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arXiv · 1603.06643

Dimension reduction in heterogeneous neural networks: generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA)

Minseok Choi·Tom Bertalan·Carlo R. Laing·Ioannis G. Kevrekidis
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Abstract

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.

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Minseok Choi, Tom Bertalan, Carlo R. Laing, Ioannis G. Kevrekidis. 2016-03-21. Dimension reduction in heterogeneous neural networks: generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA). https://doi.org/10.1140/epjst%2Fe2016-02662-3

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