STDP-driven networks and the \emph{C. elegans} neuronal network

Quansheng Ren 1, Kiran M. Kolwankar 1, 2, Areejit Samal 1, 3, Jürgen Jost 1, 4

Physica A: Statistical Mechanics and its Applications 389, 18 (2010) 3900-3914

We study the dynamics of the structure of a formal neural network wherein the strengths of the synapses are governed by spike-timing-dependent plasticity (STDP). 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 a real neural network of \emph{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 the model parameters.

  • 1. Max Planck Institute for Mathematics in the Sciences (MPI-MIS),
    Max-Planck-Institut
  • 2. Department of Physcis Ramniranjan Jhunjhunwala College,
    Ramniranjan Jhunjhunwala College
  • 3. Laboratoire de Physique Théorique et Modèles Statistiques (LPTMS),
    CNRS : UMR8626 – Université Paris XI - Paris Sud
  • 4. Santa Fe Institute,
    Santa Fe Institute