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Towards more realistic self contained models of neurons: High-order, recurrence and local learning

  • J. Mira
  • A. E. Delgado
  • J. R. Alvarez
  • A. P. de Madrid
  • M. Santos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 686)

Abstract

The anatomy and physiology of biological neurons is revisited looking at a minimum set of computational requirements to be included in new and more complex models of self-contained local computation ANN. Some of these functionalities are then integrated and the corresponding model is evaluated. Properties included are: (1) locality and autonomy in all the computations including the learning algorithms. (2) a layered architecture with high-order recurrent neurons, (3) self and external programming via input spaces and (4) fault tolerance after physical lesion, or even elimination of one or more neurons.

Given the case that the improvements included in this proposal are of some value, it is nonetheless clear to us that the more genuine properties of biological computation are still lost. If we were to look for implementation, we would say that a minimal model of a neuron should include at least the computational capacity of a microprocessor with self-programming and fault tolerance facilities as addenda.

Keywords

Self-contained models recurrent neurons local learning input programming fault tolerance 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • J. Mira
    • 1
  • A. E. Delgado
    • 1
  • J. R. Alvarez
    • 1
  • A. P. de Madrid
    • 1
  • M. Santos
    • 1
  1. 1.Dpto. de Informática y Automática Facultad de CienciasUNEDMadridSpain

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