NoProp: Training neural networks without back-propagation or forward-propagation

"Whenever these kind of papers come out I skim it looking for where they actually do backprop.

Check the pseudo code of their algorithms.

"Update using gradient based optimizations""

I mean the only claim is no propagation, you always need a gradient of sorts to update parameters. Unless you just stumble upon the desired parameters. Even genetic algorithms effectively has gradients which are obfuscated through random projections.

No you don't. See Hebbian learning (neurons that fire together wire together). Bonus: it is one of the biologically plausible options.

Maybe you have a way of seeing it differently so that this looks like a gradient? Gradient keys my brain into a desired outcome expressed as an expectation function.

> See Hebbian learning

The one that is not used, because it's inherently unstable?

Learning using locally accessible information is an interesting approach, but it needs to be more complex than "fire together, wire together". And then you might have propagation of information that allows to approximate gradients locally.