Synchronization and evolvability in nonlinear networks

Abstract:

Computation, measurement, and synchronization are key issues in complex networks. Vast nonlinear networks are encountered in biology, for instance, and in neuroscience, where for most tasks the human brain grossly outperforms engineered algorithms using computational elements 7 orders of magnitude slower than their artificial counterparts. We show that nonlinear dynamic systems tools, and in particular contraction analysis, yield simple but highly non-intuitive insights about such issues, and that they also suggest systematic mechanisms to build progressively more refined networks through stable accumulation of functional building blocks and motifs.