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Emergence of Small-World Structure in Networks of Spiking Neurons Through STDP Plasticity

  • Gleb Basalyga
  • Pablo M. Gleiser
  • Thomas Wennekers
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 718)

Abstract

In this work, we use a complex network approach to investigate how a neural network structure changes under synaptic plasticity. In particular, we consider a network of conductance-based, single-compartment integrate-and-fire excitatory and inhibitory neurons. Initially the neurons are connected randomly with uniformly distributed synaptic weights. The weights of excitatory connections can be strengthened or weakened during spiking activity by the mechanism known as spike-timing-dependent plasticity (STDP). We extract a binary directed connection matrix by thresholding the weights of the excitatory connections at every simulation step and calculate its major topological characteristics such as the network clustering coefficient, characteristic path length and small-world index. We numerically demonstrate that, under certain conditions, a nontrivial small-world structure can emerge from a random initial network subject to STDP learning.

Keywords

Cluster Coefficient Random Network Synaptic Weight Excitatory Synapse Connection Probability 
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.

Notes

Acknowledgement

This work was supported by an EPSRC research grant (Ref. EP/C010841/1).

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Gleb Basalyga
    • 1
  • Pablo M. Gleiser
    • 2
  • Thomas Wennekers
    • 3
  1. 1.Centre for Robotics and Neural Systems (CRNS)University of PlymouthPlymouthUK
  2. 2.CONICETCentro Atomico BarilocheBarilocheArgentina
  3. 3.Centre for Robotics and Neural SystemsUniversity of PlymouthDevonUK

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