Random feedback weights support learning in deep neural networks
arxiv.org
arxiv.org
The main implications seem to be for neuroscience, as far as I can tell. Backprop is considered biologically implausible because it requires either bidirectional communication over synapses (which doesn't happen) or weight sharing between neurons. But this allows the forward and backward connections to be decoupled (i.e. they are different synapses).
This is really interesting stuff, my first reaction was "why does this even work?" I think I still don't really fully understand what's going on.
This is something entirely different. They are not doing regular backpropagation at all, but somehow using neurons to learn how to backpropagate values. I haven't read the paper yet, just read their slides earlier, so that might not be correct.
This is not true. See Neural Back propagation [1]. There are known mechanisms for backwards feedback between neural connections, for example Spike Timing Dependent Plasticity - where neural inputs that are well correlated in time and potential to output firings are strengthened over time. These phenomena are vital to learning and neural development.
Ok, got it: It will simplify the approach of how to create hardware based neural networks! no more complicated look-ups of the transposed weight matrix needed.