From Spiking Neurons to Dynamic Perceptrons

  • Francesco Palmieri
  • Antonella Luongo
  • Andrew Moiseff
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

This paper begins a systematic validation for a simple and reliable artificial neural network model that can be directly related to the main behaviour of biological neural networks. The sigmoid-plus-linear filter appears to be a promising candidate if the sigmoidal function is calculated in reference to the pulse generation refractory effects. We directly compare a classical spiking neuron model with a scheme based on a sigmoidal function plus a linear filter. The filter is computed as the best least squares fit to the output of the spiking model. The results seem to confirm that FIR and IIR neural networks may be able to represent the essence of the signal processing performed by biological neurons.

Keywords

Convolution Aliasing 

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Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • Francesco Palmieri
    • 1
  • Antonella Luongo
    • 1
  • Andrew Moiseff
    • 2
  1. 1.Dipartimento di Ingegneria Elettronica e delle TelecomunicazioniUniversità degli Studi di Napoli Federico IINapoliItaly
  2. 2.Department of Physiology and NeurobiologyThe University of ConnecticutStorrsUSA

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