Backpropagation feels like a pretty gross hack to me anyway. It is a powerful component that still needs a large amount of additional hand-engineering to do more than recognize shapes it has seen before. And it's quite mathematical, which seems kind of difficult to implement in a brain.

Edit: The article and code try to show how backpropagation might work given neuron behavior. I'll leave discussing the quality of the model to biologists.

> And it's quite mathematical, which seems kind of difficult to implement in a brain.

Another commenter pointed out that you don't need perfect backprop, some random backprijections are sufficient.

> "The relationship of spike-time-dependent plasticity to other models requires further clarifying work"

- I got something for that, in fact I think I discovered that backpropagation engenders STDP:

https://github.com/guillaume-chevalier/Spiking-Neural-Networ...