intuitive perspective of the production and training of the net.


Input Signal Output Neuron Synaptic Weight Input Neuron Input Channel 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ricardo Barrón
    • 1
  • Humberto Sossa
    • 1
  • Héctor Cortés
    • 1
  1. 1.Centro de Investigación en Computación – IPN Av. Juan de Dios Bátiz S/N Esq. Miguel Othón de MendizábalUnidad Profesional ”Adolfo López Mateos”, ZacatencoMexico. D.F.MEXICO

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