Neural networks now build their own learning machinery: random units self-organize by simple rules and match backprop-trained nets on image classification
An MIT team let a randomly connected network self-organize its learning machinery from simple rules such as strengthening connections that fire together, matching backpropagation-designed networks on image classification.