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Comment by henearkr | Hacker News Reader

The whole premise of NeuraLink is built upon this assumption and nobody found it weird...

By the way, I wonder how the NeuraLink team will work around that.

I am guessing that what GP meant was that some people have started thinking of the brain not as a theoretical computer (i.e. as something that can be modeled as a Turing machine or lambda calculus etc), but as a microprocessor. The compational nature of the brain is a pretty wide held belief (though not universal), one that I for one hold.

However, starting to think of the brain as if it should resemble the organization of a microprocessor, with 'ALUs', working memory, well-defined boundaries etc are definitely wrong, and a misapplication of the notion of Turing equivalence. It is nevertheless somewhat common, especially in popculture.

Brain certainly has regions that are structurally different. The logic of differentiation certainly does not correspond to microprocessor, but differentiation is there as a fact.

That interface is based upon learning. It simply means the learning has to be ongoing.

Yes, but that might entail a lot of rework, now that it is a moving target.

Also, that could solve many of their problems. Maybe things did not work out very well initially precisely because of this unsuspectedly huge brain plasticity.

Not sure I understand what problem plasticity poses to NeuraLink. If anything, that is the mechanism that is supposed to power it up. How otherwise brain would learn the meaning of the signals received from it?

NeuraLink was supposed to learn the mapping, yes, but on the assumption that the mapping was relatively stable, unless I am completely mistaken.

What we learn in this article is that the mapping changes a lot, and quickly.

This is an important difference, as previously it could be enough to converge slowly towards the right mapping, but now it has to be done quickly in order to become good enough before starting again a retraining on it.

(To sum up my remark: the retraining has to be fast enough to not become obsolete before it can be used, given the "representation drift".)

You are conflating low-level I/O (like eyes and NeuraLink) with high-level reasoning.

Assume NeuraLink transmits an encoded signal that there's a dog in front of you. Without "representation drift" (or rather its underlying mechanism) I don't think your brain would ever notice that signal has anything to do with dogs. The wire is inserted into a random place, it is not designed to be repositioned to fire up your "dog" neurons it needs to send "dog" signal.

As for the assumption of mapping being stable, there's no such thing at all. First and foremost the brain itself must learn to interpret signals from the wire and send them back. Any mechanism on wire's side would mostly be to establish a feedback loop for the brain to be able to learn to do that (which it will do by rearranging its internal structure).

Wow there is something weird:

I always assumed that the NeuraLink allowed to output from the brain to external interfaces, not to input into the brain.

I have seen the public presentations of NeuraLink by Elon Musk, and the focus was always to extract info from the brain, not the other way around.

Do you have some info related to your interpretation?

Is there a difference between the brain learning input and output? The brain would need to learn that firing that neutron makes the cursor move right.

The presentation with pigs was all about exporting the brain's representation for olfactory stimuli. They were not demonstrating learning to control cursors.

So now I understand why the quiproquo, sorry.

I agree with you that in this situation (cursor controlling) nothing is changed regarding the learning task for the brain.

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