Dimension reduction in heterogeneous neural networks: generalized   Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA)
  We propose, and illustrate via a neural network example, two different approaches to coarse-graining large heterogeneous networks. Both approaches are inspired from, and use tools developed in, methods for uncertainty quantification in systems with multiple uncertain parameters - in our case, the parameters are heterogeneously distributed on the network nodes. The approach shows promise in accelerating large scale network simulations as well as coarse-grained fixed point, periodic solution and stability analysis. We also demonstrate that the approach can successfully deal with structural as well as intrinsic heterogeneities. 
Minseok Choi, Tom Bertalan, Carlo R. Laing, Ioannis G. Kevrekidis
nlin.AO