[cbennett]

Check methods-- the simulations exhibit sparse coding because the model was built that way, in particular, it assumes LIF (leaky integrate and fire) output neurons that can inhibit each other. In fact, this assumed inhibition is the only reason it works as is. Else, many neurons would probably fire simultaneously in an unstructured crossbar without a set learning rule.

Nevertheless, a STDP type learning rule can inspire interesting applications. One of my co-advisors authored an article [1] which shows a completely unsupervised classification on the MNIST challenge in a crossbar environment, achieving 93 \% . Nothing like state of the art CNNs etc, but considering this was done without labels, that's pretty impressive.

[1]http://www.ief.u-psud.fr/~querlioz/PDF/Querlioz_PIEEE2015.pd...