[FrustratedMonky]

I think the latest neuroscience would disagree.

Maybe if you cut open a brain, you wont find a printout with backpropagate code.

But you do find neurons, in a network.

Humans do learn right? They take in information and encode it in their brain.

Why does it have to be back propagation to qualify. Bayesian? Minimum Entropy?

There are a lot of forms a machine neural network can take. There are a lot of theories on exactly how the brain 'calculates'/'processes'. With all of the advancements in Neuroscience and AI in last 5 years, it is bit hubris to say we'll never be able to figure out the brain, and also be able to model it.

There are a lot more like this. The field is moving too rapidly for me to go find every paper today. But it is dozens, and not even so cutting edge there isn't already books on it.

https://www.quantamagazine.org/some-neural-networks-learn-la...

[kelseyfrog]

To add, there is a whole field of biologically-plausible learning. Predictive coding is one such method. It is isomorphic to SGD[1], and doesn't require global computation. ie it's backprop-free optimization. Although slower than SGD on silicon, it's massively parallelizible which ends up making it more biologically plausible rather than less.

1. 2020 PREDICTIVE CODING APPROXIMATES BACKPROP ALONG ARBITRARY COMPUTATION GRAPHS https://arxiv.org/pdf/2006.04182.pdf