The Pong setup — sensory cortex, motor cortex, and encoded game state

26:58months. And as these neurons in my petri dish turn on and off, my multielerode array is going to sense that. It's going to read it in. It's going to sense the game play and it's going to read it out into the neuron. Now, what we don't want to do is completely jumble up our readin and our readout. >> That was literally going to be my the orchestration problem there is non-trivial, >> right? Yeah. Because we've got a bunch of electrodes and the brain is not really an all readin, all readout, right? There are regions in the brain like the occipital lobe that has the visual cortex. Um there's the prefrontal cortex that makes decisions. There's the motor cortex that actually outputs my

27:40hand when I'm like let's say using a joystick right so there are segregated spots in my brain for where I'm reading in that'll be my visual cortex and where I'm going and reading out that would be my motor cortex. So they decided to create an artificial visual cortex and a motor cortex on the chip itself. Okay. So at the very top on the right hand side what you can see is the electrode layout schematic. Okay. >> On the very So you've got your rectangular multi-elerode array that has 1024 >> electrodes that I can actively read out and read in from. On the very top I'm going to make a rectangular box that's in the upper half. And eight of those

28:21electrodes I'm going to use as my quote visual cortex. It's really quite a sensory cortex, I should say, because that's what I'm >> writing into, right? The sensory cortex is getting information. When when I when I play a video game, the sensory cortex is through my eyes and through, I guess, sound sometimes >> getting information about the state of the game. >> If you talk to Call of Duty players, sound is as equally important with their high quality headphones to sneak up on you as visual. I'm I'm just being annoying. I mean, I when I was playing Halo, I used to sometimes have to like play on mute because it was like 2 a.m. and my parents would like like, "What are you doing?" So, I I'm actually quite

29:03good at playing Halo on mute. So, I don't know about you guys, but anyways, the idea is there's a sensory cortex that is getting information from the brain. So, I'm going to just arbitrarily make that top half of my electrode the sensory cortex, and that's where I'm going to write information to my neurons. And the idea here is we're segregating it from other section or sections such that it only has one thing to worry about. >> Yeah, exactly. It's kind of like I mean I don't want to give too much agency to these neurons in a dish, but what I'm saying is that those neurons in that upper half of my petri dish >> are acting like the sensory neurons. They're going to sort of understand

29:43that, hey, I'm getting information about something. And I think this goes back to the point you brought up earlier, which is proximity creates stronger neural connections. And so because you're putting them in proximity and having a similar input, >> Yeah. >> the theory is, you know, they will naturally strengthen around that shared >> Yes. idea. >> Very good. Yeah. Exactly. Those neurons in that region, they're all connected to one another, and so they're going to somehow figure it out. We're going to get into how, but that's the idea. They're going to somehow figure it out. And if actually if you if we if we put that back. Yes. The bottom half of that petri dish has those up and down arrows. Yeah. >> That's my motor cortex. >> Okay. >> Okay. So neurons in the down region in

30:26that little block. >> If those fire that's going to trigger my pong um paddle, I guess. It's a paddle, right? It's like a digital paddle. >> I know what you mean. Yes. >> You know what I mean, right? Like that it's going to it's going to trigger the paddle to go down. And if the neurons in the up region fire for those electrodes, then it's going to trigger the neurons to go up. And sometimes you will have to calculate a difference between the up and down because, you know, maybe there's more >> neurons in the down region versus the up. So I also have to normalize based on like how many neurons there are cuz I don't want to just artificially keep going down if there's randomly more neurons in that region. So there's a bunch of mathematical tricks you have to do but at the end of the day segregated

31:07regions one set of neurons is going to fire if the culture wants to go down and one set of neurons is going to fire if that petri dish culture is going to want to go up. Yes. >> Okay. So now we've now we figured out our read and our right mechanism. The right is the sensory cortex at the top eight electrodes and our read is getting it in. And I think an interesting point about this is that geographical or or locationational uh the where it's located and how it's segregated like form is function or form is following function in this context. >> Exactly. Yeah. Now there's one other thing that I want to touch upon which is how exactly that writing mechanism >> Yeah. >> is being implemented. Right. Because

31:47what do we need to give this petri dish of neurons information about? Yes. What is all the information we need to give it? Well, we need to give it a state of the game. right? Which is where is the paddle and where is the ball >> right in relation to that. >> Yes. >> Now for us when we play pong, we've got a very nice visual system. So we see where the paddle is, we see where the ball is and when we also see how it's moving. And so we can sort of >> all of our billions of neurons in our brain can like understand spatially what's going on because we understand space because we've had so much pre-training and we can figure it out. Yeah, >> this is a neuron in a petri dish. It does not have eyes. It does not have

32:27senses. We are creating the senses. So, how are we going to do it? Well, >> they use something called rate and place coding. This is something that is very commonly observed in the human brain. >> Okay. >> Okay. For example, let's take the example of the human cookia which is in our ear. That's how we sense sound, we sense frequency, and we sense loudness. Right? I've um I've touched upon the cookia before. The idea with the cookia is there's like this tube, a snail-like tube that is embedded in our ear. And what that tube is, if you were to unravel it, there would be neurons that

33:07are very thick at one end and there would be neurons that are very thin at the other end. This thing gets wrapped up into kind of like a shell shape, right? But if I hear sound of a certain frequency, that is going to excite a neuron along this axis that is at exactly the right thickness. Right? If the sound is very low frequency, like a bass sound, that's going to excite the thicker neurons because they're bigger and the wavelength of the sound is bigger. So, it's it's kind of just like, you know, the resonance is happening. And if the sound is really high, then it's going to >> excite the smaller neurons in my cookia. >> Yes. >> So that's a place coding that tells me

33:49the frequency. >> Okay. >> Okay. That's my place because my place along the axis is telling me the frequency of the sound. >> Yes. >> The rate coding is how loud is that sound. >> Ah, there it is. >> Right. Because if it's very loud, then that neuron is going to fire at a very high frequency. If it's not that loud, it's going to fire at a low frequency. So I'm getting two pieces of information with place and rate. The place is telling me the frequency. The rate is telling me the loudness. Right? And this is something that is very common in biology. Where the neurons are firing tells us one piece of information. And how much the neurons are firing tells me another piece of information. And those are simultaneously present when

34:29biological neural networks create computation. And just to continue to bring this back to the artificial comparison, I think what's interesting here is this is >> different than just being zeros or ones. Yes. >> Because you actually have two levels of of input. You have the the the place which you could uh let me not make that direct comparison, but the point is it is not as simple as a binary system. Yeah. >> It's more of like of a of a twostate modulator. >> Yeah. No, I think I think you're on to something here in that the neuron is kind of an analog system, right? In that sense. Now, some people will argue that the neurons are still digital because

35:10they are sending out spikes and if you were to condense time and discretize it into smaller and smaller segments, then in each time window, the neuron is either active or inactive. Right? And the idea of a high rate is just there's more time windows that it's active, right? So, there is there is a really healthy debate in the neuroscience community about whether to interpret neurons as analog or digital. Okay. Right. And I don't know where I fall on that. Okay. Um >> that's fair. >> But it's it's it I think it's a fair point that you're making like it's a very important point. >> Right. Right. Right. is like is like that that is that is still up for grabs >> because the answer to that will also then be deter will be relevant to like

35:52our conversation about LLMs versus other models that we had in the last episode in terms of if >> anyway you get the idea. >> Yeah. Yeah. So to be clear I just want to reiterate here what we're doing is using something that is in place and something that the brain is already used to which is place and rate coding. In the idea of the cookia, the place along this axis was the frequency and the rate at which that