352 | Bing Brunton on Connecting the Connectome to the Body

Summary

Mindscape Episode 352 | Bing Brunton on Connecting the Connectome to the Body

Date: April 27, 2026
Host: Sean Carroll
Guest: Bing Brunton, neuroscientist and biologist, University of Washington

---

Overview

In this episode, Sean Carroll speaks with Bing Brunton about the groundbreaking mapping of the fruit fly connectome and how understanding neural wiring diagrams can help us link brain activity to behavior. This conversation explores what a connectome is, why mapping it is a monumental scientific task, how it relates to bodily function and movement, and what this means for the future of neuroscience, robotics, artificial intelligence, and therapy for neural injury and disease. The discussion is both technical and accessible, with vivid metaphors, a focus on scientific skepticism, and excitement about recent computational breakthroughs.

---

Key Discussion Points and Insights

1. Defining and Understanding the Connectome

[05:44–17:40]

• What is a Connectome?

  • The connectome is "essentially a wiring diagram...for all the cells and their connections and identities in the brain.” (Brunton, 05:55)
  • There’s debate on resolution: cell-by-cell (microscale) vs. brain area-by-brain area (mesoscale).
  • "People don't agree on how they use that term." (Brunton, 08:21)
  • Human brain: 85 billion neurons—mapping at the full cellular level not yet possible.

• Beyond Neurons: Role of Glia

  • "Half of the cells in your brain are not neurons...and they're clearly important." (Brunton, 09:34)
  • Glia were once thought to be custodial, but are now seen as vital to brain function.

• Parameters and Complexity

  • There’s vast complexity: physical properties, types of signals, context sensitivity, and dynamic cellular properties.
  • "I know I don't need every single detail, but I do not know which of them are actually crucial." (Brunton, 17:09)

---

2. Insights from Model Organisms: C. Elegans and Drosophila

[23:45–30:29]

• First Connectome Ever:

  • C. elegans (nematode worm, ~300 neurons) mapped 30+ years ago.
  • Yet, understanding behavior from its connectome has proven elusive due to chemical and mechanical communication.

• Breakthrough with Fruit Fly (Drosophila)

  • Recent mapping of male and female fruit fly brain and ventral nerve cord (~150,000 + 22,000 neurons).
  • Fruit fly’s richer, more specialized connectivity matrix is somewhat easier to relate to function.
  • The existence of clear cell types in fruit flies, unlike the “multiplexed” neurons in C. elegans, helps interpretation.

Quote:
"The important thing about the size of it, paradoxically, is that it's actually a little bit easier to understand from the connectivity matrix." (Brunton, 27:39)

---

3. Structural and Functional Heterogeneity of Neurons

[30:29–32:28]

• Neurons come in astounding variety and size; some can be as long as a person is tall (the sensory neurons stretching from the big toe to the brainstem).
• “This is how you know you stubbed your toe...you want to know about it very quickly.” (Brunton, 31:42)
• Biological wiring is designed for speed and efficiency in critical bodily functions.

---

4. From Connectome to Behavior: The Walking Fruit Fly

[32:28–49:45]

• Central Pattern Generators (CPGs)
  • Discussing how networks in the nervous system create cyclic (rhythmic) activity for locomotion, breathing, digestion.
  • Long-studied but identifying concrete cell-based CPGs in walking circuits has been elusive until now.
• Brunton Lab Approach
  • Collaborated with John Tuthill, using the new fruit fly connectome to simulate VNC circuits.
  • "We went after a question that biologists and neuroscientists have been asking for over 100 years: how does the nervous system generate rhythms from non rhythms?" (Brunton, 34:55)
• Key Discovery: Minimal Circuit for Rhythm
  • Start with a 4,000-cell simulation—prune cells to discover the core needed for rhythmic output.
  • "The answer was three. Three cells. That's the minimum you need." (Brunton, 47:31)
  • These consist of two excitatory (E1, E2) and one inhibitory (I1) neuron—a simple, understandable motif producing cyclic behavior.

Memorable Exchange:

• "Three cells." (Brunton, 47:32)
  "That's the minimum you need." (Brunton, 47:31)

---

5. Embodiment and Simulation: Digital Twins

[52:05–63:13]

• Why Simulate the Whole Body?
  • The brain is not in a jar; it exists to control a body.
  • The lab's vision: create a “digital twin”—a physics-based, biologically accurate simulation of brain, body, and behavior.
  • “It’s 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...” (Brunton, 53:29)
• Experimental Validation
  • Use simulations to generate predictions, then validate with manipulations (e.g., "We cut its head off, glued it to a stick, and shined a laser at it. And it tapped its leg." [Brunton, 59:31–59:34])
  • Lasers, genetic engineering, and computer vision are key tools.

---

6. Pitfalls and Cautions: The “Digital Sphinx” Warning

[63:13–71:01]

• Dangers in Connectome Research
  • Risk of behavioral fidelity without biological fidelity—i.e., something can “look” like a real behavior without using the right neural mechanisms.
  • Example: Deep learning can train a C. elegans worm brain to run a fly body in a simulator, but "this is not biologically meaningful." (Brunton, 69:03)
  • Important to match not only outputs (behavioral “looks like”) but also internal structure (“works like”).
• Media Hype and Overselling
  • Startup said they “uploaded a fly brain,” but reality was less than claimed (e.g., didn’t simulate ventral nerve cord).

---

7. Implications: From AI to Medicine

[71:01–80:13]

• Embodiment, Intelligence, and Consciousness

  • All agreed-upon examples of conscious/intelligent agents are embodied.
  • “All of the capabilities for doing so, evolved on top of the neural computations required for sensory motor control...” (Brunton, 72:36)
  • But achieving artificial intelligence may not require biological detail—example of flight inspiration vs. actual bird mechanics.

• Therapeutic Potential

  • Understanding neural body control may lead to better therapies for spinal injuries, limb loss, etc., by revealing principles of adaptation, compensation, and plasticity.
  • “Our ability to understand those points of holistic longer term adaptations is very poor because we just haven’t had the tools... That’s one of the things we hope to do building these embodied models.” (Brunton, 80:05)

---

Notable Quotes and Memorable Moments

• On Terminology and Confusion:
  “People use that word [connectome] in a different way. And I'm sure, you know, the terminology actually does matter here."
  (Brunton, 05:55)

• On Skepticism About Connectome Value:
  "I was skeptical on a couple of different fronts...I was skeptical that if we could have it...what would you do with that? How could you even make sense of this giant spaghetti monster?"
  (Brunton, 21:07)

• On the Key Discovery:
  “The answer was three. Three cells. That's the minimum you need. And they have names. We know who they are in the fly nervous system.”
  (Brunton, 47:31)

• On Modeling and Experiment:
  “We made a prediction of things we didn’t know before...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...And it tapped its leg.”
  (Brunton, 59:31–59:34)

• On Hype vs. Reality:
  “You can get behavioral fidelity without any biological fidelity.”
  (Brunton, 69:29)

• On the Importance of Embodiment:
  “It always controlled the body and it always controlled a specific body with these limbs and these muscles and these joints and these sensors...It’s so obvious to me that we need to be understanding the brain...in the context of the body that it interacts with.”
  (Brunton, 53:29)

• On Biology’s Complexity:
  “In biology, there are no easy problems.”
  (Brunton, 77:42)

• On Motivation and Joy:
  “Sometimes I wake up and I can’t believe this is a real job.”
  (Brunton, 80:23)

---

Important Segments & Timestamps

• [05:44]–[17:40]: Introduction to connectome concepts and biological complexity
• [23:45]–[30:29]: C. elegans and fruit fly connectomes – what we can and can’t learn
• [32:28]–[49:45]: Discovery of the minimal CPG for fruit fly walking
• [52:05]–[59:56]: Embodied brain simulation and the “digital twin” concept
• [63:13]–[71:01]: Cautions about overselling models and behavioral vs. biological fidelity
• [71:54]–[80:13]: Broader implications for consciousness, AI, and therapy

---

Tone and Style

The conversation is lively, skeptical yet optimistic, blending technical explanations with vivid, sometimes humorous metaphors (e.g., describing neurons as a “giant spaghetti monster”; physically gluing fly heads to sticks). There is an emphasis on both the excitement and difficulty of piecing together how living nervous systems translate into behavior.

---

Useful for Listeners

If you missed the episode, here’s what you’ll take away:

• The connectome is not just a wiring diagram, but an evolving, context-dependent model whose details matter.
• New connectome mappings (like the fruit fly’s) allow direct computational exploration of how brains make bodies move.
• Huge caution is required not to mistake simulation “looks” for accurate representation of “how it works.”
• This research offers both conceptual inspiration for AI/robotics and potential real-world application to medicine and rehabilitation.
• Most importantly: biology is endlessly complex, but progress is accelerating, and sometimes, just sometimes, it all clicks—with only three neurons.

---

Transcript

[T-Mobile Announcer]

Everyone deserves to be connected. T Mobile and US Cellular are joining forces. Our networks are coming together bringing more T Mobile coverage all over the country. Switch to T Mobile and save up to 20% versus Verizon by getting built in benefits they leave out. Check the math@t mobile.com Switch and now T Mobile is available in a US cellular store near you Bigger network. The combination of T Mobile and US Cellular's network footprints will enhance the T Mobile network's coverage savings versus comparable Verizon plans plus the costs of options, benefits, plan features and taxes and fees vary. Savings with three plus lines include third free line free via monthly bill credits Credit stop if you cancel any lines Qualifying credit required. Warning the following ZipRecruiter radio spot you are about to hear is going to

[ZipRecruiter Announcer]

be filled with F words when you're

[ZipRecruiter Narrator]

hiring, we at ZipRecruiter know you can feel frustrated, forlorn even, like your efforts are futile. And you can spend a fortune trying to find fabulous people, only to get flooded with candidates who are just fine. Fortunately, ZipRecruiter figured out how to fix all that, and right now you can try ZipRecruiter for free at ZipRecruiter.com Zip with ZipRecruiter you can forget your frustrations because we find the right people for your roles fast, which is our absolute favorite F word. In fact, four out of five employers who post on ZipRecruiter get a quality candidate within the first day.

[T-Mobile Announcer]

Fantastic.

[ZipRecruiter Narrator]

So whether you need to hire four, 40 or 400 people, get ready to meet first rate talent. Just go to ZipRecruiter.com Zip to try ZipRecruiter for free. Don't forget that ZipRecruiter.com Zip finally, that's ZipRecruiter.com Zip hello everyone.

[Sean Carroll]

Welcome to the Mindscape Podcast.

[ZipRecruiter Announcer]

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 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? 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 sufficient for consciousness or anything like that. We'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. 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 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. 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. That's a much more subtle system, a lot more intricate things going on, little subsystems, doing different. 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.

[Sean Carroll]

Bing Brunden, welcome to the Mindscape podcast.

[Bing Brunton]

Thanks, Sean. Glad to be here.

[Sean 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.

[Bing Brunton]

I'm rather attached to mine.

[Sean Carroll]

Yes, exactly.

[ZipRecruiter Announcer]

So.

[Sean Carroll]

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.

[Bing Brunton]

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's. It has cells. And the cells work by electrical activity and they talk to each other through electricity. 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. 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 building a really big complicated building, you're building a skyscraper or something, you would have a wiring diagram, 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. 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 identities in the brain. Now, the difficulty comes in in terms of how do you actually define the units? Do you want a connectome that's necessarily at the scale of individual cells and how they're connected to other individual cells. That's one way people have used that term. But they're more what we call meso scale connectomes that exist as well as 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. 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 how the brain areas connect to each other. So people use that term to mean, like a area by area connectome as well.

[Sean Carroll]

So there's some coarse graining involved.

[Bing Brunton]

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

[Sean Carroll]

Okay.

[Bing Brunton]

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. 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, 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, but it's not a genome. Right. So we don't have that in connectomes. We don't break the three on the scale of description 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.

[Sean Carroll]

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

[Bing Brunton]

We do some.

[Sean Carroll]

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.

[Bing Brunton]

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. 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 kind of 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 have their own dynamics, but we don't understand. I think that's 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.

[Sean Carroll]

Let 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? Is everything in our body cells, there's got to be just some liquids and solids and things in there.

[Bing Brunton]

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

[Sean 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 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.

[Bing Brunton]

Yep, yep.

[Sean Carroll]

All.

[Bing Brunton]

All the stuff, right. Like, for example, your. Your bones, right. Your skeleton, you probably know that it's made out of lots of inorganic compounds. Like there's a lot of calcium in there, 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 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. 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, 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 their cells all inside it, and you just zoom in. It's got very intricate structure.

[Sean 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.

[Bing Brunton]

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 and repair ourselves or sometimes not.

[Sean Carroll]

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

[Bing Brunton]

Okay, we're, I mean, it's. We're going to try. It's going to be fine.

[Sean 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 we are, again, we, 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?

[Bing Brunton]

It is, yeah.

[Sean Carroll]

And so the, the connectome is the, the fine grain connectome, if you want to call it that, the cells we

[Bing Brunton]

can call the cellular level one or the neuro.

[Sean Carroll]

Cellular level connectome.

[Bing Brunton]

Neuronal.

[Sean Carroll]

That would be just a, A big old matrix listing every single neuron and how it connects to every other neuron.

[Bing Brunton]

Yep, exactly. Right? Yeah.

[Sean Carroll]

Okay, good.

[Bing Brunton]

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, is it?

[Sean Carroll]

It's not symmetric either. Neurons, not at all talk to others, but they don't listen necessarily.

[Bing Brunton]

That is correct.

[Sean Carroll]

Okay. Some asymmetry there, but. Okay, so is technically the connectome just the wiring diagram or is it that extra information about where information flows?

[Bing Brunton]

So there's a lot of extra information in it. And so this is the analogy I tried making earlier about the genome as well. 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, 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. I mean, you probably heard about things like dopamine, serotonin. Right. Like there's dopamine cells, there's serotonin cells, and if they both fire an action potential, they both say something. Those messages are completely different. Right. And so the identities of the cells matter.

[Sean Carroll]

Yep.

[Bing Brunton]

The other thing that really matters is how those messages are received. Right. So in analogy with kind of 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.

[Sean Carroll]

Right.

[Bing Brunton]

Does that make sense?

[Sean Carroll]

Yep, 100%.

[Bing Brunton]

So when cell A speaks to cell B and 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.

[Sean 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.

[Bing Brunton]

That is entirely correct. Right. So messages are received differently. Yeah. So that's why we care. Thank you. Heck, thank you for coming up with the analogy. Yeah. So the identity to 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 say that I' trying to. I'm a civil engineer. I'm trying to build a building. Right. And I need some materials to hold up the roof. And I say I need to know the properties of this beam so that I can hold a roof up. Now, the beam is made out of atoms, and I know that there's, like, down there somewhere. There's quantum mechanics.

[Sean Carroll]

Right.

[Bing Brunton]

But we are not solving Schrodinger's equations in order to design a roof. It's way too much. So it's super interesting. And maybe you'll be interested on the side, but you don't need it for the task of building a roof. That's where we are right now. I know. I don't need every single detail that is known about these biophysical parameters of these cells. They get really funky.

[Sean Carroll]

Good.

[Bing Brunton]

They're crazy nonlinear, and they're super special. And they're almost impossible to measure. People will spend an entire PhD measuring one cell and characterizing a lot of detail. But do we need it for these very holistic models of the entire animal nervous system? Probably not. Okay, 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.

[ZipRecruiter Announcer]

Summertime is here, and it's time to finally break out the shorts and swim trunks. But just because your wardrobe becomes more casual doesn't mean you shouldn't put thought into it. That will become clear the first time you try. Chubby's Trunks. Chubby's make swim trunks, shorts and summer shirts that are designed with intention to be both functional and look great. You might wonder, what does it mean for swim trunks to be functional?

[Sean Carroll]

How hard can it be?

[ZipRecruiter Announcer]

Chubby's will convince you that it matters. They're engineered with four way stretch fabric that lets you transition effortlessly from swimming laps to lounging poolside. They're seriously soft, dangerously flexible and incredibly quick drying and you will look good. The compliments that you start receiving will be proof of that. There's no reason to dread beach season nor to take it for granted. Chubbies will make you start looking forward to 5pm Friday and the weekend to come. They're proof that when you get shorts right the first time, they never really go out of style. And now for a limited time, Chubby's is giving our listeners 20% off with code [e-mail] that's 20% off your order with Code Mindscape. Give your thighs the VIP treatment they deserve with Chubbies. Support our show and tell them we

[Sean Carroll]

sent you

[ZipRecruiter Announcer]

Whenever you get dressed in the morning, it's nice to not have to think too hard, but also nice to look good. Rag and Bone is here to make that possible. Clothes that feel good right out of the box and look both stylish and effortless with a wide range of combinations. The the new infused line of denim at Rag N Bone is a great example. Rag N Bone uses a proprietary eight step over dye process that gives each piece a rich depth that gets better with every wash. It looks like something special and you'll feel like that when you have them on. I have both a Rag and Bone infused jacket in gray denim and jeans in blue denim. They go with everything and they fit perfectly without fuss. You pick the size and style you're looking for, from slim and straight to athletic and relaxed. It's not just denim, of course. Rag and Bone has soft T shirts and comfy knits that pull your look together. It's premium wardrobe staples made to last and made to live in. So it's time to upgrade your denim with Rag and Bone. For a limited time, our listeners get 20% off their entire order with code [e-mail] that's 20% [e-mail] with promo code Mindscape. When they ask where you heard about them, please support our show and let them know we sent you.

[Sean Carroll]

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. Is that fair?

[Bing Brunton]

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 of 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 from these simulations is that you don't need every single detail of how that map is implemented to approximate its function.

[Sean 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?

[Bing Brunton]

It's not. I think 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, 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 really large efforts to produce more connectome data sets and whatever it was, whatever 15, 20 years ago. And I remember thinking that's. Well, I won't tell you what I actually thought, but I was skeptical. I was skeptical on a couple of different fronts. I was skeptical that it was even going to work at all. Can we actually reconstruct one of these things at sufficient scale because it involves, I don't know, running a transmission like Carmex code for six months straight, making zero mistakes. 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, if somebody just handed to you magically tomorrow, what would you do with that? How could you even make sense of this giant spaghetti monster that somebody just handed you? And 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 martyr Cory Bargman, is because they knew that there were so many other details that are not observable by the connectome. 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?

[Sean Carroll]

Is that good enough to do something?

[Bing Brunton]

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, 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.

[Sean 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,

[Bing Brunton]

what do we know?

[Sean Carroll]

What animals do we have the connectomes of?

[Bing Brunton]

So the first one we got was actually like 30 years ago. We have a full a connectivity matrix of the C. Elegans 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 microscope, you're very likely to be able to see, to see them. They're fucking everywhere. 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 1,000 cells and about 300 neurons. And so the connectivity matrix of those 300ish 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 connect of the C. Elegans worm for so long and yet we still understand it. We do not understand it.

[Sean Carroll]

Right.

[Bing Brunton]

And there's actual good technical reasons 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, 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.

[Sean Carroll]

Fruit fly.

[Bing Brunton]

Fruit fly, yeah. So this is a 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. That's where they live. So these little guys are. They're more like 3 millimeters long, and so they're like the size of a grain of rice. And their entirety of their nervous system is more like the size of a sesame seed.

[Sean Carroll]

Okay, okay.

[Bing Brunton]

And so they're small enough that it has been possible to reconstruct the entirety of their brain and nervous system. We have a brain in our heads, and we also have a spinal cord that constitutes our central nervous system. The brain and spinal cord of humans and mammals, vertebrates, 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, 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, and 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.

[Sean Carroll]

Wow. And how many neurons?

[Bing Brunton]

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

[Sean Carroll]

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

[Bing Brunton]

A much bigger matrix. 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. 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, they have those reflex loops that are mechanically coupled with their body, which is squishy. And so the physics of that is pretty complicated. 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. So 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 they're actual cell types. Not every single cell is just its own little snowflake. 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, helping us understand what the heck is actually going on.

[Sean Carroll]

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?

[Bing Brunton]

Yeah. So the CL against 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 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 and with the

[Sean Carroll]

fly connectome, the fly neurons. I saw in one of your videos 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.

[Bing Brunton]

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

[Sean Carroll]

I do not know the longest cell in my body.

[Bing Brunton]

It's about as tall as you are.

[Sean Carroll]

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

[Bing Brunton]

It is a little freaky. So you have these cells that actually. The same cells we were talking about in the ventral. So in Insects in the ventral nerve cord in your body. It's in your spinal cord. You have these cells that are responsible for how. This is how you know you stubbed your toe. Okay? So there's a cell that detects when you've stepped 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 lung and spindly.

[Sean Carroll]

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

[Bing Brunton]

You can do it. Yeah, no, this is the actual, this is the normal architecture, okay? There are other cells involved and you can hand off. The mess 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.

[Sean Carroll]

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

[Bing Brunton]

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

[Sean Carroll]

kicked a rock, which segues very nicely into the actual work you've been doing with the fruit fly connect dome. So you have the connect dome. That's good. And then 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?

[Bing Brunton]

That's right.

[Sean Carroll]

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

[Bing Brunton]

Well, so. So the. So the slightly longer story is that this is, this is a, this is a long time collaboration I've had with a friend, 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. Right. That's what his lab does. And we've been collaborating for a decade now and have co advised a series of graduate students and postdocs 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, the stuff that I know that is not wrong, is because I learned it from John, stuff 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? We got to think of something, right? And John and I were talking and he's like, well, we have, we almost have eventual nerve cord connectome. It's like it's almost ready because they were in the process of cleaning it up, curating it, trying to write it up, right? He's like, what if we just simulated it? And I said that's never going to work. Let me tell you all the ways this is not going to work. So I told him all the ways it was not going to work, some of which I summarized earlier. The biophysics, all the parameters, we don't know, blah blah, blah. There's tons of stuff, there's lots and lots of reasons that it wouldn't work. But by the end of this talk, we had come to, well, you know what, let's try it anyway. It's not going to. We don't lose anything. Let's give it a good old grad school try.

[Sean Carroll]

I do, by the way, think that 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.

[Bing Brunton]

Absolutely. So our student Sarah Puglisi listened to us and she said, okay, you went off and wrote some code. Of course, long story short, it took a couple of years, but we kept at it partially because some of the, the preliminary stuff is actually kind of interesting. There were some hints. 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? To 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. So basically all biological movements are cyclic in some way. So you can be walking, running, swimming, slithering, crawling. Basically all locomotion is rhythmic. The fact that your nervous system needs some way of generating the instructions for your muscles to move in a circle, that's fundamental. 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. 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. We're lost. It feels like we're going round in circles. I'm gonna ask that man for directions. Hi there. We're trying to get to the state fairgrounds. Well, you're going to take a left

[T-Mobile Announcer]

at the old oak tree at this here road.

[Bing Brunton]

Nah, I'm just kidding.

[T-Mobile Announcer]

Let me get my phone out.

[Bing Brunton]

How is their signal out out here?

[T-Mobile Announcer]

T Mobile and US Cellular are coming together. So the network out here is huge. We get the same great signal as the city, saving a boatload with benefits. And there's a five year price guarantee too. Okay, here's the turn actually, can you

[Bing Brunton]

pull up the way to a T mobile store?

[T-Mobile Announcer]

America's best network just got bigger. Switch to T mobile today and get built in benefits the other guys leave out plus our five year price guarantee. And now T mobile is available at U S Cellular stores in Hermiston. Best mobile network based on analysis by Ooklo Speed test intelligence data. Second half of 2025 bigger network. The combination of T Mobile's and US Cellular's network footprints will enhance the T mobile network's coverage price guarantee on talk, text and data exclusions like taxes and fees apply. See t mobile.com for details.

[OrderlyMeds Announcer]

When people turn to telehealth for weight loss, they're looking for real support. That's why more people are choosing orderlymeds.com orderly meds connects you with real doctors and access to proven GLP1 medications like semaglutide and tirzeptotide. No guessing, just a more supportive experience. And all ship directly to your door in discreet packaging. Do your research, ask questions, then visit orderlymeds.com podcast for an exclusive offer. That's orderlymeds.com podcast. Individual results may vary. Not medical advice, eligibility required. C site for details.

[Sean Carroll]

And this. So just by the way, like the idea of some system of mechanical things. Cells or anything else?

[Bing Brunton]

Yep.

[Sean Carroll]

Vibrating in periodic Ways. That's one that appears all over the place. All over the place. We understand that.

[Bing Brunton]

Yeah, we understand this in general. And if we have time, I'll come back to. I love dynamical systems. We can nerd out about the dynamical systems of oscillator equations a little bit later. And actually has connections to our work in the connectome as well. But. Yes, absolutely. Yeah. In the intervening 100 years or so, lots of people have studied the idea of these circuits. The ability of your nervous system to generate rhythms is not only important for locomotion, it's also important for things like breathing because you have the. Inhale, exhale, inhale, exhale. You can control it, but if you don't think about it, it just happens. That's generated by what we call a central pattern generator, a CPG circuit as well. Digestion is cyclic, all right.

[Sean Carroll]

Yep.

[Bing Brunton]

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. Gets the food go down. Right. And in your stomach. Especially the stomach. So the most studied CPG circuit, centerpatogenary circuit is actually in the crab digestive system. There's a couple of these adorable little neurons that are responsible for churning. What's in the crab stomach goes. And it makes that rhythm and you

[Sean Carroll]

know which neurons are in charge.

[Bing Brunton]

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 understood. It's probably. Sometimes people are a little snarky and we say, like the. 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

[Sean Carroll]

and 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?

[Bing Brunton]

That's probably the plainest definition of it. And then. So the CPG is. I mean, roboticists love the cpg. So a lot of modern robotics is built on these oscillator equations. So they don't even. I've talked to roboticists who actually have no idea about the neurobiology of central pattern generators, because for them, they don't care. They just write an equation. We've done the same. So a lot of these 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. 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 a circle. Right. 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. Okay, 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?

[Sean 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 any cell.

[Bing Brunton]

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's tons of just like whole book in the library about spinal circuits of walking. Invertebrates have these ventral nerve cords. How do they generate their wing flapping? How do they walk? People have tried, and there's tons of information, but we didn't know precisely which cells they were and how they worked.

[Sean Carroll]

All right, so what are you going to do?

[Bing Brunton]

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 of the ventral nerve cord of a fruit fly. Right. And we figured whatever it is, it's gotta 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. We got it down to a network of 4,000 cells or so. We're like, it's gotta be in here somewhere.

[Sean 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 eliminate. Like you say, if I didn't have this one, it could still walk Fine.

[Bing Brunton]

Precisely. First thing we did is to make it a little more manageable. We focus on only two front legs. So in the sex of 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 4000ish.

[Sean Carroll]

Okay.

[Bing Brunton]

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. 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 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 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 let'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?

[Sean Carroll]

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.

[Bing Brunton]

Yeah.

[Sean Carroll]

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

[Bing Brunton]

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

[Sean 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 like. But flies are very different.

[Bing Brunton]

Not so. Not so. So. So this is something my. My friend and collaborator, Michael Dickinson is very fond of saying. The insect wings are actually novel limbs. I'll explain what that means for every other animal that flies. Bird wings are modified arms. Bat wings are modified arms. Other animals that fly have wings that used to be not wings. Not so of insect wings. They're not modified legs. 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 pairs of eight legs and two of them became wings. These are Just actual new things.

[Sean Carroll]

And this is reflected in the nervous system.

[Bing Brunton]

Say it again, please.

[Sean Carroll]

This is reflected in the nervous system.

[Bing Brunton]

It's very much reflected in nervous system. So just like there's these little parts of the. 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.

[Sean Carroll]

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

[Bing Brunton]

Yes, just walking for now.

[Sean Carroll]

And how did that go?

[Bing Brunton]

It worked great. 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. 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. If you get it down to a few dozen cells left over, if that's the minimum circuit you need a few dozen cells to do this, I would be ecstatic. That would be a really cool result. She went off and did it. The answer was three.

[Sean Carroll]

Three cells.

[Bing Brunton]

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

[Sean Carroll]

Tell us their names. That'd be fun.

[Bing Brunton]

That is a great question, because I actually have no idea what they're actually.

[Sean Carroll]

Look it up. Okay. Names are known.

[Bing Brunton]

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 handle this. The names are like a series of letters and numbers, and I can't remember what they are.

[Sean Carroll]

You're the one who said we know their names. That's the only reason I asked.

[Bing Brunton]

Okay, we know Royal. We. John knows what their names. So I have no idea what their names are. We gave them pet names, though.

[Sean Carroll]

Of course.

[Bing Brunton]

Of course we had to give them pet names. And they're not too cute. 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. 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.

[Sean 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?

[Bing Brunton]

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.

[Sean Carroll]

Okay, the rhythm.

[Bing Brunton]

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 than 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.

[Sean Carroll]

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.

[Bing Brunton]

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

[Sean Carroll]

Right.

[Bing Brunton]

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, some, 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 where it wouldn't be there.

[Sean 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

[Bing Brunton]

because the cells are so expensive to maintain. Neurons are some of the most expensive cells to maintain in your body. I think my hypothesis would be that if a cell is actually not necessary, the body would find a way for it not to be there over a longer time frame.

[Sean Carroll]

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

[Bing Brunton]

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 just didn't go away. And there's usually a good reason 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.

[Sean Carroll]

Okay, let's. Let's go back to our three neurons. E1, E2, I1.

[Bing Brunton]

Right.

[Sean Carroll]

And so there's a, like, really oversimplified spherical cow version of this where it's literally a circuit.

[Bing Brunton]

It is.

[Sean Carroll]

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 how do you learn about all those?

[Bing Brunton]

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 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 a simulated body. More like a video game body.

[Sean Carroll]

Yeah. Video game body. Okay.

[Bing Brunton]

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 clomp, clomp, clomp, clomp, clomp. Right. 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, so far as we can. But it would be a physics engine. Right. Models. F equals ma.

[Sean Carroll]

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

[Bing Brunton]

It's in progress. I think it's in progress. I'm really excited about it. 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.

[Sean Carroll]

Right.

[Bing Brunton]

It always controlled the body and it always controlled a specific body with these limbs and these muscles and these joints and these sensors. Right. 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.

[Sean Carroll]

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?

[Bing Brunton]

For sure. Super easy to hijack the nervous system of fly. It's part of the reasons that we're working in the flies because it was the kind of the genetic organism of choice for a really 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 added all of that stuff already exists. And 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, we just know so much more about their everything than a spider, for example. 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're lost. It feels like we're going round in circles. I'm gonna ask that man for directions. Hi there. We're trying to get to the state fairgrounds. Well, you're going to take a left

[T-Mobile Announcer]

at the old oak tree at this here road.

[Bing Brunton]

Nah, I'm just kidding.

[T-Mobile Announcer]

Let me get my phone out.

[Bing Brunton]

How is there signal out here?

[T-Mobile Announcer]

T Mobile and US Cellular are coming together so the network out here is huge. We get the same great signals as the city. Saving a boatload with benefits. And there's a five year price guarantee too. Okay, here's the turn.

[Bing Brunton]

Actually, can you pull up the way to a T Mobile store?

[T-Mobile Announcer]

America's best network just got bigger. Switch to T Mobile today and get built in benefits the other guys leave out plus our five year price guarantee. And now T mobile is available at US Cellular stores in Hermiston Best mobile network Based on analysis by OK speed test intelligence data to second half of 2025 bigger network. The combination of T mobile's and US cellular's network footprints will enhance the T Mobile network's coverage price guarantee on talk text and data exclusions like taxes and fees apply. See t mobile.com for details.

[OrderlyMeds Announcer]

When people turn to telehealth for weight loss, they're looking for real support. That's why more people are choosing orderlymeds.com orderly meds connect you with real doctors and access to proven GLP1 medications like semaglutide and tirzeptatide. No guessing, just a more supportive experience. And all ship directly to your door in discreet packaging. Do your research, ask questions, then visit orderlymeds.com podcast for an exclusive offer. That's orderlymeds.com podcast. Individual results may vary. Not medical advice, eligibility required. C site for details.

[Sean 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.

[Bing Brunton]

Yeah, 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.

[Sean Carroll]

Yeah. Well, physicists are going to join you there. That's okay. And it sounds like 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.

[Bing Brunton]

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. We haven't done it yet. So that's why this is still, I would say, a very strong hypothesis in my mind. 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. But I think 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, and we're like, oh, sure, I can write some equations in code and we can recapitulate it. We can 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. We, 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 and doesn't. 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, if you zap it with a laser, it should make the leg tap. It should go back and forth, forth. 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 his head off, and we glued it to a stick and we shined a laser at it.

[T-Mobile Announcer]

It.

[Sean Carroll]

Yeah.

[Bing Brunton]

And it tapped its leg.

[Sean Carroll]

Oh, there you go.

[Bing Brunton]

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, it was just in the nervous system that most, most cells in the nervous system are like that. Like, we give it a name because we kind of know where it came from. We have, we have nomenclature, we have systematics. We don't know what it does.

[Sean 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.

[Bing Brunton]

I sure hope so. Part of it is this idea of the embodied brain that I talked about. It goes by a couple of different names, drawing analogies between what we're doing with these, 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?

[Sean Carroll]

Okay.

[Bing Brunton]

And it's a simulation, doesn't have every single light bulb, but it has many of the important parts of the city of Singapore, 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, in real time shift if they have to shift traffic patterns or whatever, to relieve some 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. And it would be situated in a virtual reality environment that's capable of interacting with things like surfaces that are not flat, you can walk. Physics. Yeah, just physics. 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. Because the thing is, the whole thing has just mad, mad feedback and recurrence. And if it's one thing that I've learned about humans and our ability to reason through rational thought, it's humans are really terrible at reasoning through what happens with feedback circuits and recurrence. 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.

[Sean Carroll]

Okay.

[Bing Brunton]

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.

[Sean 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 the right answer for the wrong reason?

[Bing Brunton]

Yes, so many. Probably more than not. I think we need to be really careful. So 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. It's literally impossible. We know we have to make a lot of assumptions. A lot of people, again, including us, we've been doing the same thing. One thing that we can measure with a lot of fidelity and relatively easily is just the behavior output of the animal 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 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. A lot of people are basically saying that, okay, this is the grounding. If we can get a model that 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. Sure, 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 trying to fundraise, putting out work that I looked at it and 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. And what they said they did was that they had uploaded A flybrain. That was the headline brain. They've. They've uploaded a brain. They claim they did it. And of course, you have to read them. 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?

[Sean Carroll]

Yeah. Okay.

[Bing Brunton]

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. It actually, I think a lot of. I got a couple of friends who are not neuroscientists, like chemists, where I was like, oh, well, I think I heard this thing. They uploaded a fly brain. Right? Like, that sounds really cool. Because they didn't know exactly the. 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. This is bananas. 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. You have to understand reinforcement learning. You have to kind of understand the architecture of the nervous system. It's a lot of stuff to explain, and it just takes a long time. And so Ellie and I are sitting around, we're like, well, what is the thing that we can do to point out how ridiculous is what is the logical extreme of what they're effectively doing? Or like, well, they're not actually even using the fly brain connectome. This could be anything. It could be a random matrix. In fact, it might as well be a worm matrix. It might as well be a CL against worm matrix. And.

[Sean Carroll]

Sorry, what did they upload?

[Bing Brunton]

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

[Sean Carroll]

Okay.

[Bing Brunton]

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.

[Sean Carroll]

Okay.

[Bing Brunton]

Even me saying that that took conversation about the ventral nerve corridors and how that actually controls your limbs. Right. Anyway, that was one of the things that they did not have. They did not have a ventral nerve record, even though their animation definitely had little legs that were moving around. That was the animation part. Ellie and I were like, okay, well, 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 he and another graduate student lab, they just. They did it. They downloaded the CL against worm connectome, all 300 cells in its glory. And we popped it into a reinforcement learning Algorithm that we've been using for lots 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'll, it'll, it'll runs around just like, just like we know the flies do.

[Sean Carroll]

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

[Bing Brunton]

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.

[Sean Carroll]

Okay.

[Bing Brunton]

And so what we're learning from all of this is. This is just. I mean, it's silliness. Right. If you basically use that much deep learning in there and you are 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 hyperrealistic biomechanical body doesn't mean anything. This is not biologically meaningful. You can get behavioral fidelity without any biological fidelity.

[Sean 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.

[Bing Brunton]

Yeah. So we actually even, we even tried a little bit. So the worm connectome 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.

[Sean Carroll]

Yeah. Okay.

[Bing Brunton]

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

[Sean Carroll]

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

[Bing Brunton]

Yeah. If you have the right interfaces. Yes. I think that's a great analogy.

[Sean Carroll]

I can emulate an engine in a different computer.

[Bing Brunton]

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.

[Sean Carroll]

Right. Okay, good.

[Bing Brunton]

HDMI matters. And the real trick to get something meaningful out of it is to actually engineer Those interfaces, right?

[Sean Carroll]

Yeah. Okay, so that's a good cautionary tale. We should all read the popular science literature with a little bit of caution. But since we're near 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 real. We've had a couple of podcasts recently that talked about whether biology was intrinsically important to the idea of consciousness.

[Bing Brunton]

Okay.

[Sean 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?

[Bing Brunton]

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

[Sean Carroll]

Sure.

[Bing Brunton]

We have no examples that we all agree on of agents that are intelligent and conscious, except the ones that are embodied. We don't agree. Right. The other ones may or may not be intelligent and conscious, but we don't agree. So the only ones who actually agree on 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 and 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? 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? All of that machinery, all of the capabilities for doing so, evolved on top of 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, that understanding 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.

[Sean Carroll]

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

[Bing Brunton]

All Right. All right. I am a scientist,

[Sean Carroll]

but 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

[Bing Brunton]

that I actually don't feel that strongly about. I don't think it's actually important to understand the details of how biology implement something to build an artificial system that takes advantage of some of those insights. I mean, I feel like the field of bioinspired engineering is full of these examples where the concept that biology might do something was sufficient to inspire a perfectly good solution without understanding the details. I mean, bird flight is the only first one everyone always thinks of. Right. 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. 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.

[Sean Carroll]

Fair enough.

[Bing Brunton]

Right. So it's just that we can do it. I think that's a lot of times where the bio inspiration comes from. The inspiration is sometimes just more like, isn't that cool? The central pattern generator circuits that I talked about earlier, same example, right. The observation that there must be something inside your spinal cord that generates rhythms. That's all the roboticists took. 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 implement this chromato oscillator on my onboard computing ship. Great, perfect. This works great. That's all they needed. 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, 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.

[Sean Carroll]

Right.

[Bing Brunton]

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.

[Sean 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.

[OrderlyMeds Announcer]

Things.

[Bing Brunton]

I think that's absolutely true. Yeah. I don't know. 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 pretty difficult, as far as I understand.

[Sean 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.

[Bing Brunton]

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

[Sean Carroll]

There are no easy problems.

[Bing Brunton]

There are no easy problems. Every time you come up with that economy of surely it must be A versus B. Thirty years later it's both.

[Sean 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?

[Bing Brunton]

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 I said, brain's 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 neurological control. Right. You can have an injury to 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. All of those interactions are very important and poorly understood because they're holistic. Our ability to understand those points 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 lock, that's kind of, hopefully that works. Right. But after it locks, 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 growing new muscles versus growing new tendons versus growing new neural pathways? 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 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?

[Sean Carroll]

This all sounds very complicated.

[Bing Brunton]

Biology is complicated.

[Sean Carroll]

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

[Bing Brunton]

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

[Sean 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.

[Bing Brunton]

Thank you Sean. This was super fun.

[T-Mobile Announcer]

Everyone deserves to be connected. T Mobile and US Cellular are joining forces. Our networks are coming together bringing more T Mobile coverage all all over the country. Switch to T Mobile and save up to 20 versus Verizon by getting built in benefits they leave out. Check the math@t mobile.com Switch and now T Mobile is available in a US cellular store near you bigger network the combination of T Mobiles and US Cellular's network footprints will enhance the T mobile network's coverage savings versus comparable Verizon plans plus the costs of options, benefits, plan features and taxes and fees vary. Savings with three plus lines include third free line free via monthly bill credits credit stop if you cancel any lines. Qualifying credit required.