352 | Bing Brunton on Connecting the Connectome to the Body

The connectome is the wiring diagram of a brain, a big matrix that tells us what neurons talk to what other neurons. Understanding it is an important step to understanding how brains work, but a long way from the final answer. A big next step is understanding how neuronal circuits connect to and guide bodily behavior. Very recent work on mapping the fruit-fly connectome has brought us closer to that goal. I talk with neuroscientist Bing Brunton about the connectome, how we can study it to understand bodily motion in flies and other creatures, and where it's all taking us. Chubbies is here to keep you comfy and looking good year-round. Get 20% off with code MINDSCAPE at chubbiesshorts.com/MINDSCAPE https://chubbiesshorts.com/MINDSCAPE! #chubbiespod Upgrade your denim game with Rag & Bone! Get 20% off sitewide with code MINDSCAPE at www.rag-bone.com https://www.rag-bone.com/. #ragandbonepod Support Mindscape on Patreon https://www.patreon.com/seanmcarroll. Blog post with transcript: https://www.preposterousuniverse.com/podcast/2026/04/27/352-bing-brunton-on-connecting-the-connectome-to-the-body/ Bing Wen Brunton received her Ph. D. in neuroscience from Princeton University.. She is currently a Professor of Biology and the Richard & Joan Komen University Chair at the University of Washington, with affiliations at the eScience Institute for Data Science, the Paul G. Allen School of Computer Science & Engineering, and the Department of Applied Mathematics. Web site https://www.bingbrunton.com/ University of Washington web page https://biology.washington.edu/people/bing-w-brunton Google Scholar publications https://scholar.google.com/citations?user=UftAYPkAAAAJ&hl=en YouTube channel https://www.youtube.com/@bingsbrain Bluesky https://bsky.app/profile/bingbrunton.bsky.social Artworks (Instagram) https://www.instagram.com/bings_art/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

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• 0:58Sean Carroll?

  Hello, 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. It's not just information processing. There is absolutely information processing happening, but that's an abstraction, right?

• 1:43Sean Carroll?

  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. We're gonna 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.

• 2:27Sean Carroll?

  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. We'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

• 3:13Sean Carroll?

  around? How does that then go into controlling our bodies and what we do and our behavior? So 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've 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.

• 3:54Sean Carroll?

  And 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 of really interesting research in biology and neuroscience. So let's go.

• 4:38Sean Carroll?

  Bing Brundt, welcome to the Mindscape Podcast.

• 4:41Speaker 03

  Thanks, Sean. Glad to be here.

• 4:42Sean Carroll?

  So I think that for this audience, it would be good to start pretty broadly because the brain, 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.

• 5:07Speaker 03

  I'm rather attached to mine.

• 5:09Sean Carroll?

  Yes, exactly. So, but 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?

• 5:23Speaker 03

  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. The 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

• 6:05Speaker 03

  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? And 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, a literally 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. Now, the difficulty comes in, in terms of how do you actually define the units?

• 6:50Speaker 03

  Like, do you want a connectome that's necessarily at the scale of individual cells? And how they're connected to other individual cells?

• 6:58Sean Carroll?

  Okay.

• 6:58Speaker 03

  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. We 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 scale of cells and how they connect to each other. It's about, it's mostly like brain areas and the, how the brain areas connect to each other.

• 7:41Speaker 03

  So people use that term to mean, to mean like a area-by-area connectome as well.

• 7:47Sean Carroll?

  So there's some coarse graining involved.

• 7:49Speaker 03

  There's a lot of coarse graining. And so people don't agree on how they use that term.

• 7:53Sean Carroll?

  Okay.

• 7:53Speaker 03

  Right?

• 7:54Sean Carroll?

  Okay.

• 7:54Speaker 03

  So the, 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. Okay? Now, people usually agree, if you say, if I tell you, hey, Sean, I got a, 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, C, Gs, and Ts, 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. It's not a genome, right? So we don't have that in connectomes.

• 8:27Speaker 03

  Like, we don't quite agree on the, on the scale of description of, of, of like, what is this, what is the, what is like, do you need to have every single neuron in that spider for that to be the connectome of a spider? Right?

• 8:40Sean Carroll?

  Well, the human brain has like 85 billion neurons. We do have some maps of connectomes of more manageable creatures.

• 8:47Speaker 03

  We do. Some.

• 8:51Sean Carroll?

  We'll get there. I did notice you were 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.

• 9:02Speaker 03

  There are other cells and they're clearly important. So the rough estimate of my my understanding is that half of the cells in your brain are not neurons. They'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 involving all kinds of vital functions and they have that have their own dynamics. But we don't understand. I think that's like a really, really exciting emerging field in neuroscience, just understanding all of the other cells in your brain and what they do and what they do in concert with the neurons.

• 9:47Sean Carroll?

  Let me demonstrate how ignorant I am about biology. I mean, you said the body is made of cells, etc. 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.

• 10:00Speaker 03

  Oh, for sure. Yeah, there's definitely stuff in the extracellular space. Yes. But but I think all I meant was that all of life as we know it is made out of cells.

• 10:10Sean Carroll?

  Right.

• 10:10Speaker 03

  We can quibble about viruses later, but, you know, living organisms are composed of cells. Yeah.

• 10:16Sean Carroll?

  But 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 than we.

• 10:25Speaker 03

  Squishy, yeah.

• 10:25Sean Carroll?

  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.

• 10:41Speaker 03

  Yep.

• 10:43Speaker 03

  Yep, all the stuff, right? Like, for example, your bones, right? Your skeleton. You probably know that it's made out of, you know, lots of inorganic compounds, like there's a lot of calcium in there, right? When you drink your milk, your mom tells you to drink your milk. But your bones, even though this skeletal elements of it, a lot of its 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?

• 11:17Sean Carroll?

  Right.

• 11:18Speaker 03

  Because it needs to be vascularized. Otherwise, it's going to die. It 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.

• 11:40Sean Carroll?

  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. And that means it doesn't repair itself, et cetera.

• 11:55Speaker 03

  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.

• 12:10Sean Carroll?

  Yeah.

• 12:10Speaker 03

  And repair ourselves, or sometimes not.

• 12:16Sean Carroll?

  Yes. Well, you and your friends are going to figure out how to make all of my organs repair themselves. And make it soon, okay?

• 12:23Speaker 03

  We're going to try. It's going to be fun.

• 12:27Sean Carroll?

  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. Is it that feature that distinguishes neurons from non-neurons?

• 12:51Speaker 03

  It is, yeah.

• 12:53Sean Carroll?

  And so the connectome is the fine-grained connectome, if you want to call it that. The level of cells.

• 12:59Speaker 03

  We can call it the cellular level one or the neural level.

• 13:02Sean Carroll?

  Cellular level connectome. That would be just a big old matrix.

• 13:08Speaker 03

  Yep.

• 13:08Sean Carroll?

  Listing every single neuron and how it connects to every other neuron.

• 13:13Speaker 03

  Yep, exactly right.

• 13:14Sean Carroll?

  Okay, good.

• 13:15Speaker 03

  From a computational perspective, because I am a computationalist, by the time it gets to me, it's that gigantic connectivity matrix. And it has structure, it's sparse, it's not at all random, it's all kinds of cool.

• 13:27Sean Carroll?

  Is it, it's not symmetric either. Neurons like talk to others, but they don't listen necessarily.

• 13:34Speaker 03

  That is correct.

• 13:35Sean Carroll?

  Okay, some asymmetry there.

• 13:36Speaker 03

  Yeah.

• 13:37Sean Carroll?

  And in the, but, but okay, so is it, is technically the connectome just the wiring diagram? Or is it that extra information about where information flows?

• 13:48Speaker 03

  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. Like, we don't even understand, we don't agree on the correct way of representing the information. So that giant connectivity matrix you talked about, Sean, 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, 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.

• 14:28Sean Carroll?

  Hmm.

• 14:29Speaker 03

  Right? And so the identities of the cells matter.

• 14:31Sean Carroll?

  Yep.

• 14:33Speaker 03

  The other thing that really matters is how those messages are received. Right?

• 14:39Sean Carroll?

  Okay.

• 14:40Speaker 03

  So, so, so in analogy with, it's very like context-dependent language, trying to think 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.

• 14:57Sean Carroll?

  Right.

• 14:57Speaker 03

  Does that make sense?

• 14:58Sean Carroll?

  Yep, 100%.

• 14:59Speaker 03

  So 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.

• 15:11Sean Carroll?

  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?

• 15:18Speaker 03

  That is entirely correct. Right. So messages are received differently. Yeah, so that's why we care. Thank you. Thank you for coming up with analogy. Yeah, so the identities of the cells matter. There's lots of other really interesting, but also very detailed biophysical properties of each cell that clearly do matter, but we don't know by how much. So the thing that I usually try to tell my graduate students when I'm first introducing them to this type of modeling that we do is, like, say that I'm a civil engineer. I'm trying to build a building, right? And I need some materials to hold up the roof.

• 16:03Speaker 03

  And I say, "I need to know the properties of this beam so that I can hold the roof up." Now, the beam is made out of atoms, and I know that there's, you know, there's like down there somewhere there's quantum mechanics, right? But we are not solving Schrodinger's equations in order to design a roof. It's way too much. So like, it's super interesting, and maybe you'd be interested in it on the side, but you don't need it 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. They get really funky.

• 16:36Sean Carroll?

  Good.

• 16:36Speaker 03

  They're 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.

• 16:48Speaker 03

  But do we need it for these very holistic models of the entire animal nervous system? Probably not.

• 16:54Sean Carroll?

  Okay.

• 16:55Speaker 03

  Where do we stop? It's hard to say right now.

• 16:58Sean Carroll?

  Right.

• 16:59Speaker 03

  Like, I know I don't need every single detail, but I do not know which of them are actually crucial.

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• 18:04Sean Carroll?

  And the individual neurons are different, not only sort of structurally or biologically, but even in terms of information processing, right?

• 18:13Speaker 03

  Yes.

• 18:13Sean Carroll?

  Like, they have different, I don't know, I want to say, algorithms for turning input into output. Is that fair?

• 18:20Speaker 03

  I think that's fair. Yeah. So if you think of it computationally, in terms of just maps, right?

• 18:26Sean Carroll?

  Yeah.

• 18:26Speaker 03

  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?

• 18:35Sean Carroll?

  Right.

• 18:36Speaker 03

  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.

• 18:51Sean Carroll?

  But is the specification of how each neuron maps inputs to outputs part of what we call the connectome? Or is that a next step?

• 18:59Speaker 03

  It's not.

• 19:00Sean Carroll?

  It's not, okay.

• 19:02Speaker 03

  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, I'm, you can't, you know, the audience can't see, but I'm raising my hand right now. 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. And whatever it was like, whatever, 15, 20 years ago. And I remember thinking that's like, well, I won't, I won't tell you what I actually thought, but I was skeptical.

• 19:36Sean Carroll?

  Skeptical.

• 19:37Speaker 03

  I was skeptical 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 chromax code for six months straight, making zero mistakes. And 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 to you magically tomorrow, like, what would you do with that? Right?

• 20:06Sean Carroll?

  Right.

• 20:07Speaker 03

  Like, how could you even make sense of this giant spaghetti monster that somebody just handed you?

• 20:13Speaker 03

  And so, so I think, 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, this might actually, I think we might, 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 Martyr, Corey Bargman, is because they knew that there were so many other details that are not observable by the connectome. Like, 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, we never thought we could. Nobody thought that we could, right?

• 20:57Speaker 03

  So the disagreement was whether or not the stuff that you can measure Effectively, these connectivity matrices, is that sufficient?

• 21:04Sean Carroll?

  Is that good enough to do something?

• 21:06Speaker 03

  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? And so there's a giant continuum of opinions. And I was, you know, I was somewhere in the middle, but you know, 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 dataset.

• 21:37Sean Carroll?

  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?

• 21:51Speaker 03

  What do we know?

• 21:52Sean Carroll?

  What animals do we have the connectomes of?

• 21:54Speaker 03

  So the first one we got was actually like 30 years ago. We have a full connectivity matrix of the C. elegans nematode worm. It's not, 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, they're nematode flatworms. They're about a millimeter long and they're, they live in the soil. So if you got, if you scooped up any soil in your, in your garden and looked it under a microscope, you're very likely to be able to see them. They're fucking everywhere.

• 22:29Speaker 03

  And, and so they're millimeter long and they have, 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 a thousand 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 connectome of the C. elegans worm for so long. And yet we still don't understand it. We do not understand it.

• 23:03Sean Carroll?

  Right.

• 23:04Speaker 03

  And there's actual good technical reasons why, why, why C. elegans 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 uterusophila fruit fly.

• 23:37Sean Carroll?

  Fruit fly?

• 23:38Speaker 03

  Fruit fly, yeah. So this is a kind of fruit fly that I, every year, 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, they're more like three millimeters long.

• 24:04Speaker 03

  And so they're like the size of a grain of rice.

• 24:09Speaker 03

  And their entirety of their nervous system is more like the size of a sesame seed.

• 24:15Sean Carroll?

  Okay.

• 24:16Speaker 03

  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? Invertebrates. They 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.

• 24:59Speaker 03

  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's, that's, that whole thing has been, has been mapped out. And that, 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.

• 25:18Sean Carroll?

  Wow. And how many neurons?

• 25:20Speaker 03

  So the brain has 150K. And then the ventral nerve cord has an additional 22K.

• 25:28Sean Carroll?

  Okay, good. So a much bigger matrix than our little C. elegans.

• 25:31Speaker 03

  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. Now, the reason that the C. 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. There's a ton of chemical communication. They're constantly squirting out neurotransmitters and other chemicals at each other.

• 26:08Speaker 03

  There's a lot of mechanical computation, so it's a squishy thing that crawls around in a matrix, not a mathematical matrix, a soil matrix. And 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.

• 26:49Speaker 03

  So the fact that we had the neuroconnectivity 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. All 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's actually going on.

• 27:31Sean Carroll?

  In other words, because individual neurons, etc., might be more specialized or something like that, rather than just like every neuron pitches into every task.

• 27:39Speaker 03

  Right, yeah. So the CLG neurons, some of them are, I mean, they're like, they're so not, 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 a, we have olfactory system, cells that detect smells, right? Like, they have, 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, in the, in the worm, in the worm nervous system. And that's a feature, it's computational feature of how their nervous system works that's in common with ours.

• 28:20Sean Carroll?

  And with the fly connectome, the fly neurons, I saw in, you know, one of your videos, the, these images of these neurons, and 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.

• 28:43Speaker 03

  Right. Do you know the longest cell in your body?

• 28:46Sean Carroll?

  I do not know the longest cell in my body.

• 28:48Speaker 03

  It's about as tall as you are.

• 28:51Sean Carroll?

  That's a little freaky. I don't want to think about that.

• 28:54Speaker 03

  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, in insects, in the ventral nerve cord, in your, in your body, it's in your spinal cord, okay? You have these cells that are responsible for how, this is how you know you, you stubbed your toe. Okay? 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.

• 29:28Sean Carroll?

  Why does it need one cell to do that? Can't like, a bunch of cells hand off the message?

• 29:33Speaker 03

  You can do that. No, this is, this is the actual, this is the, this is the normal architecture.

• 29:39Sean Carroll?

  Okay.

• 29:40Speaker 03

  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.

• 29:52Sean Carroll?

  Stupid brain. I don't think I want to know about it at all. I just want to get on my day.

• 29:57Speaker 03

  You want to know something? Well, this is how you don't, this is how you don't fall over. There's all the shit that your, your body does that like you don't

• 30:01Sean Carroll?

  Fair enough.

• 30:02Speaker 03

  Think about, right? You don't have to think about not falling over. If you're, if you're hiking and you kick a rock, you don't fall over. And you also don't want to waste your precious time thinking about how to not fall over. You simply want it done, right? You want to keep on having that conversation about number theory you're having with your body. Right? You don't have to think about how to not fall over just because you kicked a rock.

• 30:21Sean Carroll?

  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 this, there's this open question that you elucidated very nicely. Is it good enough to help us do anything? And you've been asking, what is the relationship between the connectome and walking in the fruit fly? Is that right?

• 30:46Speaker 03

  Yep. Uh huh. That's right.

• 30:47Sean Carroll?

  So I don't know. How do you even start with that? What do you, what do you do?

• 30:52Speaker 03

  Well, um, so, so the, so the, the slightly longer story is that this is a, this is a, this is a long time collaboration I've had with a, a friend, a friend and collaborator of mine, John Tethel. And John is a, 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, right? Like that's, that's what his lab does. And we've been collaborating for, you know, like a decade now and have co-advised a series of graduate students and post-docs doing, doing, doing some combination of theory and modeling and it's been super fun. And so John's also been really involved in some of these connectomics efforts. So a lot of what I said, that all like the, the stuff that I know that is not wrong is because I learned it from John.

• 31:38Speaker 03

  Stuff that is wrong, I made that up. I take responsibility. And so I remember a couple of years ago, uh, John and I were, were, were taking a walk and we had a brand new PhD student, uh, who was, who was thinking about joining our labs. And we're like, oh, what do we, what do we have them do? Like, we got to think of something, right? And, and, and John and I were talking and he's like, well, we have, like, we, 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? He's like, what if we just like simulated it? And I said, "That's never gonna work. Let me tell you all the ways this is not gonna work." So I told him all the ways it was not gonna work, some of which I summarized earlier. You know, the biophysics, all the parameters we don't know, blah, blah, blah.

• 32:23Speaker 03

  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 gonna... We don't lose anything. We're like, "Let's just, let's give it a good old grad school try."

• 32:36Sean Carroll?

  I do, by the way, think that like, half of the secret to succeeding in graduate school is listening to your advisor tell you that won't work and distinguishing when they're right from when they're wrong.

• 32:47Speaker 03

  Absolutely. So our student, Sarah Puklecy, listened to us and she said, "Okay." She went off and wrote some code.

• 32:58Speaker 03

  So 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, all animal movements are rhythmic. Actually, not just animals, like even bacteria move by spinning their flagellum, right?

• 33:42Speaker 03

  So basically, all biological movements are cyclic in some way. So you can be walking, running, swimming, slithering,

• 33:51Speaker 03

  crawling, like basically all locomotion is rhythmic, right? And so the fact that your nervous system needs some way of generating the instructions for your 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 and spinal cord and spinal cord, was capable of generating these cycles.

• 34:29Speaker 03

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

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• 35:33Sean Carroll?

  And this, so just by the way, like, the idea of some system of mechanical things, cells or anything else,

• 35:41Speaker 03

  Yep.

• 35:42Sean Carroll?

  vibrating in periodic ways, that's one that appears all over the place.

• 35:47Speaker 03

  All over the place.

• 35:47Sean Carroll?

  We understand that, yeah.

• 35:49Speaker 03

  We understand this in general, and if we have time, I'll come back to, like, I love dynamical systems.

• 35:55Sean Carroll?

  We will, okay, good.

• 35:56Speaker 03

  We can nerd out about the dynamical systems of oscillator equations a little bit later, and it has, 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, it's also important for things like breathing.

• 36:23Speaker 03

  All right, because you have like 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. Digestion is cyclic.

• 36:40Speaker 03

  Alright.

• 36:40Sean Carroll?

  Yep.

• 36:41Speaker 03

  So you have to, you have to churn the stuff in your digestive system. So when you, there's a, yeah, so there's a sequence of muscle contractions that gets your, I guess the food go down.

• 36:51Sean Carroll?

  I see, okay.

• 36:52Speaker 03

  Right, in your stomach. And in your stomach, especially the stomachs of, so the most studied CPG circuits, central motor generator circuit is actually in the crab digestive system. There's a couple of these like adorable little neurons that, that are responsible for churning what's in the crab stomach goes

• 37:10Speaker 03

  and it makes that rhythm.

• 37:11Sean Carroll?

  And you know which neurons are in charge?

• 37:14Speaker 03

  Uh, this is the work of Eve Marter. She is known for having studied this for decades and that system is so extraordinarily well understood. We actually, 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.

• 37:40Sean Carroll?

  And the thing we're looking at, so the idea of central pattern generators. These are little sub-circuits within the connectome.

• 37:48Speaker 03

  Yes.

• 37:48Sean Carroll?

  That are responsible for, is it always cyclic rhythm motions or is there a more general definition?

• 37:53Speaker 03

  That's, that's probably the plainest definition of it. And then, so the, the CPG is, I mean, like roboticists love the CPG. So a lot of modern robotics is, is built on these oscillator equations. So they don't even, like, I've talked to roboticists who like actually have no idea about the neurobiology of, of central motor generators. Cause 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 can write, there's 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 circle. Right?

• 38:36Speaker 03

  But we didn't know what actually were the cells and their connections in an actual nervous system that generated these rhythms for any animal that walks.

• 38:47Speaker 03

  So that's where we were a couple of years ago is like, nobody had ever actually found what are the cells, what are their names, how do they work?

• 38:54Sean Carroll?

  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, but we hadn't quite identified.

• 39:11Speaker 03

  We hadn't quite find the actual cells. And so to be fair, people have studied lots and lots of walking systems. People like there are tons of like just like whole bookshelves in the library about spinal circuits of walking. Invertebrates have these 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 cells they were and how they worked.

• 39:42Sean Carroll?

  All right. So what are you going to do?

• 39:44Speaker 03

  So 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 a network of 4,000 cells or so. We're like, it's got to be in here somewhere.

• 40:17Sean Carroll?

  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.

• 40:31Speaker 03

  Precisely. So we simulated, so we simulated, okay, so first thing we did is we, to make it a little more manageable, we focused 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. We 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.

• 40:53Sean Carroll?

  Okay.

• 40:54Speaker 03

  Okay. So then we simulate that and we were able to demonstrate that that those 4,000 neurons were able to generate a cycle. They can generate motor rhythms and actuate the muscles that that would have to move the leg. Again, you can't see me, but I'm moving my my arm forwards and backwards.

• 41:12Sean Carroll?

  Yeah, arm is moving.

• 41:13Speaker 03

  Right? It actuates these muscles right here on your shoulder, like the ones that move your shoulder forwards and the ones that move your shoulder back. Okay? So just by simulation, again, we're 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 predictive matrix.

• 41:30Sean Carroll?

  Shallow learning.

• 41:33Speaker 03

  Regular numerical simulations.

• 41:35Sean Carroll?

  Right, okay.

• 41:35Speaker 03

  The stuff that we've been doing for a long time. You write a lot of code and you run it a million times. Um, 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 let's try to reduce it. Now let's cut away one at a time. We tried, 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?

• 42:04Sean Carroll?

  I think it does. And so this is just one leg, or I guess it's symmetric.

• 42:09Speaker 03

  It's symmetric.

• 42:10Sean Carroll?

  The front two legs are doing the same thing.

• 42:11Speaker 03

  I mean, yeah.

• 42:12Sean Carroll?

  By the way, let's just take an aside to explain the fascinating question, which is the wings.

• 42:18Speaker 03

  Yeah. Yeah, I know. It's wild.

• 42:23Sean Carroll?

  So 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.

• 42:32Speaker 03

  Not so, not so. So, so, so this is something my, my friend and collaborator, Michael Dickinson, is very fond of saying. The insect wings are, are actually novel limbs.

• 42:43Sean Carroll?

  Right.

• 42:44Speaker 03

  I'll explain what that means. So for every other animal that flies, um, like bird wings are modified arms, bat wings are modified arms, right? Other animals that fly have wings that used to be not wings.

• 42:58Sean Carroll?

  Right.

• 42:59Speaker 03

  Not so of insect wings. They're not modified legs.

• 43:05Speaker 03

  There'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 legs and two of them became wings. These are just actual new things.

• 43:17Sean Carroll?

  And this is reflected in the nervous system?

• 43:21Speaker 03

  Say it again, please.

• 43:22Sean Carroll?

  This is reflected in the nervous system.

• 43:24Speaker 03

  It's very much reflected in the nervous system. So just like there's these little parts of your spine that correspond to, 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. And 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.

• 43:49Sean Carroll?

  Cool. Okay, so there's a whole separate future research project, understanding how flies fly. You're trying to understand how they walk.

• 43:57Speaker 03

  Yes, just walking for now.

• 43:59Sean Carroll?

  And how did that go?

• 44:00Speaker 03

  It worked great.

• 44:03Speaker 03

  So the pruning study that I briefly described earlier, where we took a functioning system that was able to generate these CPG-like rhythms, and 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. Like, that would be a really cool result.

• 44:46Speaker 03

  She went off and did it. The answer was three.

• 44:50Sean Carroll?

  Three cells?

• 44:51Speaker 03

  Three cells. That's the minimum you need. And they have names. We know who they are in the fly nervous system.

• 44:59Sean Carroll?

  Tell us their names, that'd be fun.

• 45:01Speaker 03

  That is a great question, because I actually have no idea what their actual names are.

• 45:05Sean Carroll?

  The names are known.

• 45:06Speaker 03

  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.

• 45:18Sean Carroll?

  You're the one who said we know their names, that's the only reason I ask.

• 45:21Speaker 03

  Okay, we, the royal we, like.

• 45:23Sean Carroll?

  The royal we.

• 45:24Speaker 03

  Like John knows what their names are, so I have no idea what their names are. We gave them pet names, though.

• 45:29Sean Carroll?

  Of course.

• 45:30Speaker 03

  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.

• 45:39Sean Carroll?

  Yeah.

• 45:40Speaker 03

  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 they're two excitatory cells. And the last one is I1, because it's an inhibitory cell. And they are connected in a particular, very understandable, architecture motif that explains why this tiny little circuit is capable of generating cycles.

• 46:06Sean Carroll?

  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?

• 46:20Speaker 03

  So, okay, so I will, I will, I will be slightly more precise and say that they are, they are, we believe the three neurons are sufficient to generate the rhythm.

• 46:29Sean Carroll?

  Okay, the rhythm.

• 46:30Speaker 03

  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. And then there's other cells involved to make it actually walk. Does that make sense?

• 47:04Sean Carroll?

  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 responsibilities shared among different subcommittees.

• 47:16Speaker 03

  I certainly don't feel like the nervous system is wasting cells.

• 47:19Sean Carroll?

  Right.

• 47:20Speaker 03

  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 neural manifolds and, I don't know, there's words thrown around if you talk to some other neuroscientists. I 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. If it's there, there's probably a pretty good reason it's there or it wouldn't be there.

• 47:54Sean Carroll?

  Well, it's possible, like, 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.

• 48:04Speaker 03

  Because the cells are so expensive to maintain, neurons are some of the most expensive cells to maintain in your body. I think it, we, 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 timeframe.

• 48:23Sean Carroll?

  So it's actually more plausible to have vestigial organs in the body than vestigial neurons?

• 48:30Speaker 03

  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 of, that's a different cycle we can go on, about why those vestigial organs didn't go away. And 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.

• 48:54Sean Carroll?

  Okay, let's go back to our three neurons, E1, E2, I1. And so there's a like really oversimplified spherical cow version of this where it's literally a circuit and 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. And you know, how do you learn about all those?

• 49:23Speaker 03

  Yeah, 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?

• 49:49Sean Carroll?

  Like a mechanical body or?

• 49:51Speaker 03

  A simulated body, more like a video game body.

• 49:53Sean Carroll?

  Yeah, video game body, okay.

• 49:55Speaker 03

  Yeah, so my son's been playing like Red Dead Redemption. He rides a little horse around in this virtual little environment. It's like clomp, clomp, clomp, clomp, right? That's just the 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.

• 50:19Sean Carroll?

  Right, good. So, sorry, is that going on? Does that exist? Did that help? Did that teach you anything?

• 50:25Speaker 03

  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 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.

• 50:48Speaker 03

  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?

• 50:56Sean Carroll?

  Yeah.

• 50:56Speaker 03

  In 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, 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? But it's, it's just, this is, this is, this is, I'm really excited about it.

• 51:25Sean Carroll?

  Is there any usefulness in imagine doing it in good old fashioned physical reality as well as virtual reality?

• 51:34Speaker 03

  Like robotic?

• 51:35Sean Carroll?

  Yeah, either a robot or can you like hijack the nervous system of an actual fly?

• 51:39Speaker 03

  For sure. Super easy to hijack the nervous system of a fly. As part of the reasons that we're working in the flies is 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.

• 52:01Sean Carroll?

  Uh huh.

• 52:02Speaker 03

  And 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.

• 52:15Sean Carroll?

  Right.

• 52:16Speaker 03

  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.

• 52:57Speaker 01

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• 53:00Speaker 06

  ChatGPT for teens is designed to help teens learn, think critically, and grow their skills. ChatGPT for teens supports active learning by breaking concepts down. Rather than giving teens the answer, study mode helps teens work through problems, and interactive quizzes test their knowledge and provide immediate feedback. Safety protections are on by default, and parental controls offer families additional options, like setting study hours and quiet hours. Visit chatgpt.comPARENTS to learn more.

• 53:27Sean Carroll?

  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.

• 53:36Speaker 03

  Yeah, I 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.

• 53:45Sean Carroll?

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

• 53:50Sean Carroll?

  And, and 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.

• 54:08Speaker 03

  It is a guess at the moment. We have, we, 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. We haven't done it yet. So that's why this is still, I would say, a very strong hypothesis in my mind. It's our, it's our, we have good reason to make this guess, but it's, it's still a guess at this point until we can confirm it biologically.

• 54:36Speaker 03

  But I think like, one of the things that's kind of cool about this result is that as a, as a computational modeling person, I've spent the majority of my career fitting data. Like, somebody has an observation, something they already know. And we're like, oh, sure, I, I can like, write some equations in code and we can, we can recapitulate it. We 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.

• 55:21Speaker 03

  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. And in our model, it was a cell that has a name, right? And doesn't, I do actually know the name of this neuron. 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, if you zap it with a laser, it should make the leg tap. It should go back and forth.

• 55:51Sean Carroll?

  Okay.

• 55:52Speaker 03

  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. So 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.

• 56:17Sean Carroll?

  Yeah.

• 56:18Speaker 03

  And it tapped its link.

• 56:20Sean Carroll?

  Oh, there you go.

• 56:21Speaker 03

  It's adorable. It's like, it was an actual model-driven prediction.

• 56:25Sean Carroll?

  Right.

• 56:26Speaker 03

  Like, we had no idea, nobody had any idea what this neuron did. It was just, it was just in the nervous system. Most, most cells in the nervous system are like that, like, we don't know. We can give it a name, because we kind of know where it came from, we have nomenclature, we have systematics, we don't know what it does.

• 56:40Sean Carroll?

  Well, I guess that was an 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. Is there an obvious roadmap to what we're able to do?

• 57:00Speaker 03

  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. So, 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?

• 57:40Sean Carroll?

  Okay.

• 57:40Speaker 03

  And it's a simulation, doesn't have every single light bulb.

• 57:44Sean Carroll?

  Sure.

• 57:46Speaker 03

  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? Or to, 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

• 58:30Speaker 03

  system and the body so that we know how information goes in and how information comes out. And it would be situated in a virtual reality environment that's capable of interacting with, with things, right? Like surfaces that are, that are not flat, right? You can walk a long piece, you can, 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, you know, 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

• 59:10Speaker 03

  that can predict what's going to happen in parts of these circuits that are hard to predict otherwise. Because the thing is like, the whole thing has just mad, mad feedback and recurrence, right? Like, and it's one thing that I've learned about humans and our ability to, to, to reason through rational thought is humans are really terrible at, at reasoning through what happens with feedback circuits and, and recurrence. Like we can go forward, we can follow a path, like A to B to C to D.

• 59:37Sean Carroll?

  That we can do.

• 59:38Speaker 03

  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.

• 59:47Sean Carroll?

  Okay.

• 59:47Speaker 03

  And that's one of the arguments that I make in, in motivating why we need these complicated computational models. We can't do it, but we have computers.

• 59:59Sean Carroll?

  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?

• 1:00:20Speaker 03

  Yes, so many, probably more than, more than not. I think we need to be really careful. So this is, I mean, this is something that I think maybe, maybe we talked about briefly in person at some point is, is this idea of the, the digital Sphinx paper that we, we, we wrote a couple of weeks ago. And the brief intro to that is that there's a lot, I was, I was starting to see a lot of work and, and conversation in the field, including by, 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, it's literally impossible, we know we have to

• 1:01:05Speaker 03

  make a lot of assumptions, right? And so a lot of people, and 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.

• 1:01:28Sean Carroll?

  Yeah.

• 1:01:29Speaker 03

  Right? That we can do. Anything external with cameras, we can do because we have, we have cameras and we have really good computer vision. And so a lot of people are, are, are basically saying that, okay, this is, 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 we've, surely we've gotten something right.

• 1:01:58Sean Carroll?

  Sure.

• 1:01:59Speaker 03

  I know, I know. I'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? You're not doing what you said you did."

• 1:02:35Sean Carroll?

  Right.

• 1:02:35Speaker 03

  And what they said they did was that they had uploaded a fly brain. That was the headline. They've uploaded a brain. They claim they did it. And of course, you have to read the details. You looked at it and I was like, "Okay, this is not..." They didn't actually do that, but that's what they said they did, right?

• 1:02:54Sean Carroll?

  Yeah, okay.

• 1:02:55Speaker 03

  But this thing got a lot of... It kind of went viral and it got a lot of attention and not just among non-scientists. But actually, I think a lot of... I got a couple of friends who are not neuroscientists, like chemists. I was like, "I heard this thing. They uploaded a fly brain, right? Like, that sounds really cool." Because they didn't know exactly the...

• 1:03:15Sean Carroll?

  Sure.

• 1:03:16Speaker 03

  Yeah, exactly. And so as an exercise, just as an exercise, I'm sitting around with one of my postdocs, Elliot Abe, and I was like, "Elliot, this is nuts. Like, this is bananas, right? Like, this is not the right way of doing it." But to explain why it was not the right way of doing it took a lot of technical words, right? You have to understand reinforcement learning. You have to kind of understand the architecture of the nervous system. Like, there's a lot of stuff to explain and it just takes a long time. And so Elliot and I are sitting around. We're like, "Well, what is the thing that we can do to point out how ridiculous this is? What is the logical extreme of what they're effectively doing?" We're like, "Well, you know, they're not actually even using the fly brain connectome. This could be anything.

• 1:04:00Speaker 03

  It could be a random matrix. In fact, it might as well be a worm matrix. It might as well be a C. elegans worm matrix." And so...

• 1:04:09Sean Carroll?

  Sorry, what did they upload?

• 1:04:11Speaker 03

  They uploaded a portion of... They simulated a portion of the fly brain.

• 1:04:15Sean Carroll?

  Okay.

• 1:04:17Speaker 03

  Yeah. And crucially, since we spent so much time earlier talking about the ventral nerve cord and how that controls leg movements, they did not simulate the ventral nerve cord.

• 1:04:25Sean Carroll?

  Okay.

• 1:04:27Speaker 03

  But you know, even me saying that, right? Like that took that took conversation about like ventral nerve cord wires and how that actually controls your limbs, right? Anyway, that was one of the things that they did not have. They did not have ventral nerve cord, even though their animation definitely had little legs that were like moving around, right? That was the animation part. So Ellie and I were like, okay, well, what if we what if we upload a worm brain and get it and train it to control the fly body? We're like, we could totally do this. And so and so he and another graduate student at lab, they just they did it. They downloaded the C. elegans worm connectome, all 300 cells in its glory. And we popped it into a reinforcement learning algorithm that we've been using for lots

• 1:05:10Speaker 03

  of other things to control a biomechanically realistic fruit fly body walking around in a physics engine to imitate 3D kinematics of flies. Like, and it works, like it all it'll it'll runs around just like, just like we know the flies do.

• 1:05:32Sean Carroll?

  The worm connectome in the fly body wriggles around the right way?

• 1:05:36Speaker 03

  Yeah, yeah. I mean, like, if you use enough deep learning, if you use a little deep learning and you can train it with good enough data, it is perfectly possible to get a worm brain to control a fly body.

• 1:05:48Sean Carroll?

  Okay.

• 1:05:49Speaker 03

  And so what we're learning from all of this is, this is just, I mean, it's silliness, right? Like, if you if you if you basically use that much deep learning in there and you and you're allowing all of these parameters to change in ways that are not obvious, the fact that you have a connectome and the fact that you have a hyper realistic biomechanical body doesn't mean anything. Like, this is not biologically meaningful.

• 1:06:10Sean Carroll?

  Right.

• 1:06:11Speaker 03

  You can get behavior fidelity without any biological fidelity.

• 1:06:15Sean Carroll?

  Well, and especially because you said that we're not even talking about the sort of identities of the individual neurons or, you know, their maps from inputs to outputs. So how in the world can you expect to get something believable, realistic? I don't know what you want to call it.

• 1:06:33Speaker 03

  Yeah, so we actually even we even tried a little bit. So the worm connectum also has motor neurons. It has the neurons that would be controlling their muscles. And we use that population of motor neurons to to to to wire it up to the fly body actuators just for fun.

• 1:06:51Sean Carroll?

  Yeah, okay.

• 1:06:52Speaker 03

  Basically, but that's sort of that's where the deep learning comes in, right? Like, if you have an artificial neural network that maps, that does that mapping between the motor neurons and how it how it produces torque.

• 1:07:04Sean Carroll?

  Right.

• 1:07:05Speaker 03

  In the body, that's where the neural network was, and we trained that.

• 1:07:09Sean Carroll?

  But this is kind of just showing that I can run the same software on, you know, a Mac or a PC.

• 1:07:17Speaker 03

  Um, yeah, if you have the right interfaces, yes, I think that's a great analogy.

• 1:07:22Sean Carroll?

  I can emulate an engine in a different.

• 1:07:24Speaker 03

  It's an emulator, right. It's totally an emulator. It's exactly what it is. It's like if you have the right, if you have, if you're, if you, if you don't care about meaningful interfaces, you can get lots of things to plug together.

• 1:07:35Sean Carroll?

  Right. Okay, good.

• 1:07:37Speaker 03

  And that's like, you know, like HDMI matters, right? Like, if you and and the and the real trick to get something meaningful out of it is to actually engineer those interfaces.

• 1:07:46Sean Carroll?

  Right, yeah. Okay, so that's a that's a good cautionary tale. We should all read the popular science literature with a little bit bit of caution. But since we're at the end of the podcast, let's, you know, think big a little bit about the implications of everything that you're telling us. You know, I'm getting the, one of the messages I'm getting is the the embodied nature of all of these neurons, all the brain and so forth. These days, forgetting about wild claims from startup companies, but there's still a lot of interest in AI and consciousness. And I mean, consciousness, both artificial and and real.

• 1:08:29Sean Carroll?

  We've had a couple of podcasts recently that talked about whether biology was intrinsically important to the idea of consciousness.

• 1:08:40Speaker 03

  Okay.

• 1:08:40Sean Carroll?

  And not because of like anti-physicalism or mystical woo stuff, but because all, maybe, maybe all of the little processes going on underneath the hood of a biological organism, the respiration, the metabolism, and the signals going forth, maybe all of those matter in some way over and above just the algorithm that is being run on the hardware. Are there, do you take any lessons from your work for these kinds of ideas?

• 1:09:09Speaker 03

  Um, yeah, I think so. Um, I maybe try, I'll maybe state what I'm about to say a little more strongly than I actually believe for the, for the sake of conversation.

• 1:09:19Sean Carroll?

  Sure.

• 1:09:21Speaker 03

  We have, we have no examples that we all agree on of, of agents that are intelligent and conscious, except the ones that are embodied. We don't agree, right? Like, the other ones may or may not be intelligent and conscious, but we don't agree yet. So the only ones who actually agree on are, are embodied agents. Furthermore, the nervous system, all nervous systems, evolved starting about 500 million years ago to control a body, to sense from the environment, respond to those senses in order to move around in the world and seek food and mates and et cetera, et cetera. That's what animals do, right?

• 1:10:01Speaker 03

  And everything that we think of as reasoning, as consciousness, I mean, there's all of these words, I'm not going to list them all, right?

• 1:10:11Speaker 03

  All of that machinery, all of the capabilities for doing so, evolved on top of the neural computations required for sensory motor control, for sensing from the environment and moving the body around. And so, in my guess, like, I'm going to guess

• 1:10:28Sean Carroll?

  Yeah.

• 1:10:29Speaker 03

  that understanding the, the platform, the substrate on which all of those other capabilities were built, would be important for understanding the stuff above, as well as a strong constraint in how it could possibly have gotten there.

• 1:10:46Sean Carroll?

  I think that, you know, for, for claiming to say things stronger than you actually believe, that was incredibly, uh, reasonable claim. You put all the caveats in there.

• 1:10:57Speaker 03

  All right, all right. I am a scientist.

• 1:11:02Sean Carroll?

  But, but the, the sort of the, to turn that around a little bit, if we are interested in artificial approaches to thinking, consciousness, whatever, there's a lot we still have to learn from the biological reality of it.

• 1:11:19Speaker 03

  Um, that I actually don't feel that strongly about. Like, I don't think it's actually important to understand the details of how biology implements something to build an artificial system that takes advantage of some of those insights. I mean, I feel like the, the field of bio-inspired engineering is full of these examples, where there's the concept, the biology might do something, was sufficient to inspire a perfectly good solution, without understanding the details. I mean, like, bird flight is the only, is the first one everyone always thinks of, right? Like, we knew that birds can fly, therefore we were inspired to fly. It turns out, imitating the way that birds fly was an utter failure. We had to throw it out all the window and start over, right? Fixed-wing aircraft, that's where it's at, right?

• 1:12:05Speaker 03

  And so, the details of how a bird actually flies is fascinating, and we continue to study it, but understanding it was not necessary for us to fly.

• 1:12:13Sean Carroll?

  Fair enough.

• 1:12:13Speaker 03

  Right? So, it's just that we can do it. It's, I think that's a lot of times where the bio-inspiration comes from. The inspiration is sometimes just more like, like, isn't that cool? Like, the central pattern generator circuits that I talked about earlier, same example, right? The, the, the observation that there must be something inside your spinal cord that generates these rhythms, that's all the roboticists took.

• 1:12:33Sean Carroll?

  Yeah.

• 1:12:34Speaker 03

  They didn't need exactly how it worked, exactly what the cells are and how it works in an actual biological system. They're like, oh, I can, I can implement this, this, this chromatic oscillator on, on my onboard computing chip. Great, perfect, this works great. Like, that's all they needed, you know? So, I'm not sure we need to know the details of biological intelligence to get to artificial intelligence. I don't think that's necessary. However, the concept that it may be necessary for that agent, for that intelligent system, to be actually embedded in something that's interactive, that has a physics, so to speak, it doesn't have to be our physics.

• 1:13:15Speaker 03

  But I think it should have maybe some rules and constraints, some notion of energy, some notion of conservation laws, like not just limitless everything.

• 1:13:25Sean Carroll?

  Right.

• 1:13:26Speaker 03

  Maybe that's important. I don't know. This part I'm speculating, but I don't think it's necessary to understand how biology works to get artificial intelligence.

• 1:13:35Sean Carroll?

  I guess I would completely agree with you there. The difference, the failure of the analogy a little bit is we all know what flying is. We don't really know what consciousness is, and maybe, maybe there's a little bit more to be learned than just inspiration from the biological side of things.

• 1:13:54Speaker 03

  I think that's absolutely true. Yeah, I don't know. I don't think, I think the biology of consciousness is difficult. There's the psychology and the philosophy of consciousness. Getting at the biological basis of consciousness is, is, is pretty difficult, as far as I understand.

• 1:14:11Sean Carroll?

  Of course, famously, it has been labeled the easy problem of consciousness. But to be fair, David Chalmers always said, "The easy problem is hard."

• 1:14:22Speaker 03

  Yeah. Well, there's, I mean, it is, I don't know. I'm, I, in biology, there are no easy problems.

• 1:14:28Sean Carroll?

  There are no easy problems. That's perfectly fair.

• 1:14:30Speaker 03

  There are no easy problems. Every time you come up with an economy, oh, surely it must be A versus B. 30 years later, it's both.

• 1:14:35Sean Carroll?

  It's both or C. Yeah. I guess, okay, then the last question will be, are there any potential therapeutic aspects of this? Are we learning enough about how connectomes work that we can help figure out ways to fix them when they're broken?

• 1:14:51Speaker 03

  I sure hope so. I think one of the most interesting applications that I actually think about a lot in building our embodied models of the, like, except brains not in a jar, right, actually connected to a body, is exactly that interface between the nervous system and the musculoskeletal system, of which there are tons of pathologies and dysfunctions that Are pretty terrible when they happen to you. We can think about, for example, spinal injury. So that affects both your nervous system as well as your neuromuscular control.

• 1:15:23Sean Carroll?

  Yeah.

• 1:15:24Speaker 03

  You can have an injury, like if I have a bum ankle on one side, I start limping. That's a different neural strategy. And then over time, I might adopt a different gait and walk differently. And then a little bit longer, maybe the legs on one side of my body get stronger. So it's adapting at a different timescale as well. So understanding those interactions between how your nervous system controls movement in compensation to injury and attempting to repair it. Or if you have an amputation, you can't repair the amputation, but you can repair the function. Right? So all of those interactions are very important and poorly understood because they're holistic.

• 1:16:05Speaker 03

  Like, our ability to understand those kinds of holistic, longer-term adaptations is very poor because we just haven't had the tools to be able to do so. And so that's one of the things we hope to be able to do in building these embodied models is to understand not just, does it walk? That's kind of, you know, hopefully that works, right? But after it walks, what happens when it breaks? What happens when we break it in different ways? How does it compensate? What is the role of plasticity? What is the role of, like, growing new muscles versus growing new tendons versus growing new neural pathways? Right? And if we understand that, perhaps we'll have some new clues about how to design therapeutics to help that process work better. Right? Like, your PT does a lot of crazy stuff, but not all PTs agree on how to rehabilitate

• 1:16:50Speaker 03

  after some kind of injury. Is there any guidance that we might be able to come up with by understanding the interactions with your nervous system and the musculoskeletal system a little bit better?

• 1:16:58Sean Carroll?

  This all sounds very complicated.

• 1:17:01Speaker 03

  Biology is complicated.

• 1:17:04Sean Carroll?

  That's why it's the science of the 21st century, I guess.

• 1:17:08Speaker 03

  It's squishy and it's fun. And it's, I just feel so, I feel so privileged to be able to do it and to, like, you know, spend my time hanging out with my friends, talking about brains and biomechanics. I like, sometimes I wake up and I like, I can't believe this is a real job.

• 1:17:28Sean Carroll?

  I think that is the perfect place to end because that's an inspiration for everyone. So Bing Brunton, thanks so much for being on the Mindscape Podcast.

• 1:17:35Speaker 03

  Thank you, Sean. This was super fun.

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