20170327 Extending Olshausens classical SparseNet

In a previous notebook, we tried to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse. In particular, we saw that in order to optimize competition, it is important to control cooperation and we implemented a heuristic to just do this.

In this notebook, we provide an extension to the SparseNet algorithm. We will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winnertakeall basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ):
@article{Perrinet10shl,
Title = {Role of homeostasis in learning sparse representations},
Author = {Perrinet, Laurent U.},
Journal = {Neural Computation},
Year = {2010},
Doi = {10.1162/neco.2010.0508795},
Keywords = {Neural population coding, Unsupervised learning, Statistics of natural images, Simple cell receptive fields, Sparse Hebbian Learning, Adaptive Matching Pursuit, Cooperative Homeostasis, CompetitionOptimized Matching Pursuit},
Month = {July},
Number = {7},
Url = {http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl},
Volume = {22},
}
This is joint work with Victor Boutin.