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Comment by grondilu | Hacker News Reader

I vaguely recall that experts consider artificial neural networks to be a very gross approximation of biological ones. They often state that one reason we don't have AI today is that we don't really know how the brain and the neurons it is made of work.

Then I wonder : how does openworm deal with that lack of knowledge? Is there any chance progress in modeling C. Elegans could be used to improve machine learning?

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ANNs aren't supposed to model biological neurons, they're more inspired by them. We actually know a great deal about how real neurons work, although the emergent effects keep us from learning much about the brain as a whole.

But modeling biological neurons is expensive; they're far more complex than a logistic function. Just modeling a worm is taking them an entire cluster, and they appear to be using a pretty simple model (the Hodgkin–Huxley equations way back from 1952). I don't think we'll ever simulate real neurons to detect cat photos. Instead, modeling entire brains might allow us to see how learning happens, which could be simplified into new algorithms for machine learning.

We believe that the underlying implementation (biological neuron vs logistic function) doesn’t matter as long as it has the fundamental properties from which intelligence can emerge. Like how you can build computers from gears, relays, vacuum tubes or silicon - as long as you have a transistor-like nonlinearity to work with, you’re good.

From what I understand, the complication is that each neurone acts behaves like a complex neural network by itself, so to get the same overall behavior, you would need many more fundamental units.

It could be other way around. Evolution invented imprecise and roundabout way of doing gradient descent.

There’s a lot of work in artificial gene regulatory networks that suggest that the network topology not the implementation details is responsible for function. I think we learn more with a simplified model and then figure out why we are falling short of our expectations.

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