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Comment by 2bitencryption | Hacker News Reader

maybe the functional nodes (wetware neurons vs software neurons) are very different, but it seems like the way they are manipulated via back-propagation, pooling, recurrence, layerings, etc, are similar, right?

because, at the end of the day, it's more about how behavior is emergent than how behavior functions physically, I would say.

I would guess that if we ever get to some true sci-fi AI "consciousness", it would just be a hyper-scaled version of what we already have. But that's just fun speculation.

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Nope. Brains don't use layers or back propagation. CS NN's are really just a cool name for math that has almost nothing in common with how the brain functions.

There is a lot of misinformation about how the brain works. For example you see a lot of drawings with different parts of the brain doing different things. However, if you look at an actual brain almost none of this is physically obvious. At best those diagrams show what stops working when that part is damaged, though again plenty of people have very different structures and it still mostly works.

This is not correct, artificial NNs are not functionally related to neurons in the brain. Nothing like back-propagation has been observed in real neurons. The early layers of a CNN may be similar to early perceptrons in the brain, but beyond this any connection between the two is fantasy.

Real cells use Hebbian learning, which in some cases is equivalent to back-propagation, but is way less efficient. Otherwise, yes, many of the techniques used in ML is also used in the body. Not only ML, actually, but also electrical engineering and surely many other fields. The more you learn about the body the more machine-like it will look to you. In some respects. In others, it's fucking space technology. Nanobots exist, they are called proteins, and each of your cells have hordes of them, for example.

The big difference is that biological neurons emit binary outputs. Because of this, there’s no gradient and they can’t be trained by SGD.

There's phase and frequency information in neural spike trains.

But biological neurons are also stochastic, and the firing probabilities are continuous, so you can do SGD on them.

Well, I think it may not just be Hebbian learning but likely Balanced Amplification learning [1] as well which propagates quite a bit faster. Promising paper came out recently echoing this phenomena globally with a model of visual perception in the macaque brain[2].

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667957/ [2] https://www.cell.com/neuron/fulltext/S0896-6273(18)30152-1?

Honestly, this is more like having invented solar cells and then saying that that's how trees work. Turning light into energy is just a small part of what trees do, and solar cells are only an approximation of the idea but the actual implementation is very very different from photosynthesis.

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