  We show how the Equation-Free approach for mutliscale computations can be
exploited to extract, in a computational strict and systematic way the emergent
dynamical attributes, from detailed large-scale microscopic stochastic models,
of neurons that interact on complex networks. In particular we show how the
Equation-Free approach can be exploited to perform system-level tasks such as
bifurcation, stability analysis and estimation of mean appearance times of rare
events, bypassing the need for obtaining analytical approximations, providing
an "on-demand" model reduction. Using the detailed simulator as a black-box
timestepper, we compute the coarse-grained equilibrium bifurcation diagrams,
examine the stability of the solution branches and perform a rare-events
analysis with respect to certain characteristics of the underlying network
topology such as the connectivity degree

Social and Information Networks;Adaptation and Self-Organizing Systems;Biological Physics;Neurons and Cognition