Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408075 | Neurocomputing | 2011 | 14 Pages |
Abstract
We show how the Equation Free approach for multi-scale computations can be exploited to extract, in a computational rigorous 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 bifurcation, stability analysis and estimation of mean appearance times of rare events can be derived bypassing the need for obtaining analytical approximations, providing an “on-demand” model reduction with respect to the underlying degree distribution.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Konstantinos G. Spiliotis, Constantinos I. Siettos,