Development of Neural Network Structure with Biological Mechanisms

  • Samuel Johnson
  • Joaquín Marro
  • Jorge F. Mejias
  • Joaquín J. Torres
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5517)


We present an evolving neural network model in which synapses appear and disappear stochastically according to bio-inspired probabilities. These are in general nonlinear functions of the local fields felt by neurons—akin to electrical stimulation—and of the global average field—representing total energy consumption. We find that initial degree distributions then evolve towards stationary states which can either be fairly homogeneous or highly heterogeneous, depending on parameters. The critical cases—which can result in scale-free distributions—are shown to correspond, under a mean-field approximation, to nonlinear drift-diffusion equations. We show how appropriate choices of parameters yield good quantitative agreement with published experimental data concerning synaptic densities during brain development (synaptic pruning).


Neural networks Brain development synaptic pruning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Samuel Johnson
    • 1
  • Joaquín Marro
    • 1
  • Jorge F. Mejias
    • 1
  • Joaquín J. Torres
    • 1
  1. 1.Instituto Carlos I de Física Teórica y Computacional, and Departmento de Electromagnetismo y Física de la MateriaUniversity of GranadaSpain

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