[smaddox]

I find it remarkable that their simulations exhibit sparse encoding. Is this a know property of artificial neural networks based on spike-time dependant plasticity?

I can imagine how it might emerge in this particular implementation from the electric current following the path of least resistance through the circuit, thereby preventing adjacent neurons from reaching criticality. This mechanism never occured to me before reading this article, though. Is anyone aware of any prior art on this topic?

[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...