The classifier test: can models decode the “built” task?

1:00:59the color task. >> Mhm. >> And what you do is you say, okay, now in this new task where it's trying to find color, but the motor response is completely different. When you say when you say motor response >> meaning in in the first task the color response was upper left lower right. >> Oh I I get >> but my motor response now in the second task is same color but now I'm going in the opposite axis. Yes. >> Right. >> If I train a classifier on the on the color task >> and I want to decode the color meaning I look at the neurons and I try to guess >> was the color red or blue right? >> No way. >> And I don't retrain the classifier. I

1:01:39just I just now test it on on the second task >> which is which is a totally different motor thing. >> It can still guess the color. >> No way. >> You see this is the key [clears throat] for me. Yeah. Yeah. >> Now now it clicked. Right. This result is very profound because it means that the geometry of the representation in this highdimensional space is stable. Right. >> That's actually No, that no that is and the way they got to it's they're clever. That's that's I love the I love these like neuroscience. >> So clever, >> you know. Yeah. Because because that's what that means. If you've trained a classifier here and then now >> this classifier is not trained on this second task. But because the neural

1:02:20representation is stable, I can take all of the learning I've done here and just apply it here. And I can guess if it was looking at green or red or whatever, >> which means that underlying manifold structure >> is the same. >> Is the like is a is it is it is a >> it's like a real thing. >> It's a thing. It's like a real mathematical thing in this highdimensional space. >> Wow. >> Very cool. Right. >> This is this one. This one. >> And they did the same thing for the shape. They did the same thing for the direction of the motor and all that other stuff. >> That's like a really big deal. >> Mhm. >> Yeah. I thought I thought it was very very cool. >> This is why everyone was rushing to get out their manifold stories early this year cuz it seems to be a confluence of >> Yeah, it is. It is kind of cool because

1:03:02the next story we're going to talk about is is about manifolds as well, right? >> Oh my god. >> Um, one one of the other cool things that they did was so you know if like they have a compression they actually what they found was >> Sorry. [laughter] >> Yeah, >> this is so it's cool. >> It's so cool.