The core hypothesis — composition by shared neural subspaces

56:18Okay. If this cognitive Legos is correct, >> yes, >> then the brain should learn that mixing task, >> but it shouldn't learn it as a brand new problem. >> It should be just >> it should be a cognitive Lego shape task from the motion side and a cognitive Lego from the color task. >> Combining them together, >> the neural representations of those two. Yes. >> Should be resilient. >> Okay. Like it it does it's not ephemeral. It's not When you say resilient, how do you mean? >> What I mean [clears throat] by resilient is the same neurons >> that were responding to color are now going to still be responding to color.

56:59The same neurons that were responding to this axis are still going to be responding to this axis. But the the subject is still is going to be doing a completely new task, >> right? But using the same old the same old neurons. >> Yes. you using the same sort of procedure, right? The color processing module >> developed for one task >> and the the response the axis motor response for another task. It's going to just snap them together like Legos. >> Literally like Legos. Yeah. And and then that gives it the ability to then do the derivation without ascribing more neurons to do. >> Exactly. Yeah. Yeah. So, it's it's it's learning something, but it's actually it's it's it's able to learn because

57:41it's able to snap >> older older learned habits together, but >> at the neural level, the neurons are now working together. >> Yes. Yes. Yes. So, yes, the the idea is if if you Okay, I'm going to pause. This one This one is This one is >> This one's weird. >> This one's weird, >> dude. These systems neuroscience is is is really cool to think about that way. >> I just I Let's pause and and and really think about this >> because I know we have a lot to cover but I'm really trying to the point is right you have these you have these a neuron has a learning about whether it's shape or color in this context on these axes. >> Yeah.

58:21>> And let's say uh as an analogy the color is a red yellow a red Lego brick. Yeah. >> And the shape is a blue Lego brick. And so rather than saying when you you rather than having a new yellow Lego brick, you can just take those two put them together now and they are now able to get to this al this reversed response task that you're talking about >> this like combined >> combined this com this like combinator >> and the and the key is that we're combining two different modalities here. We're we're combining it's it's >> I see it's it's >> responding to color.

59:02>> Mhm. Right. >> But the response is with the shape response. >> Mhm. >> It's it's using the the the the axis that it's using it used to use for shape. Yes. >> It's a totally different task. >> But it is able to reuse an old a previously paved path. >> Mhm. >> For a different uh use case without losing the ability to still do the old thing. >> Yeah. >> And also not losing the ability to continue to do the new thing. >> Yes. Exactly. And so and one of the things they did was they randomized like which task they were doing and they found that the the you know the performance didn't decrease at all right which and that's that's something that like bi biological brains do all the time but what the key thing is what they

59:44can do is they can actually do simultaneous single unit recordings of five different brain regions at the same time. Ah so this is okay >> and this is where the neurons come in right so we can we can now do electrophysiology which is literally put in electrodes into the brain and then listen to thousands of neurons at the same time I think it's on the order of a thousand in this study from five different brain regions the parietal the lateral prefrontal cortex the frontal eye fields >> the temporal cortex the striatam right so this is where their sensory motor information reinforcement learning um preoter information. There's a executive hub which is the prefrontal

1:00:26cortex which is really the task rule. It's trying to figure out which task am I on in the first place, right? And that is what is really cool, okay, is the fact that we could use all of these things. And so how do how do we actually make sure that like how do we show that these cognitive Legos are actually working, >> right? Like how do you demonstrate existence? >> Yes. How do you demonstrate that there's these manifolds and all this other stuff? Here's what they did. Okay. So, you get a classifier that is trained on color. Okay. Okay. In task in in the in