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Dynamics of Firing Patterns in Evolvable Hierarchically Organized Neural Networks

  • Olga Chibirova
  • Javier Iglesias
  • Vladyslav Shaposhnyk
  • Alessandro E. P. Villa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5216)

Abstract

A scalable hardware platform made of custom reconfigurable devices endowed with bio-inspired ontogenetic and epigenetic features is configured to run an artificial neural network with developmental and evolvable capabilities. The hardware architecture allows internetwork communication and this study analyzes the simulated activity of two hierarchically organized spiking neural networks. The main features were an initial developmental phase characterized by cell death (apoptosis driven by excessive firing rate), followed by spike timing dependent synaptic plasticity in presence of background noise. The emergence of precise firing sequences formed by recurrent patterns of spike intervals above chance levels suggested the build-up of a connectivity, out of initially randomly connected networks, able to sustain temporal information processing. The relative frequency of precise firing sequences was higher in the downstream network and their dynamics suggested the emergence of an unsupervised hierarchical activity-driven connectivity.

Keywords

Output Layer Input Layer Spike Train External Input Couple Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Olga Chibirova
    • 1
  • Javier Iglesias
    • 1
    • 2
  • Vladyslav Shaposhnyk
    • 1
  • Alessandro E. P. Villa
    • 1
  1. 1.Grenoble Institut des Neurosciences-GIN, INSERM UMRS 836NeuroHeuristic Research Group Université Joseph FourierGrenoble 1France
  2. 2.Departament de Física i Enginyeria NuclearUniversitat Politècnica de CatalunyaTerrassaSpain

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