Synaptogenesis: Constraining Synaptic Plasticity Based on a Distance Rule

  • Jordi-Ysard Puigbò
  • Joeri van Wijngaarden
  • Sock Ching Low
  • Paul F. M. J. Verschure
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9886)

Abstract

Neural models, artificial or biologically grounded, have been used for understanding the nature of learning mechanisms as well as for applied tasks. The study of such learning systems has been typically centered on the identification or extraction of the most relevant features that will help to solve a task. Recently, convolutional networks, deep architectures and huge reservoirs have shown impressive results in tasks ranging from speech recognition to visual classification or emotion perception. With the accumulated momentum of such large-scale architectures, the importance of imposing sparsity on the networks to differentiate contexts has been rising. We present a biologically grounded system that imposes physical and local constraints to these architectures in the form of synaptogenesis, or synapse generation. This method guarantees sparsity and promotes the acquisition of experience-relevant, topologically-organized and more diverse features.

Keywords

Machine learning Connections Biologically constrained 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jordi-Ysard Puigbò
    • 1
  • Joeri van Wijngaarden
    • 1
  • Sock Ching Low
    • 1
  • Paul F. M. J. Verschure
    • 1
    • 2
  1. 1.Laboratory of Synthetic, Perceptive, Emotive and Cognitive Science (SPECS), DTICUniversitat Pompeu Fabra (UPF)BarcelonaSpain
  2. 2.Catalan Research Institute and Advanced Studies (ICREA)BarcelonaSpain

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