Pramana

, Volume 77, Issue 5, pp 817–826 | Cite as

Learning and structure of neuronal networks

  • KIRAN M KOLWANKAR
  • QUANSHENG REN
  • AREEJIT SAMAL
  • JÜRGEN JOST
Article

Abstract

We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates necessary competition between different edges. The final network we obtain is robust and has a broad degree distribution. Then we study the dynamics of the structure of a formal neural network. For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of the real neural network of C. elegans and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of model parameters.

Keywords

Neuronal networks scale-free network synapses learning logistic map 

PACS Nos

87.18.Sn 87.19.lv 05.45.Xt 89.75.Hc 

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Copyright information

© Indian Academy of Sciences 2011

Authors and Affiliations

  • KIRAN M KOLWANKAR
    • 1
    • 2
  • QUANSHENG REN
    • 2
    • 3
  • AREEJIT SAMAL
    • 2
    • 4
  • JÜRGEN JOST
    • 2
    • 5
  1. 1.Department of PhysicsRamniranjan Jhunjhunwala CollegeMumbaiIndia
  2. 2.Max Planck Institute for Mathematics in the SciencesLeipzigGermany
  3. 3.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina
  4. 4.Laboratoire de Physique Théorique et Modèles StatistiquesCNRS and Univ Paris-SudOrsayFrance
  5. 5.Santa Fe InstituteSanta FeUSA

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