NN look like black boxes because we’re not stripping out spurious axons in the network to understand the computational circuit. If you can remove a weight (w_ij

NN look like black boxes because we’re not stripping out spurious axons in the network to understand the computational circuit. If you can remove a weight (w_ij -> O) from a NN with no change in the network’s function across a broad spectrum of inputs then it clearly wasn’t necessary in the first place as that weighted edge was not involved in the information processing. Training networks will not only tune certain weights to dial up on signal but they will also (effectively) zero out certain weights that don’t carry information (if they’re not effectively zeroed our then they introduce noise and compromise the network function), in effect reducing the noise and focusing on signal. For anyone interested I wrote a paper in Natures Molecular Systems Biology in 2008 which goes into this. It describes artificial gene regulatory networks but my background was in AI and the NN math is the same. I believe the results are universally applicable across a broad range of NN-class network. Lots of relevant remarks in the supplementary as well. This includes a description of an algorithm to prune the network of spurious interactions. http://m.msb.embopress.org/content/4/1/213.abstract http://m.msb.embopress.org/content/4/1/213.abstract