A Formal Model for Definition and Simulation of Generic Neural Networks

  • Authors:

  • M. A. Atencia;G. Joya;F. Sandoval

  • Affiliations:

  • Departamento de Lenguajes y Ciencias de la Computación, E.T.S.I. Informática, Universidad de Málaga, Campus de Teatinos, 29071 Málaga, Spain. E-mail: matencia@lcc.uma.e ...;Departamento de Tecnología Electrónica, E.T.S.I. Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071 Málaga, Spain;Departamento de Tecnología Electrónica, E.T.S.I. Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071 Málaga, Spain

  • Venue:
  • Neural Processing Letters
  • Year:
  • 2000

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Abstract

This paper presents the definition of a formal data structure, which assists in the characterization of any neural paradigm, with no restriction, including higher-order networks. Within this model, a neural network is mathematically described by specifying some static parameters (number of neurons, order) as well as a set of statistical distributions (which we call the network ‘dynamics’). Once a concrete set of distributions is defined, a single algorithm can simulate any neural paradigm. The presented structure assists in an exhaustive and precise description of the network characteristics and the simulation parameters, providing us with a unified criterion for comparing models and evaluating proposed systems. Though not presented here, the formal model has inspired a software simulator, which implements any system defined according to this structure, thus facilitating the analysis and modelling of neuronal paradigms.