  We study the effect of learning dynamics on network topology. 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. This incorporates
necessary competition between different edges. The final network we obtain is
robust and has a broad degree distribution.
