Effect of Feedback Strength in Coupled Spiking Neural Networks

  • Javier Iglesias
  • Jordi García-Ojalvo
  • Alessandro E. P. Villa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)


We simulated the coupling of two large spiking neural networks (104 units each) composed by 80% of excitatory units and 20% of inhibitory units, randomly connected by projections featuring spike-timing dependent plasticity, locality preference and synaptic pruning. Only the first network received a complex spatiotemporal stimulus and projected on the second network, in a setup akin to coupled semiconductor lasers. In a series of simulations, the strength of the feedback from the second network to the first was modified to evaluate the effect of the bidirectional coupling on the firing dynamics of the two networks. We observed that, unexpectedly, the number of neurons which activity is altered by the introduction of feedback increases in the second network more than in the first network, suggesting a qualitative change in the dynamics of the first network when feedback is increased.


Spike Train Semiconductor Laser Feedback Strength Spike Timing Dependent Plasticity Synaptic Pruning 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Javier Iglesias
    • 1
    • 2
  • Jordi García-Ojalvo
    • 2
  • Alessandro E. P. Villa
    • 1
  1. 1.Grenoble Institut des Neurosciences-GIN, NeuroHeuristic Research GroupUniversité Joseph FourierGrenobleFrance
  2. 2.Departament de Física i Enginyeria NuclearUniversitat Politècnica de CatalunyaTerrassaSpain

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