Reviews: Neural networks grown and self-organized by noise

The main contributions of this paper are to propose an algorithm to learn a pooling architecture and one to grow the architecture with only self-organization principles. The developmental algorithm is evaluated on a different input geometry and on experiments with faults in the first layer. A last experiment evaluates the proposed algorithms on a MNIST classification task. I like the originality of the work, as the authors propose the principle of a growing machine, that is able to yield a functional architecture from a limited set of rules. The principles to follow for building such self-organized network are clearly exposed.