352 | Bing Brunton on Connecting the Connectome to the Body

Introduction

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Podcast Introduction

1:30Hello, everyone. Welcome to the Mindscape podcast. I'm your host, Sean Carroll. As you are listening to this podcast or listening to anything else or looking at anything else, your brain is processing information. We can argue about how much information is in the podcast or anywhere else. But in some sense, there are bytes of information being sensory inputted into your brain and then processed. And that affects what you do, how you behave. Now, as we've talked about in the podcast recently, there's other things going on in the brain and the nervous system and the body as well.

2:08It's not just information processing. There is absolutely information processing happening, but that's an abstraction, right? What there's actually happening are atoms, molecules, cells doing various physical things. And we find it very, very interesting and helpful to talk about those physical processes in terms of information being processed. And today we're not going to worry about deep questions about whether or not that information processing is efficient for consciousness or anything like that.

2:40We're going to get our hands dirty a little bit and think about the connection between what goes on in our brains, our nervous systems, and our bodies. There's a constant interaction. In fact, it's even, of course, a little bit of a mistake to separate our brains from our bodies because our brains are part of our bodies. So in reality, we're going to be talking about interactions between two different parts of our bodies, how we move around in the world and how our brains send signals back and forth, receiving signals and then transmitting them to the nervous system, which then does things.

3:15We've also talked recently on the podcast about the connectome, the idea that if you knew every neuron in a brain or maybe some coarse-grained version of groups of neurons and how they connected to each other, you would have the wiring diagram of the brain. And so we have some wiring diagrams for simple organisms, nowhere close to human beings yet, but we're working on that. What does that give us, knowing the wiring diagram, knowing how that information flows around?

3:46How does that then go into controlling our bodies and what we do and our behavior?

Connectome Discussion

3:52So that's what we're going to be talking about today. Bing Brunton is a neuroscientist and biologist at the University of Washington, and she has been leading the charge in very recent days. We have mapped out the connectome of the fruit fly. You might know that we've mapped out the connectome of C. elegans, the little worm that biologists like to study. It's only 300 neurons, right? The fruit fly has over 100,000 neurons, and now we've mapped out that. So that's a much more subtle system, a lot more intricate things going on, little subsystems doing different things.

4:27And so we're going to be talking about how we can learn about the relationship between the fruit fly brain, such as it is. There is a brain there. It's pretty impressive, actually. And how the fruit fly does things like walking around, flying, other kinds of things. This is absolutely new stuff, less than a year old, and just the beginning. Of a forefront, a really interesting research in biology and neuroscience. So let's go.

Guest Introduction

5:11Bing Brunton, welcome to the Mindscape podcast. Thanks, Sean. I'm glad to be here. So I think that for this audience, it would be good to start pretty broadly because the brain is kind of like time. I've written books about time. And what I've noticed when I wrote books about time is that everyone has an opinion about how time works, what it is, things like that. And I think that maybe the brain has a little bit of that, right? We all have brains. People have their opinions about how it works. I'm rather attached to mine.

5:41Yes, exactly.

Connectome Definition

5:44But the connectome in particular is something we have talked about in the podcast before. But why don't you give us the high-level overview of what the connectome is, how the neurons work, all that fun stuff. Yeah, that's actually – yeah, you went right for it. I think there's actually a little bit – some of the confusion around connectomes is exactly what it is. Because people use that word in a different way, and I'm sure you know the terminology actually does matter here, right? So I think the rough definition – and my colleagues actually differ on this, and so I'm going to try to channel them a little bit.

6:16The rough idea is that we all know that the brain is composed of cells because it's an organ, like every organ in your body. So it has cells, and the cells work by electrical activity, and they talk to each other through electricity. And so unlike an anonymous net of cells that are just kind of passing messages forward and backwards, the cells actually have specific identities. Some of them have specific jobs, and they also have specific localization. Some cells are found in different parts of the brain and nervous system, and some parts are not, right?

6:47And so there's essentially a wiring diagram, so to speak, of the brain. You can think about it in terms of – if you're like building a really big, complicated building, right? You're building a skyscraper or something. You would have a wiring diagram, you know, literally an engineering diagram of, okay, so this is where the transformers are. I'm going to flip this switch, and this thing's going to turn on these lights over here, right? So you can sort of have a diagram of that, and that's sort of the connectome, roughly speaking, is that for all the cells and their connections. And the identities in the brain.

7:19Now, the difficulty comes in in terms of how do you actually define the units? Like, do you want a connectome that's necessarily at the scale of individual cells and how they're connected to other individual cells? Okay. Okay? So that's one way people have used that term. But there's sort of, like, more what we call mesoscale connectomes that exist as well. In particular, because there's certain animals that are so big, like humans, for example, or even smaller rodents, where we can't really get – technologically, we don't have the capability of getting the cell-by-cell connectome.

7:54We just can't do it. Some people think we should. Some people think it's impossible. Some people think even if we could have it, it's useless. But nevertheless, we have these, like – like, if you hear about the human connectome, the human connectome is not the scale of cells and how they connect to each other. It's about – it's mostly, like, brain areas and how the brain areas connect to each other. So people use that term to mean, like, an area-by-area connectome as well. So there's some coarse-graining involved. There's a lot of coarse-graining. And so people don't agree on how they use that term.

8:26Okay. Right? Okay. So the whole omics thing in biology, so every word that ends in omics, like genomes, proteome, transcriptome, right? It's supposed to mean comprehensive map thereof. Hmm. Okay? Now, people usually agree. If I tell you, hey, Sean, I got a genome of a new, I don't know, spider that I found, you would expect that genome to be at the resolution of the base pairs, the A's, C's, G's, and T's. Right? Like, you have that expectation. If I gave you something else, you're like, that's not a genome. I don't know what this is, but it's not a genome, right? So we don't have that in connectomes.

8:59Like, we don't quite agree on the scale of description of, like, what is, like, do you need to have every single neuron in that spider for that to be the connectome of a spider? Right? Well, the human brain has, like, 85 billion neurons.

Known Connectomes

9:15We do have some maps of connectomes of more manageable creatures. We do. Some. We'll get there. I did notice you were kind of very careful there about talking about cells rather than talking about neurons. I presume that's because there are other cells. There are other cells, and they're clearly important. So the rough estimate, in my understanding, is that half of the cells in your brain are not neurons.

9:45They're, I mean, our word for not neurons is just glia, which doesn't mean anything except just the word for it. And they're clearly important. People used to think that they are just there to, you know, like, kind of like custodial staff or something, but that's so trivializing. They do a lot more than that. They clearly are involved in all kinds of vital functions, and they have their own dynamics. But we don't understand. I think that's, like, a really, really exciting emerging field in neuroscience is understanding all of the other cells in your brain and what they do and what they do in concert with the neurons.

10:18Let me demonstrate how ignorant I am about biology. I mean, you said the body is made of cells, et cetera. Is it entirely made of cells? Like, is everything in our body cells? There's got to be, like, just some liquids and solids and things in there. Oh, for sure. Yeah, there's definitely stuff in the extracellular space. Yes. But I think all I meant was that all of life as we know it is made out of cells. Right. We can quibble about viruses later, but, you know, living organisms are composed of cells. Yeah.

10:48But I think one of the lessons that we're going to be bumping into over the course of the podcast is biology is messy. Things are more complicated. It's squishy, yeah. Squishy and interconnected and complex. And, I mean, maybe one of the things to keep in mind is that a macroscopic organism is pretty much a matter of teamwork between different kinds of cells, but also cells and non-cell substances. Yep. Yep. All stuff, right? Like, for example, your bones, right?

11:20Your skeleton.

11:22You probably know that it's made out of, you know, lots of inorganic compounds. Like, there's a lot of calcium in there, right? So you drink your milk. Your mom tells you to drink your milk. But your bones, even though the skeletal elements of it, a lot of these material properties come from the calcium matrix and lots of other stuff that's going on that's kind of complicated. It's also this really intricate, meshy structure that has blood vessels all inside it, right? Right. Because it needs to be vascularized. Otherwise, it's going to die.

11:53It needs sugar to be fed. It needs oxygen to stay alive. And so even something that you think is structural, like, it's not like a stainless steel beam in a building. It's alive, right? And it's alive in a way that only cells can keep it alive. And so there's cells all inside it. And if you just, like, zoom in, it's got a very intricate structure. I do think this is not what we're talking about, but I do suspect that that's got to be a frontier of artificial organism building. Like, when we build robots, we make steel beams. We don't make it out of cells.

12:24And that means it doesn't repair itself, et cetera. We think about that quite a bit. And so not only, I mean, this is relevant for our thinking of connectomes, but really it's just a really great fundamental question of biology is how organisms are able to recover from injury. Yeah. And repair ourselves, or sometimes not.

12:48Yes. Well, you and your friends are going to figure out how to make all of my organs repair themselves and make it soon, okay? I mean, we're going to try. It's going to be fun. We'll try. Yeah. Okay. Just to follow up the last little bit, very interesting that half of the cells in my brain are not neurons. They're the other things, the glial cells. So, again, we have this cartoon picture in our brain of the neurons firing signals back and forth to each other.

13:20Is it that feature that distinguishes neurons from non-neurons? It is. Yeah. And so the connectome is the fine-grained connectome, if you want to call it that, the level of cells. We can call it the cellular level one or the neural one. Cellular level connectome. Neuronal. That would be just a big old matrix listing every single neuron and how it connects to every other neuron. Yep. Exactly right. Yeah. Okay, good. From a computational perspective, because I am a computationalist, by the time it gets to me, it's that gigantic connectivity matrix.

13:55And it has structure. It's sparse. It's not at all random. It's all kinds of cool. Is it, it's not symmetric either. Neurons, like, talk to others, but they don't listen necessarily. That is correct. Okay, some asymmetry there. Yeah. And in the, but, but okay, so is it, is the, is technically the connectome just the wiring diagram, or is it that extra information about where information flows? So there's a lot of extra information in it. And so this is what I'm, the analogy I tried making earlier about the genome as well.

14:28Like, we don't even understand, we don't agree on the, the, the correct way of representing the information. So that giant connectivity matrix you talked about, Sean, is, is definitely a part of the information, but it's nowhere near all the information that we get out of this technology. So, for instance, it matters the identity of the cells, because the neurons are not, they're, they're, they, I mean, you've probably heard about things like dopamine, serotonin, right? Like, they're dopamine cells, they're serotonin cells. And if they both fire an action potential, they both say something, those messages are completely different.

15:01Right? And so the identities of the cells matter. Yep. The other thing that really matters is how those messages are received. Right? Okay. So, so, so, so in analogy with, with kind of, it's, it's very like context dependent language, trying to think of a, of a, of an interesting social analogy. Like, if you say the same thing to two different people, depending on your relationship with them, they can hear very different messages. Right. Does that make sense? Yep. A hundred percent.

15:31So when cell A speaks to cell B, and the cell A says exactly the same thing to cell C, depending on the identities of B and C, they could hear very different messages and do very different things with it. Sure. If you say you're a bonehead to your best friend, it's received differently than if you say that to your graduate students. Right? That is, that is, that is entirely correct. Right. So, so message messages are received differently. Yeah. So that's why we care. Thank you, thank you for coming up with analogy. Yeah. So the, the, the, the, the, the, the identities of the cells matter.

16:02Um, there's other, lots of other really interesting, but also very detailed biophysical properties of each cell, um, that clearly do matter. But, um, we don't know by how much, um, so, so the thing that I usually try to, to tell my, my, my, my, uh, my graduate students when I'm first introducing them to this, this type of modeling that we do is, um, like, say that I'm trying to, I'm a, I'm a, I'm a, you know, I'm a civil engineer. I'm trying to, I'm trying to build a building.

16:32Right. And I need some materials to hold up the roof. And I say, I need to know the, the properties of this, of this beam so that I can hold the roof up. Now the beam is made out of, of, of atoms. And I know that, that there's, you know, there's like down there, someone there's quantum mechanics. Right. But we are not solving Schrodinger's equations in order to design a roof. It's just, it's way too much. So like, it's super interesting. And maybe you'd be interested in on the side, but you don't need it for the, for the task of building a roof. So that's sort of where we are right now. Like, I know I don't need every single detail that is known about these biophysical parameters of these cells.

17:07They get really funky. Good. Like they're a crazy nonlinear and they're super special and they're almost impossible to measure. Like people will spend an entire PhD measuring one cell and like characterizing a lot of detail. But do we need it for these very holistic models of the entire animal nervous system? Probably not. Where do we stop? It's hard to say right now. Right. Like I don't, I know, I know, I don't need every single detail, but I do not know which of them are actually crucial.

Biological Complexity

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19:53For a limited time, our listeners get 20% off their entire order with code MINDSCAPE at rag-bone.com. That's 20% off at rag-bone.com with promo code MINDSCAPE. When they ask where you heard about them, please support our show and let them know we sent you. And the individual neurons are different, not only sort of structurally or biologically, but even in terms of information processing, right? Like they have different, I don't know, I want to say algorithms for turning input into output.

20:27Is that fair? I think that's fair. Yeah, so if you think of it computationally in terms of just maps, right? If you are able to define exactly what its inputs are and what its outputs are, then you can infer some kind of function that maps it from the inputs to the outputs, right? I think that's a totally valid way of saying it. And I think that might be one of the clues, computational clues as well, in order to be able to run some of these simulations, is that you don't need every single detail of that, how that map is implemented to approximate its function.

20:58But is the specification of how each neuron maps inputs to outputs part of what we call the connectome, or is that a next step? It's not. It's not, okay. So, I mean, I don't know. It's hard to say, right? But I feel like this is partially why I, among some of my colleagues, I'll admit, you can't, you know, I mean, the audience can't see, but I'm raising my hand right now as, I was skeptical, okay? So this whole thing started, I don't know, I feel like I was in grad school when I first heard about these, like, really large efforts to produce more connectome data sets.

21:35And whatever, it was like, whatever, 15, 20 years ago. And I remember thinking that's, like, well, I won't tell you what I actually thought. But I was skeptical. I was skeptical. I was skeptical that on a couple of different fronts, I was skeptical that it was even going to work at all. Right? Like, can we actually reconstruct one of these things at sufficient scale? Because it involves, I don't know, like, running a transmission like Chromex code for six months straight, making zero mistakes.

22:02And so I was skeptical it was possible even to do it in the first place. And then I was further skeptical that if we could have it, right, like, if somebody just handed it to you magically tomorrow, like, what would you do with that? Right? Like, how could you even make sense of this giant spaghetti monster that somebody just handed you?

22:20And so I think some of our, I mean, it's only been pretty recently that some of the work that my lab has been doing with some collaborators has started to convince me that, hey, this might actually, I think we might actually be able to do this. Now, the reason I was skeptical, and lots of other people were skeptical, so there were essays written, I don't know, ballpark 10, 15 years ago by lots of people in the field, including, like, Eve Marder, Corey Barkman, is because they knew that there were so many other details that are not observable by the connectome.

22:52Like, this information about all of the channels, the biophysical properties of some of these cells, we can't get them from the connectome. We know we can't. We never thought we could. Nobody thought that we could, right? So the disagreement was whether or not the stuff that you can measure, effectively, these connectivity matrices, is that sufficient to teach us something? Is that good enough to do something, yeah. Yeah, versus sort of the other logical extreme would be it's utterly useless because you actually need all of the other stuff, right?

23:24And so there's a giant continuum of opinions, and I was, you know, I was somewhere in the middle, but, you know, kind of in the middle skeptical side, but I never actually worked in the connectome. I was simply fascinated by these efforts that some of my friends were undertaking. And my current opinion is swaying a little bit closer to the, I think we can actually do something useful with this data set.

Useful Connectome Data

23:45Well, having done useful things with them, I think that's a good opinion for you to have. So what are the connectomes that we do know something about, even if the human cellular level connectome is far away?

23:58What do we know? What animals do we have the connectomes of? So the first one we got was actually like 30 years ago. We have a full connectivity matrix of the C. elegance nematode worm. It's not an earthworm, like the kind you see sometimes attempting to cross the sidewalk and perishing in the middle. It's not those. They're much smaller. They're flatworms or nematode flatworms. They're about a millimeter long, and they live in the soil. So if you scooped up any soil in your garden and looked it under a microscope, you're very likely to see them.

24:34They're fucking everywhere. And so they're millimeter long, and this particular species has been studied a lot in molecular biology because they breed really quickly, and so we have tons of tools. They have about 1,000 cells and about 300 neurons. And so the connectivity matrix of those 300-ish neurons has been mapped out decades ago, many decades ago. And so if you talk to people in connectomes, one of the first things they always bring up is like, but we've had the connectomes of the C. elegance worm for so long, and yet we still understand it.

25:10We do not understand it. And there's actual good technical reasons why C. elegance worms are actually really difficult from a connectomics perspective to understand. And so the one that has come out much more recently in the last year or two is a couple of efforts by lots of giant collaborative teams. I was not involved in any of these teams. I was simply cheering them on from the sidelines to map the full connectivity matrix of a Drosophila fruit fly.

25:44Fruit fly. Fruit fly. Yeah. So this is the kind of fruit fly that every year at the end of the summer, my kitchen gets infested with fruit flies, and I can't get rid of them. So you've seen them, too, in your kitchen. They buzz around. Anytime you have a little bit of rotten fruit or a pile of compost or something in your kitchen, that's where they live. So these little guys are more like three millimeters long, and so they're like the size of a grain of rice.

26:16And their entirety of their nervous system is more like the size of a sesame seed. Okay. And so they're small enough that it has been possible to reconstruct the full, the entirety of their brain and nervous system. So we have a brain in our heads, and we also have a spinal cord. So that constitutes our central nervous system. So the brain and spinal cord of humans and mammals, right, and vertebrates.

26:48They have an analogous structure, so they have also a central brain that's inside their head. It goes down their neck, just like ours. And then the remainder of, instead of a spinal cord, insects and invertebrates have this thing called a ventral nerve cord. It's actually remarkably similar in terms of its structure and how it's organized to our spinal cord. But instead of being on their back, it's actually in their stomach side. So it's on their belly side. That's why it's called ventral nerve cord. Anyway, so that whole thing has been mapped out.

27:18And there's two of those data sets for one male and one female fruit fly, and that was only published in the last half a year or so. Wow. And how many neurons? So the brain has 150K, and the ventral nerve cord has an additional 22K. Okay, good. So a much bigger matrix than our little Z elegans. It's a much bigger matrix. And I think the important thing about the size of it, paradoxically, is that it's actually a little bit easier to understand from the connectivity matrix.

27:52Now, the reason that the Z elegans connectivity matrix has been so hard to understand is, it took us a while to figure this out as a community, they do a lot of computation not using that connectivity matrix. So there's a ton of chemical communication. They're constantly squirting out neurotransmitters and other chemicals at each other. There's a lot of mechanical computation. So it's a squishy thing that crawls around in a matrix, not a mathematical matrix, a solar matrix.

28:25And so there's a lot of mechanical stretching and reflexes that go on like that. You know the thing the doctor does when they like, yeah. Because they have those reflex loops that are mechanically coupled with their body, which is squishy, right? And so the physics of that is like pretty complicated. So the short way of saying it is that the way that they function as an animal is taking advantage of lots of other computational properties. So they do chemical communication, they do mechanical computation, in addition to neural computation.

28:56So the fact that we had the neural connectivity matrix was just not quite good enough to understand what they do. In contrast, it is some of our current understanding and perhaps hope that the connectivity matrix of the fruit fly, because of that it's a little bit bigger, it has jointed limbs, just like humans do. And it has enough cells that there are actual cell types, like not every single cell is just like its own little snowflake, like they actually have types of cells.

29:29All of those, we are hoping, makes it so that that connectivity matrix is more helpful, more directly helpful, like helping us understand what the heck is actually going on. In other words, because individual neurons, et cetera, might be more specialized or something like that, rather than just like every neuron pitches into every task. Right, yeah. So the CL against neurons, some of them are, I mean, they're so not specialized that, you know, how we have, you know, we have a visual system, so there's cells that detect photons, and we have olfactory systems, cells that detect smells, right?

30:04Like they have single cells that have multiple sensory modalities going into it, because it's just so tiny, it's so compressed, right? They've had to multiplex in that way. And we don't see that as much in our understanding in the worm, in the worm nervous system. And that's a feature, it's a computational feature of how their nervous system works that's in common with ours. And with the fly connectome, the fly neurons, I saw in, you know, one of your videos, these images of these neurons.

30:39And I think that people, certainly I have this image of a neuron, like a little blob with a couple little spikes. But these are very spindly things. They're stretching across some non-trivial fraction of the size of the fly. Right. Do you know the longest cell in your body? I do not know the longest cell in my body. It's about as tall as you are. That's a little freaky. I don't want to think about that. It is a little freaky. So you have these cells that, actually the same cells we were talking about in the ventral, so in the insects, in the ventral nerve cord, in your body, it's in your spinal cord.

31:13Okay? You have these cells that are responsible for how, this is how you know you stubbed your toe. So there's a cell that detects when you've stubbed your toe. So one end of it is at your big toe. Okay? And the other end of it, it goes all the way up to your brain stem. So the very base of your skull. So talk about long and spindly. Why does it need one cell to do that? Can't like a bunch of cells hand off the message? You can do it.

31:43No, this is the actual, this is the normal architecture. Okay. There are other cells involved and you can hand off the message. The advantage of having one cell do it is that you can do it really fast because as it happens, if you stub your toe, your brain really wants to know about it very quickly. Stupid brain. I don't think, I want to know about it at all. I just want to get all the way down. You want to know something. Well, this is how you don't, this is how you don't fall over. It's all the shit that your body does that like you don't think about. You don't have to think about not falling over. If you're hiking and you kick a rock, you don't want to fall over.

32:16And you also don't want to waste your precious time thinking about how to not fall over. You simply want it done. Okay. You want to keep on having that conversation about number theory you're having with your buddy. Right. You don't have to think about how to not fall over just because you kicked a rock. Which segues very nicely into the actual work you've been doing with the fruit fly connectome. So you have the connectome. That's good. And then there's this open question that you elucidated very nicely. Is it good enough to help us do anything?

32:47And you've been asking, what is the relationship between the connectome and walking in the fruit fly? Is that right? That's right.

Walking Rhythm Generation

32:56So I don't know. How do you even start with that? What do you do? Well, so the slightly longer story is that this is a long-time collaboration I've had with a friend and collaborator of mine, John Tuthill. And John is a fly experimentalist. His lab does neurophysiology. And they study the ventral nerve cord and the sensors that come in as well as the motor control that goes out. That's what his lab does. And we've been collaborating for like a decade now.

33:28And have co-advised a series of graduate students and postdocs doing some combination of theory and modeling. And it's been super fun.

33:35And so John's also been really involved in some of these connectomics efforts. So a lot of what I said, the stuff that I know that is not wrong is because I learned it from John.

33:45Stuff that is wrong, I made that up. I take responsibility. And so I remember a couple of years ago, John and I were taking a walk. And we had a brand new PhD student who was thinking about joining our labs. And we're like, oh, what do we have them do? Like, we got to think of something, right? And John and I were talking. And he's like, well, we almost have a ventral nerve cord connectome. It's like, it's almost ready. Because they were like in the process of cleaning it up, curating it, trying to write it up, right?

34:17He's like, what if we just like simulated it? And I said, that's never going to work. Let me tell you all the ways it's not going to work. So I told him all the ways it was not going to work. Some of which I summarized earlier, you know, the biophysics, all the parameters we don't know, blah, blah, blah. There's tons of stuff. There's lots and lots of reasons it wouldn't work. But by the end of this talk, we had come to, well, you know what? Let's try it anyway. You know, it's not going to, we don't lose anything. Let's give it a good old grad school try. I do, by the way, think that like half of the secret to succeeding in graduate school

34:49is listening to your advisor tell you that won't work and distinguishing when they're right from when they're wrong. Absolutely. So our student, Sarah Puklesi, listened to us and she said, okay, we went off and wrote some code.

35:05So I'm, of course, long story short, it took a couple of years, but we kept at it. Partially because some of the preliminary stuff is actually, it was kind of interesting. There were some hints, right? And what ended up happening is that we went after a question that biologists and neuroscientists have been asking for over 100 years, which is this question of how does the nervous system generate rhythms from non-rhythms? How does this happen? And to tell you, give you a context a little bit about why this is such an important question,

35:39all animal movements are rhythmic. Actually, not just animals, like even bacteria move by spinning their flagellum, right? So basically all biological movements are cyclic in some way. So you can be walking, running, swimming, slithering, crawling, like basically all locomotion is rhythmic, right? And so the fact that your nervous system needs some way of generating the instructions for

36:11your muscles to move in a circle, that's fundamental, right? This is like one of the, and so we've been, ever since the 1910s, some of the first experiments demonstrated that the generation of these rhythms is not by reflex only, is that your central nervous system, somewhere in your brain is spinal cord, and spinal cord, was capable of generating these cycles.

36:37But we didn't know exactly where, we didn't know which cells did it, we didn't know how they did it.

Central Pattern Generators

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37:13We shop for everything at home now. Why can't we shop for blinds at home too? That's why I love 3-Day Blinds. Instead of wandering around a store, 3-Day Blinds sends a professional design consultant straight to you. They bring all the samples, help you see what actually works in your space, take expert measurements, and even handle the installation. We've all bought something that looked great in a store and looked terrible once we got home. With 3-Day Blinds, you see everything in your actual light with your actual furniture before you commit. Plus, whether you're home or away, you can control your window treatments with the 3-Day Blinds app.

37:48So for a free, no-charge, no-obligation consultation, head to 3dayblinds.com slash save50 for our buy one, get one 50% off deal. That's the number 3-Day Blinds.com slash save50. And this, so just by the way, like the idea of some system of mechanical things, cells or anything else, vibrating in periodic ways, that's one that appears all over the place. All over the place. We understand that, yeah.

38:19We understand this in general. And if we have time, I'll come back to, like, I love dynamical systems. And so we can nerd out about the dynamical systems of oscillator equations a little bit later. And it actually has connections to our work in the connectome as well. But yes, absolutely, yeah. And so in the intervening 100 years or so, lots of people have studied this, the idea of these circuits. And so the ability of your nervous system to generate rhythms is not only important for locomotion.

38:49It's also important for certain things like breathing, right? Because you have the inhale, exhale, inhale, exhale. You can control it, but if you don't think about it, it just happens. And so that's generated by what we call a central pattern generator, a CPG circuit as well.

39:06Digestion is cyclic, right? Yep. So you have to turn the stuff in your digestive system. So when you, there's a, yeah, so there's a sequence of muscle contractions that gets your, gets the food to go down, right, into your stomach. And in your stomach, especially the stomach, so the most studied CPG circuits, intrapodidinary circuit is actually in the crab digestive system. There's a couple of these like adorable little neurons that, that are responsible for churning

39:36what's in the crab stomach goes. It's cold, cold, cold, cold, cold, cold. And it makes that rhythm. And you know which neurons are in charge? This is the work of Eve Marger. She is known for having studied this for decades. And that system is so extraordinarily well understood. We actually understand. It's probably, sometimes people are a little snarky and we say like the crab digestive circuit is like the only neural circuit we actually understand in all of neurobiology. It's a bit of an exaggeration, but it's not untrue either. Like we actually understand that circuit.

40:07And the thing we're looking at, so the idea of central pattern generators, these are little sub-circuits within the connectome that are responsible for, is it always cyclic rhythm motions or is there a more general definition? That's probably the plainest definition of it. And then, so the CPG is, I mean, like roboticists love the CPG. So a lot of modern robotics is built on these oscillator equations.

40:38So they don't even, like I've talked to roboticists who like actually have no idea about the neurobiology of central pattern generators because for them it doesn't, they don't care. They just write an equation. We've done the same. So a lot of these like computational models of locomotion in animals and robotics, it's just based on a, you just write an oscillator equation. It just goes around in a circle. It's not, you write, there's not lots of them you can write. It doesn't really matter how it's implemented by cells. You just care that there exists a thing that goes in a circle, right? But we didn't know what actually were the cells and their connections in an actual nervous

41:12system that generated these rhythms for any animal that walks. Okay. So that's where we were a couple of years ago is like nobody had ever actually found what, what are the cells? What are their names? How do they work? So in other words, you knew from prior experience with digestive systems and breathing that there had to be these CPGs, central pattern generators that would do these rhythmic motions. You also know that walking is kind of a paradigmatic rhythmic motion. Yeah. But we hadn't quite identified. We hadn't quite defined the actual cells.

41:43And so to be fair, people have studied lots and lots of, of, of, of walking systems. People like there's tons of like, just like whole bookshelves in the library about spinal circuits of walking. Invertebrates have these ventral, ventral nerve cores. How do they generate their wing flapping? How do they walk? Like people have tried and there's tons of information, but we didn't know precisely which one, which cells they were and how they worked. All right. So what are you going to do?

42:14So we had an opportunity. It's not like we were smarter than all of these other people who have worked on it. It's just that we had an opportunity of having the complete connectivity map of the ventral nerve cord of a fruit fly. Right. And we figured whatever it is, it's got to be in there somewhere. Right. Like we don't know, like instead of starting from building it up from individual components that I can actually do experiments on, we took the reductionist approach. We're like, it's in here somewhere. You know, we got it down to, you know, a network of 4,000 cells or so.

42:45We're like, it's got to be in here somewhere. So wait, when you say you got it down, you're basically like saying, okay, we have 150 or 170,000 neurons. And you like eliminate, like you say, like if I didn't have this one, it could still walk fine. Precisely. So we simulated, so we simulated, okay. So first thing we did is we, to make it a little more manageable, we focus on only two front legs. So insects have six legs. So we just got rid of the other four. We're like, okay, let's just have two legs, the two front legs.

43:16We got rid of all of the parts of the nervous system that don't control the two front legs. So we just have two front legs. That's how we got to 4,000-ish. Okay. So then we simulate that, and we were able to demonstrate that those 4,000 neurons were able to generate a cycle. They can generate motor rhythms and actuate the muscles that would have to move the leg. Again, you can't see me, but I'm moving my arm forwards and backwards. It actuates these muscles right here on your shoulder, like the ones that move your shoulder

43:47forwards and the ones that move your shoulder back. Okay. So just by simulation, again, we are doing no reinforcement learning. There's no machine learning here. There's actually no deep learning going on at all. We're just doing brute force numerical simulations of this giant connective matrix. Shallow learning. Regular numerical simulations. It's not that we've been doing for a long time. You write a lot of code and you run it a million times. We can get these rhythms to come out, right? So then we asked, now that we have these rhythms, now that it's actually in here somewhere, now

44:17let's try to reduce it. Now let's cut away one at a time. We basically just started getting rid of cells. We're like, do I need this one? No. Do I need this one? No. And you just keep going until you've thrown away as many cells as you possibly could without losing the rhythm. And what you have left over is the minimal circuit. Does that logic make sense? I think it does. And so this is just one leg, or I guess it's symmetric. The front two legs are doing the same thing. By the way, let's just take an aside to explain the fascinating question, which is the wings.

44:48Yeah. Yeah, I know. It's wild.

44:52So I would have thought from my mammalian-centric point of view that wings are just like, you know, arms that have grown wing legs, but flies are very different. Not so. Not so. So this is something my friend and collaborator, Michael Dickinson, is very fond of saying. The insect wings are actually novel limbs. Right. I'll explain what that means. So for every other animal that flies, like bird wings are modified arms. Bat wings are modified arms.

45:23Right? Mm-hmm. Other animals that fly have wings that used to be not wings. Right. Not so of insect wings. They're not modified legs.

45:35There's theories about exactly how they evolved, but they're actually novel structures. It's not like they took a pair of legs. It's not like they used to have eight pairs of eight legs and two of them became wings. These are just actual new things. And this is reflected in the nervous system. Say it again, please. This is reflected in the nervous system. It's very much reflected in the nervous system. So just like there's these little parts of your spine that correspond to it. Like you have parts of your spine that's like this goes to the left leg, this goes to the right leg, this goes to your trunk, right? Like same thing. They have parts of their ventral nerve cord that go to each of the six legs.

46:08And you can actually see them. They're like little balls that kind of stick out. They're a little bit bigger because they have more cells. And then they have the same thing. Like there's little clumps of cells that correspond to the wings. Cool. Okay. So there's a whole separate future research project understanding how flies fly. You're trying to understand how they walk. Yes. Just walking for now. And how did that go? It worked great. So the pruning study that I briefly described earlier where we took a functioning system that was able to generate these CPG-like rhythms.

46:41And then we started just pruning it. We started cutting away everything computationally that didn't seem necessary for it to be there. I remember I was sitting in actually this office with Sarah and with John the day we figured out, okay, let's give it a try. You know, like let's do this pruning study. So remember we started out with 4,000 cells. And I remember telling Sarah, Sarah, just go give it a try. You know, if you get it down to a few dozen cells left over, like if that's the minimum circuit, you need a few dozen cells to do this, I would be ecstatic.

Minimum Circuit Discovery

47:12Like that would be a really cool result. She went off and did it. The answer was three.

47:20Three cells. Three cells. That's the minimum you need. And they have names. We know who they are in the fly nervous system. Tell us their names. That would be fun. That is a great question because I actually have no idea what their actual names are. Look it up. Okay. Their names are known. They have, their names are known and their lineages are known. So we sort of know where they came from. The names are, I can't, I can't handle this. The names are like a series of letters and numbers and I can't remember what they are. You're the one who said we know their names. That's the only reason I asked.

47:51Okay. We, the royal we. The royal we. Like John knows what their names are. So I have no idea what their names are. We gave them pet names though. Of course. Of course we had to give them pet names. And, and, and they're not too cute, but so there's three cells. And remember I told you earlier, the cells have identities. It kind of matters like what type of cell they are. Yeah. So two of the cells are excitatory. They make other cells more excited. And one of the cells is inhibitory. It makes other cells less excited. And so they're called E1 and E2 because there are two excitatory cells. And the last one is I1 because it's an inhibitory cell.

48:24And they are connected in a particular, very understandable architecture motif that explains why this tiny little circuit is capable of generating cycles. Well, maybe this is the place then to get into dynamical systems theory a little bit. I mean, because my next question was, how do three tiny neurons manage to tell the leg how to walk? So, okay. So I will, I will, I will be a slightly more precise to say that they are, they are, we believe the three neurons are sufficient to generate the rhythm.

48:59Okay. The rhythm. They can generate the rhythm. They, they're not sufficient to actually control them. They have, I don't know, like dozens of, of individual muscles that need to be coordinated in their legs to be able to walk. Like we have many more, but you can get the idea, right? Like there are many more muscles and there are degrees of freedom in a limb. So actually controlling them to do something coordinated and not super clumsy is a little more complicated. But we believe these three neurons, our hypothesis is that these three neurons generates the basic rhythm.

49:31And then there's other cells involved to make it actually walk. Does that make sense? Okay, good. And that's just the lesson we're learning over and over again. There's a lot of teamwork in biology. A lot of, a lot of responsibilities shared among different subcommittees. I certainly don't feel like the, the nervous system is wasting cells. Right. Like we have all these cells, they're doing something, right? Like I just don't, I don't, I don't, I don't, I don't, I mean people have all these ideas about like low dimensional structures and neuromanifolds and I don't know. There's words thrown around if you talk to some, some, some other neuroscientists.

50:05I just don't, I don't, I don't think biology is wasteful in that way. There's redundancy and that has, there's a good reason for the nervous system to be redundant in case it gets injured, et cetera. Right. I don't think there's waste. I don't think we have cells for no reason. And if it's there, there's probably a pretty good reason it's there or it wouldn't be there. Well, it's possible. Like what, what do I know? But I can imagine that it used to be useful and then the evolutionary use of it sort of went away, but the cell lingered for a while. Because the cells are so expensive to maintain, neurons are some of the most expensive cells to maintain in your body.

50:41I think it would, my hypothesis would be that, that if, if a cell is actually not necessary, it would, the body would find a way for it not to be there over a longer time frame. So it's actually more plausible to have vestigial organs in the body than vestigial neurons. If you're thinking of the vestigial organs that I'm thinking about, there's actually just like, I mean, we can go off on a super long tangent if we wanted to. That's a different cycle we can go on about why those vestigial organs didn't go away.

51:12And there's usually a good reason because they got stuck, basically. Like not that we, like, not that we had a use for them, but just because, you know, the way that evolution works, just, they got stuck. Okay, let's, let's go back to our three neurons, E1, E2, I1. Right. And so there's a, like, really oversimplified spherical cow version of this where it's literally a circuit. It is. And it is, it is constructing a rhythm. And then there's the slightly more complicated version where there's external inputs and outputs and other influences going on.

51:51And, you know, how do you learn about all those? Yeah. So, so to learn about all of the other stuff, I think what my lab, our vision, and there's tons of collaborators who are involved in all of this because we, this is kind of a giant team effort, is to then actually embody the nervous system. The connectome in all of its glory, actually put it inside a body where it belonged all along. Right. Like a mechanical body or?

52:21A simulated body. More like a video game body. Yeah, a video game body. Okay. Yeah. So my son's been playing, like, Red Dead Redemption. He rides a little horse around in this virtual little environment. It's a clump, clump, clump, clump, clump, right? Yeah. That's just an animation, right? Like, it doesn't really matter if it is biomechanically realistic, physically realistic, biologically interpretable, whatever. It's just a video. So we want to do that, but actually have it be biologically interpretable and then also physically realistic as so far as we can. But it would be a physics engine, right? Models F equals MA. Yeah. Right. Good. So, sorry. Is that going on?

52:52Does that exist? Did that help? Did that teach you anything? It's in progress. I think it's in progress. I'm really excited about it. It's, I mean, this is a bit superlative, but I feel like I've rarely in my career felt so much conviction that something is the right thing to do. Like, I just, I feel, it's so obvious to me that the brain does not live in a jar. Right. It always controlled a body and it always controlled a specific body with these limbs and these muscles and these joints and these sensors, right?

53:26In order to move around the world and eat and collect information and do all the things that animals do. And so it's just so obvious to me that we need to be understanding the brain and nervous system in the context of the body that it interacts with to produce the behaviors that the animal actually does. And so that's the grand overall vision of what we're doing. I'd love to be able to, I mean, we are, we're like, it's early days, right?

Embodied Brain Simulation

53:51But it's just, this is, I'm really excited about it. Is there any usefulness in imagine doing it in good old fashioned physical reality as well as virtual reality? Yeah. Either a robot or can you like hijack the nervous system of an actual fly? For sure. Super easy to hijack the nervous system of fly. As part of the reasons we're working in the flies, because it's, it was the, it was the kind of the genetic organism of choice for a very long time. And so our ability to hijack every aspect of its nervous system, do gene engineering, to put proteins in it, to shine lasers at it, all of that stuff already exists.

54:31And that, that is the reason we're working in flies is because the wealth of knowledge that has accumulated over the many decades of people working on the fly. Like we just know so much more about their everything than a spider, for example. Right. So yeah, we can hijack it. So a lot of the things that, I mean, we're neuroscientists, we love lasers. So there's a lot of lasers going on. We shine lasers at them and we can make them do things. We can shine lasers at them when they're walking, flying, trying to sniff, stuff like that. We shop for everything at home now.

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55:34So for a free, no-charge, no-obligation consultation, head to 3dayblinds.com slash save50 for our buy one get one 50% off deal. That's the number 3-Day Blinds.com slash save50. On this episode of Plant Killers, we'll explore one nation's most notorious fruit and vegetable killer, bad dirt. What makes bad dirt so bad? The answer, the ingredients. But fear not, true crime enthusiasts. This story has a happy ending. Miracle-Gro Organic Raised Bed and Garden Soil.

56:06It's made with quality organic ingredients from upcycled green waste like compost and aged bark. Unlike the other guys who can't say the same. Looks like Bad Dirt's murdering days are over. Thanks to Miracle-Gro. Join us next time on Plant Killers. I don't think that the sentence, we're neuroscientists, we love lasers, is that obvious to the outside world. I didn't realize that lasers are the thing. Yeah, we love lasers. I don't know if we, I think we might love lasers slightly more than physicists do. Because we just play with them.

56:37Yeah, well, physicists are going to join you there. That's okay.

56:42And it sounds like, and maybe I didn't quite get it right, but it sounds like rather than learning about these three neurons by experimenting on the neurons, you almost guessed, or you almost sort of figured out they have to be doing this in order to make it work. It is a guess at the moment. We do need to do the validation experiments. We need to also corroborate our predictions and our hypotheses by doing experiments on these actual neurons. For technical reasons, that stuff is ongoing.

57:14We haven't done it yet. Sure. So that's why this is still, I would say, a very strong hypothesis in my mind. Right. It's our, we have good reason to make this guess, but it's still a guess at this point until we can confirm it biologically. But I think, like, one of the things that's kind of cool about this result is that as a computational modeling person, I've spent the majority of my career fitting data. Like, somebody has an observation, something they already know. So, and we're like, oh, sure, I can, like, write some equations in code and we can recapitulate it.

57:47We can, like, make a model that does the same thing that you already know. This is one of the few instances where I feel like the model actually came before the experiments. We were agnostic going in. We had this giant data set. We're like, let's just simulate it. And then we made a prediction of things we didn't know before. And so, part of this result I haven't talked about is that we haven't quite gotten to the three cells that we predicted to be the core CPG circuit. But there's other parts of the nervous system that we did predict. Like, we made some predictions of, there's this one pathway that comes down from the neck.

58:22And in our model, it was a cell that has a name, right? And doesn't matter, I do actually know the name of this. It has fewer letters and numbers. I know what it is. But this neuron that comes down from the central brain, and our model said, oh, okay, if you zap it with a laser, it should make the leg tap. It should go back and forth. Okay. Okay? And nobody has ever even studied this neuron before because there's a lot of them. But somebody did actually make a cell line. Like, there was a fly we could order that somebody had already made that had the correct proteins in it so that we can shine a laser at it and activate that cell.

59:02So we ordered it. We grew it. We cut its head off. And we glued it to a stick. And we shined a laser at it.

59:10Yeah. And it tapped its leg. Oh, there you go. It's adorable. It's like it was an actual model-driven prediction. Right. Like, we had no idea. Nobody had any idea what this neuron did. It was just in the nervous system. Most cells in the nervous system are like that. Like, we don't have a name because we kind of know where it came from. We have a nomenclature. We have systematics. We don't know what it does. Well, I guess that was my obvious next question. If you have three neurons per leg controlling the rhythm and there are six legs, that's 18 neurons, that leaves 150,000 minus 18 neurons to figure out what they do.

59:48Is there an obvious roadmap to what we're able to do? I sure hope so. So part of it is this idea of the embodied brain that I talked about. And it goes by a couple of different names. So drawing analogies between what we're doing with these virtual models of animals with the nervous system and the biomechanics of the body, we and some other people have been calling them digital twins, which is a word that we're borrowing from industry and from industrial engineering.

1:00:22So the digital twins that exist in industry are digital twins of things like airplanes and cities. Like, there's a digital twin of the city of Singapore, right? Okay. And it's a simulation. It doesn't have every single light bulb. But it has many of the important parts of the city of Singapore, like including its morphology, its connectivity. And it's hooked up to real live sensors in the city so they can sort of update the status of the city. And the city planners kind of use it to do things like predict disaster response, right?

1:00:55Or to in real time shift, if they have to shift traffic patterns or whatever to relieve congestion because of an emergency in one place, stuff like that, okay? So that's what people in industry have used these digital twins for. And in close analogy of that, the thing that we're thinking about building, I think, would be considered a digital twin of an animal, a behaving animal. So it would have a simulation of the nervous system and the interfaces between the nervous system and the body so that we know how information goes in and how information comes out.

1:01:26And it would be situated in a virtual reality environment that's capable of interacting with things, right? Like surfaces that are not flat, right? You can walk up a piece of physics. Physics. Yeah, just physics, right? They can also interact with other agents. So this would be an agent-based model. And so you can have two animals interacting with each other. They can even touch each other, for example, stuff like that. And so in that way, if we have a set of simulations that are developed in very close collaboration with our experimental collaborators, we should be able to come to a set of models that can predict what's going to happen in parts of these circuits that are hard to predict otherwise.

1:02:08Because the thing is, like, the whole thing has just mad, mad feedback and recurrence, right? And if it's one thing that I've learned about humans and our ability to reason through rational thought is, humans are really terrible at reasoning through what happens with feedback circuits and recurrence. Like, we can go forward. We can follow a path, like A to B to C to D. That we can do. That we can do. As soon as there's recurrence, when D goes back to B and then C goes back to A, our intuition for what's going to happen is really poor.

1:02:38Okay. And that's one of the arguments that I make in motivating why we need these complicated computational models. We can't do it, but we have computers. Well, I guess an obvious issue that floats to mind is when you are simulating the biology on the computer, you have to make some choices about what to include, what not to include, what to model, what not to model. Is there any danger you'll get sort of get the right answer for the wrong reason?

1:03:12Yes. So many, probably more than not. I think we need to be really careful. So this is, I mean, this is something that I think maybe we talked about briefly in person at some point is this idea of the digital Sphinx paper that we wrote a couple of weeks ago. And the brief intro to that is that there's a lot, I was starting to see a lot of work and conversation in the field, including by my lab and our collaborators, where because the whole thing is so overwhelming and there's so many details and we know we can't possibly measure them all.

1:03:55It's literally impossible. We know we have to make a lot of assumptions, right? And so a lot of people, again, including us, we've been doing the same thing. The thing that one thing that we can measure with a lot of fidelity and relatively easily is just the behavior output of the animal. We can get cameras and we can track what it's doing. We can see how it's moving its legs around. We can see where it's like pointing its head. That we can do. Anything external with cameras we can do because we have cameras and we have really good computer vision.

1:04:30And so a lot of people are basically saying that, okay, this is the grounding, right? Like if we can get a model that looks like it's behaving like the animal in that it matches what the animal was observed to do with a camera, then surely we've gotten something right.

1:04:50Sure. I know. I know.

1:04:54I'm glossing over lots of details. Of course, lots of people are doing this in a really careful way. But what I was a little afraid of was that people were starting to do this in a not careful way. And in particular, there was some stuff coming out on social media by some startup companies, you know, trying to fundraise, putting out work that I looked at it and lots of our friends looked at it and was like, that's not, you're overselling this, right?

1:05:25You're not doing what you said you did. Right. And what they said they did was that they had uploaded a fly brain.