Listeners:

Top listeners:

00:00 00:00

Go to album

Paul Verschure on consciousness and distributed adaptive control CSN Podcasts
Edvard Moser on grid cells and entorhinal cortex CSN Podcasts
Giacomo Rizzolatti on mirror neurons and action understanding CSN Podcasts
Robert Axelrod on game theory and prisoner's dilemma CSN Podcasts
Adrian Owen on disorders of consciousness and vegetative state CSN Podcasts
Jonathan Whitlock on markerless motion capture and posterior parietal cortex CSN Podcasts
Luis Puelles on neuroanatomy and prosomeric model CSN Podcasts
Zoltan Molnar on subplate neurons and cortical development CSN Podcasts

Hillel Chiel on biomechanics and neural control

Episode 3 14.03.2012

PLAY EPISODE

Season 2012

0 Followers 22 Episodes

Description

Why does understanding the body matter as much as understanding the brain , and how do soft, squishy biomechanics simplify the control problems that nervous systems must solve? Hillel Chiel extracts general principles from tongues, worms, and sea slugs. Subscribe for more from the Convergent Science Network podcast series. Hillel Chiel’s research program rests on a foundational claim: evolution selects not for brains or bodies in isolation, but for the coupled dynamical system of brain, body, and environment. This means that understanding neural control without understanding biomechanics is like studying software without knowing the hardware it runs on. His work systematically demonstrates how mechanical properties of soft tissues constrain and simplify the control problems that nervous systems face , often dramatically. The tongue provides a vivid entry point. Modeled as a muscular hydrostat (a “hot dog in a bun” of longitudinal and circumferential muscles), the tongue’s geometry creates a massive mechanical advantage for the longitudinal muscle when the tongue is extended. A simple pulse of neural activation produces rapid shortening that the circumferential muscle cannot resist until the tongue is already retracted. The control implication is striking: for a single lapping motion, the nervous system can effectively ignore one of the two muscle groups. This simplification is invisible without biomechanical analysis and would never be predicted from neural recordings alone. Chiel then scales up to peristaltic locomotion, challenging the standard view that it is slow and energetically wasteful. His mathematical analysis of continuous (rather than segmental) peristaltic waves shows that, properly configured, the center of mass can maintain constant velocity without depending on external friction , meaning the energy costs come from internal tissue properties rather than ground contact losses. A one-meter robot built on this principle moves fast enough that you have to walk briskly to keep up. The Aplysia feeding system illustrates the principle that what a muscle does depends on its mechanical context. As the geometry of the feeding apparatus changes during a bite, swallow, or rejection, the same muscle can switch from protraction to retraction. This means that multifunctional behavior arises not from dedicated muscles for each action but from changing coalitions of muscles recruited according to the current biomechanical state. Chiel frames this as a general principle: the nervous system exploits context-dependent mechanics to achieve behavioral flexibility with minimal rewiring, ganging degrees of freedom together for simple movements and fractionating them when precision is needed.

Tagged as:
biomechanics Control Problems Muscle neural control Simplify Control Understanding Without

About the author

CSN Podcasts

Both the triumphs of humanity and its most evil deeds have resulted from collaboration. In a time where humanity is required to aspire to the former and minimize the latter, the question arises of how collaboration arises and why it fails. Surprisingly, this phenomenon, so central to who we are, is not well understood. Hence, a collaborative effort is required to understand collaboration in its full biological, psychological, sociological, cultural, and economic complexity and to translate this understanding into operational impact. This series of podcasts is one step toward achieving these complementary goals. The Collaboration Podcast presents interviews with people who are central orchestrators of collaboration in various domains including business, government, science, art, health, sustainability, and the military. The discussions were conducted by Prof. Dr. Paul F.M.J. Verschure and members of the Program Advisory Committee of the Ernst Strungmann Forum on Collaboration (https://www.esforum.de/forums/ESF32_Collaboration.html) during 2021 and had the goal to sketch a map of opportunities, challenges, and obstacles in human collaboration. The forum took place in May 2022, and now we would like to share this series of interviews with a broader audience. The full report of the Forum will be published in 2023 by MIT Press. The podcast was produced by the Convergent Science Network (https://www.convergentsciencenetwork.org/). Context: The stability of social systems depends critically on realizing sustainable methods of “collaboration,” yet how and by which means collaboration is achieved is not clearly understood; neither are the conditions or processes that lead to its breakdown or failure. Collaboration can be understood as cooperation between agents toward mutually constructed goals. Part of the reason for our lack of understanding is that the phenomenon of collaboration is, by nature, a highly multidisciplinary problem, and effective research into its complexities has been difficult to achieve across the broad range of scientific and technical disciplines involved. The need for a fundamental understanding of collaboration, however, has become increasingly important. Not only does humankind demand answers as it attempts to address critical challenges at multiple scales (e.g., climate change, migration, enhanced automation, social and economic inequality), but ever-increasing technological and economic means of interconnecting people and societies are disrupting long-established, familiar patterns of how we interact. Radical technological changes that are ongoing have the potential to reshape collaboration in ways that are currently hard to predict or influence (e.g., by altering configurations in interaction, information creation, and modes of communication). On one hand, such changes could disrupt hitherto stable forms of collaboration by affecting critical communication channels and traditional roles, as can be observed in the rapidly changing patterns in governance, commerce, and social interaction. Conversely, technology could lead to the emergence of novel, successful forms of collaboration that deviate from traditional “hierarchical” architectures. Evidence of this can be seen in areas as diverse as highly automated manufacturing plants, the open science movement, collaborative software repositories, user-centered services, and the sharing of economy-based modes of organization. Without a fundamental understanding of the mechanisms, processes, and boundary conditions of collaboration, it is not possible to evaluate or predict which of these possible scenarios are sustainable or even plausible. The Forum “How Collaboration Arises and Why it Fails” (May 8–13, 2022, Location: Frankfurt am Main, Germany) Chairs: Andreas Roepstorff and Paul Verschure Program Advisory Committee: Jenna Bednar, Julia R. Lupp, Bhavani R. Rao , Andreas Roepstorff, Ferdinand von Siemens, and Paul Verschure

Timestamp

00:00:03 - This is the Convergent Science Network podcast. Leading researchers in the domain
00:00:10 - of neuroscience, brain theory and technology are interviewed by Paul Verschoor and Tony Prescott.
00:00:19 - Okay, so I'm Paul Verschure with our Barcelona Brain Cognition Technology Summer
00:00:26 - School here with Hillel Kiel from Case Western University, a reserve university, sorry.
00:00:32 - Well, I pronounce it Hillel Kiel and I'm at Case Western Reserve University.
00:00:36 - Almost right. It's an amalgam between Case Institute and Western Reserve University
00:00:40 - and everybody gets confused by the name, so no problem.
00:00:43 - I'm not the only one. No, you're not the only one. Welcome to the Conversion
00:00:48 - Science Network podcast.
00:00:50 - And so you were speaking in our summer school.
00:00:55 - And for us, it was a great opportunity because you have been active in this
00:00:58 - whole domain of trying to understand biology at sort of a system level.
00:01:04 - And to also bring this together with a very much technology-oriented view and
00:01:09 - using robots as a way to test theory and so on.
00:01:12 - That's exactly where we want to be. and you are really one of the leading characters
00:01:17 - in this field but now so what I saw was really interesting,
00:01:25 - very, very impressive in your talk and very useful is you really try to bring
00:01:29 - us to this list of principles.
00:01:30 - So where you say, look, I look at these systems and here I'm really going to
00:01:33 - give you principles. Of course, we can argue then about these principles.
00:01:35 - And we did. That's probably what we will do again.
00:01:38 - But I think this is really where we want to go, right? So we want to extract these principles.
00:01:41 - And you started fairly simple with, well, of course, simple can still be complicated,
00:01:46 - but rather low scale, let's say, the small scale neuromuscular systems.
00:01:52 - That's right. So maybe we can start a discussion there that you can try to describe
00:01:57 - a little bit what are these key principles are that you found at that level
00:02:02 - of system organization.
00:02:03 - So let me step back and say something about the general principles thing.
00:02:06 - I didn't show this slide, but this is one that actually it's in one of the reviews
00:02:09 - I wrote in 1997 with my colleague Randy Beer called The Brain Has a Body.
00:02:15 - And that's a particularly influential reference. That's probably one of my few
00:02:19 - citation classics. If you go to Google Scholar, it's got, I don't know, over 400 citations.
00:02:23 - For a neurobiologist, that's pretty impressive.
00:02:27 - Unless, of course, you invented a new technique that everybody's using.
00:02:30 - And in that, what we presented was the idea that what evolution selects for
00:02:37 - are not bare brains or particular bodies.
00:02:40 - It's a coupled system of the brain, the body, and the environment.
00:02:44 - So brains are embedded and they are embodied within a particular body with a certain biomechanics.
00:02:50 - And then the agent as a whole, brain-body dynamic, has to work in an environment
00:02:55 - and has to function in that environment.
00:02:58 - So what I was essentially doing, sort of you keyed in correctly,
00:03:02 - we're looking for general principles.
00:03:03 - But in many cases, what's informing our search is that larger framework,
00:03:09 - this idea of the couple dynamical systems framework.
00:03:12 - And one way to look at it is to say, okay, many neurobiologists are,
00:03:17 - you'll excuse the expression, they're neurocentrists.
00:03:21 - For them, if it's not the nervous system, they're not interested. it.
00:03:24 - And they think that the most important thing might be discovering a new channel
00:03:27 - or a particular molecule and stuff like that.
00:03:29 - And again, I have a lot of respect for the reductionist approach.
00:03:33 - From the point of view of this larger framework, what evolution selects for is the entire package.
00:03:38 - And so a particular channel, well, if it's dysregulated, affects behavior,
00:03:43 - and then the animal dies, yes.
00:03:45 - But otherwise, it may just do, it may be one voice in the crowd.
00:03:49 - Biomechanics is something that many neurobiologists don't focus on,
00:03:52 - because what they find is if they can throw the body away and just get the brain
00:03:56 - in the dish, they can do all their experiments very quickly.
00:03:59 - So the question was, what is the role of the biomechanics?
00:04:03 - And in a sense, that was one of the themes that ran through several of the other
00:04:07 - issues in the talk, though I started with that and focused simply on an unusual
00:04:12 - biomechanical periphery.
00:04:14 - And that sort of helps sharpen people's focus.
00:04:18 - We're used to, if you're a roboticist, you're certainly used to thinking about
00:04:21 - actuators, you're thinking about joint torques, you're thinking about kinematics,
00:04:26 - you know about the problem of how ill-posed it is if you have excess degrees of freedom.
00:04:31 - But what happens if you're confronted with a completely different body plan?
00:04:35 - It's all soft, and it has potentially, you know, it can wrap itself in knots.
00:04:40 - So one of the things that makes that, I think, a nice place to start is it disorients
00:04:45 - people enough to get them thinking about why might this be, you know,
00:04:49 - how would you do this? How would you control this?
00:04:51 - I know Frank Grass is going to be talking about cephalopod control and the kinds
00:04:55 - of things that octopi can do.
00:04:56 - But even our tongues, or especially our tongues, are incredibly flexible devices.
00:05:01 - Our conversations being, we're using, I use the term muscular hydrostats,
00:05:06 - that was originated by Kieran Smith.
00:05:09 - And this is, this challenges your whole idea of how control works.
00:05:14 - And the interesting thing was, we initially thought, well, this is going to
00:05:17 - be incredibly complicated.
00:05:18 - What we discovered is you actually
00:05:20 - analyze the mechanics, there are some simplifications that emerge.
00:05:24 - And then that seemed to me a useful principle to enunciate. That, and again.
00:05:29 - I take your points very well, that that principle is a typical biology principle.
00:05:35 - It's not the same as a standard, say, physics principle.
00:05:38 - In a given context, when you talk about a specific behavior,
00:05:42 - if you define what the biomechanics does in that context and in that behavior,
00:05:47 - then that will simplify enormously the control questions.
00:05:50 - Right. So can you give some examples of this kind of simplification that you
00:05:54 - get through through to biomechanics.
00:05:56 - So one of the things I talked about in the particular model we did of a tongue.
00:06:01 - So you have this longitudinal muscle that runs down the center of the tongue,
00:06:04 - and you have a circumferential muscle that wraps around it.
00:06:07 - You could think of it like, if our listeners are trying to form an image,
00:06:10 - it might be helpful to think of a hot dog in a bun.
00:06:13 - Okay? So you have the central muscle, the longitudinal muscle is the hot dog,
00:06:17 - and the bun is the circumferential muscle.
00:06:19 - So if the central muscle contracts, what happens is the whole thing gets short
00:06:24 - and fat because the volume can't change.
00:06:27 - Now, what's fascinating is this. Again, I'm not going to go through this in
00:06:30 - any detail, but I showed a little bit of the math in my talk.
00:06:33 - When the tongue is long, it turns out it has a huge mechanical advantage relative
00:06:38 - to the circumferential muscle.
00:06:39 - What that means is for neural control, if I put just a pulse of activation in
00:06:44 - to the longitudinal muscle, it's going to shorten immediately.
00:06:47 - And the circumferential muscle, even if it's working very, very hard,
00:06:51 - will not be able to overcome those forces until the whole tongue has gotten pretty short.
00:06:56 - So it almost drops out of the control picture. In fact, if you didn't activate
00:07:01 - at all, there are passive forces that would stop it.
00:07:04 - So the real key issue is you might want to activate it because you want to extend
00:07:08 - the tongue again and get it going again.
00:07:10 - But if you didn't, if you just wanted a single lap, You could ignore the circumferential muscle.
00:07:14 - So this is something that would not be obvious to somebody unless they'd sat
00:07:18 - down and looked at the mechanics.
00:07:20 - Once you look at the mechanics, it becomes very obvious. And then you can look
00:07:23 - at the neural control that is actually used, that has evolved for it,
00:07:26 - and say, oh, that's why these features are there.
00:07:28 - But you could also argue that maybe this is, let's say, just a happy coincidence
00:07:33 - because this tongue has to fit in your mouth, for instance. Okay.
00:07:37 - So if this is really where building robots becomes the crucial thing,
00:07:42 - because it's from an evolutionary standpoint, there may be any number of possible
00:07:47 - historical accidents that led you to have the tongue in the particular shape it is.
00:07:51 - By building the robotic device, you can actually say, well, at least is it physically
00:07:56 - realistic to say that this phenomena happens in this way?
00:07:59 - And when you do that and you see that it does, as I emphasized in my talk,
00:08:03 - and I have to always emphasize this to anyone who's listening,
00:08:06 - it doesn't prove that it works that way in biology.
00:08:08 - You're raising the key question. There may be other contingencies, historical accidents.
00:08:13 - It provides a physical argument that at least it's possible.
00:08:17 - And then there are ways of testing that. Now, we can't run evolution over again,
00:08:22 - unless you deal with like bacteria, which have very, very short generation times.
00:08:26 - They don't have tongues, so it'd be hard to do it with them.
00:08:29 - But you can try to create devices like robots, and then you can,
00:08:34 - either in simulation or in the actual device, you can play with the properties.
00:08:40 - So you can change where the key point of trade-off will be, change the relative
00:08:46 - mechanical advantage, depending on how you set up the materials and the actuation.
00:08:50 - And then you can see whether the control ideas continue to be relevant.
00:08:54 - And again, when we've done those kinds of things, usually in simulation,
00:08:57 - but also sometimes in robots, that has paid off very nicely. Right.
00:09:01 - But now, what was interesting with the tongue control is you showed us that
00:09:06 - when you try to, let's say, generalize the trivial interpretation of the control
00:09:12 - of the two muscles, you would run into difficulties.
00:09:15 - So one of the things the animal has to do, of course, is if it's lapping up
00:09:19 - like what it does insides of eggs, most animals have to worry.
00:09:24 - First of all, if they're hungry, they want to eat quickly. But there's also
00:09:27 - an evolutionary issue, which is during the time that they're feeding,
00:09:30 - they may be very good prey items and become someone else's lunch.
00:09:34 - So often you want to eat on the run and then literally get out of there.
00:09:38 - Or another animal may come and grab it away.
00:09:42 - So they need speed. And the problem is, what we found was if we simply tried
00:09:46 - to do a scale version, a faster scaled version of the inputs that had worked
00:09:51 - for the tongue lapping that we started with, they did not work properly.
00:09:55 - They were filtered by both the mechanics and by the low-pass filtering of the muscle.
00:10:00 - And so we had to jigger them in order to get the same frequency behavior at a faster time scale.
00:10:06 - What's interesting is when we then went and looked at some other data where
00:10:10 - people have looked not just at lapping, but other behaviors in,
00:10:14 - for example, locomotion, you will find if you look at the EMGs,
00:10:18 - they are simply not scaled versions of one another.
00:10:20 - You can't get away with doing that. Part of that, probably a large part of that,
00:10:24 - is the low-pass filtering. But the other part is the mechanics.
00:10:27 - And so it was very nice to realize that, oh, this is not just a problem for
00:10:32 - getting this model to work. This might be a general principle for where mechanics
00:10:37 - and neural control have to be kept in mind together.
00:10:40 - Right, exactly. And that also led you to one of your, if you want,
00:10:44 - principles, maybe more behavioral one in terms of timing is everything.
00:10:49 - Timing is everything, absolutely.
00:10:50 - And so there was a very interesting paper, I was actually asked to comment on
00:10:56 - it, that came out in Science about, I think, six months to nine months ago, about cat lappings.
00:11:02 - I don't know if you saw it, but it was actually on the cover of Science.
00:11:05 - And I guess because of the paper I published, they were interested in my comments
00:11:09 - on it. It was a very nice paper.
00:11:10 - And it turns out most people think about lapping as you immerse the tongue into
00:11:15 - the fluid, and then you essentially use it as a cup and withdraw.
00:11:19 - And that's probably how tupinambis does it. It coats its tongue with the fluid
00:11:23 - and then withdraws it and then scrapes it off.
00:11:26 - Cats don't do that. It turns out they do the very fast lapping,
00:11:29 - but the fluid doesn't go on top of their tongue. It's on the bottom.
00:11:33 - What they do is they rapidly create a column of fluid, which they then withdraw in.
00:11:40 - And so what happened was these were guys at MIT. It was fascinating.
00:11:44 - The guy in mechanical engineering, I guess, had his pet cat,
00:11:46 - noticed something interesting, took movies.
00:11:49 - They created this very interesting robot that created this fluid column.
00:11:53 - And they then did analysis on how fast you had to move and what the areas would
00:11:58 - have to be so that you could maintain the fluid column.
00:12:01 - And then they went and looked at the speed of lapping in a whole variety of
00:12:05 - different basically cat animals, animals from the cat species,
00:12:10 - lions and tigers and cats and things like that.
00:12:13 - And they found that if you looked at the scales, there were some very nice scaling
00:12:17 - laws that fit with their model of how you actually did lapping. Very, very beautiful.
00:12:23 - Again, I very much like the paper because here, once again, people think about
00:12:27 - the principles in terms of the mechanics and the physics.
00:12:30 - And then they realize that that's going to create constraints on what the nervous
00:12:33 - system has to do to solve the problem that way.
00:12:36 - Right. So then after that, you sort of now start to move to,
00:12:41 - if you want, a complexification of tongues, which is like peristaltic movements
00:12:46 - across multiple segments.
00:12:49 - This could be sort of multiple tongues glued together in some funky way.
00:12:54 - And from there, you moved on to the aplysia. Right. Right.
00:13:00 - So what are the key insights that you gained looking at these peristaltic movements?
00:13:06 - That you see in different kinds of animal species. So what you see is the thing
00:13:10 - that we had initially focused on, and the literature emphasizes that peristaltic
00:13:14 - involves segmental movement.
00:13:16 - And as I mentioned, Arne McNeil Alexander has this lovely book on animal locomotion.
00:13:21 - That's the name, and I highly recommend it.
00:13:24 - I'm not related to him in any way, but I highly recommend it.
00:13:27 - It's a great book. It's very accessible.
00:13:29 - And he has a whole beautiful chapter on some of the issues in the mechanics
00:13:34 - of locomotion, and he talks about peristaltic locomotion.
00:13:37 - And one of the things he emphasizes is that you have to get the mass moving
00:13:40 - and then you have to slow it down and decelerate it.
00:13:43 - And this is really the classic way that people think about it.
00:13:46 - And if you have segmental locomotion, that's how it works.
00:13:50 - But if you actually spend time staring at earthworms, so after the rain in Cleveland,
00:13:57 - and we get lots and lots of rain,
00:13:58 - the earthworms, which will otherwise drown because they need the oxygen,
00:14:05 - will come out and walk across the sidewalks.
00:14:08 - So you get an opportunity, if you care, to look at worms on a fairly regular basis.
00:14:13 - Now, I think most people either squish them or ignore them, but if you saw me,
00:14:17 - I would be bending down and actually staring at the worm for an extended period of time.
00:14:21 - And what you see is there are different regions in the body.
00:14:24 - It's not a simple movement, actually, but the animal does have this wave of
00:14:28 - contraction that goes from one end to the other. So on the one hand,
00:14:31 - there are clearly segments.
00:14:32 - You can see them, but you see this wave that's moving, and the wave is very smooth.
00:14:37 - It's not confined to one segment at
00:14:40 - a time so this led us to think about what would
00:14:42 - happen if we did this analysis at the
00:14:45 - differential level what if you took segments and
00:14:48 - took them down to the level of very very tiny elements and that analysis which
00:14:53 - again i hope will be coming out within the next few months ordinarily when people
00:14:57 - talk about peristaltic motion they think of it as rather energetically inefficient
00:15:01 - and very slow and this analysis showed that actually that's neither of those are necessarily true.
00:15:09 - If you set it up properly, you can actually keep the center of mass at a constant velocity.
00:15:15 - And of course, there are going to be frictional forces within the body.
00:15:18 - You don't have to depend on external friction to keep moving,
00:15:22 - which means that the energies are not due to losses due to frictional contact.
00:15:28 - And that means that you could probably sustain these movements if you could
00:15:31 - minimize internal friction for quite a long period of time.
00:15:35 - That was very interesting. And the robot I showed that is built on that principle
00:15:39 - moves quite, quite fast, as you saw in the video.
00:15:42 - Those earlier videos, at least one of the underwater one, we had to speed up
00:15:46 - because it was quite slow and it was boring to watch.
00:15:48 - Whereas this one, you had to actually walk fast to keep up with it.
00:15:51 - But this system, what we want to know, of course, what are then these biomechanical
00:15:57 - properties that actually make this an effective locomotion device?
00:16:03 - That's right. So what are the
00:16:05 - key biomechanics that make this task actually doable if you are a worm?
00:16:11 - So this would require, and this is not an area I have pursued,
00:16:14 - but I'll say a few things from what I've read.
00:16:16 - And if someone was going to be inspired by this to follow up,
00:16:19 - what I'd recommend doing is spending some time studying the details of the cross
00:16:25 - bridges and the actual mechanical arrangements within the musculature of the body wall of the worm.
00:16:30 - Because what people have shown in a variety of different settings,
00:16:33 - so I have a colleague, Kishin Nishikawa, who works on ballistic movements in frog tongues.
00:16:40 - They can actually, there are three different classes. In one class,
00:16:43 - the animal essentially throws the tongue out. That's a ballistic movement.
00:16:46 - And there's another class, for example, where they use a muscular hydrostatic
00:16:49 - property and they slowly protrude it.
00:16:52 - So here you have a tongue. You have species that are similar,
00:16:56 - but one is feeding on termites that are relatively slow and that are in crevices,
00:17:00 - and they use a muscular hydrostatic property, and one is going after insects
00:17:04 - that can fly away fast, and they use a ballistic inertial tongue.
00:17:07 - She's looked very carefully at the cross bridges and come up with some very
00:17:11 - interesting models for how there may be very important nonlinear ways in which
00:17:16 - you can extend muscles much further and also contract them much further,
00:17:20 - depending on how the cross bridges work.
00:17:22 - So I would spend time looking at the material properties and understanding that.
00:17:26 - And then if that was, as I would expect, was true,
00:17:30 - I would then talk to people who are doing biomimetics and see if I could build
00:17:33 - devices like that, perhaps initially macroscopic, but ultimately with advances
00:17:38 - in nanomaterials, it would be very exciting to try to get someone interested in that.
00:17:42 - And that would be the way I think you could, again, not prove that the biology
00:17:46 - works that way, but strongly suggest that that's the key physical property.
00:17:50 - But for instance, in the system you showed, which is this larger device that
00:17:56 - uses parasitic movements to move, I don't know exactly the dimension,
00:17:59 - it looked about like a meter long or something like that. So pretty, pretty big.
00:18:04 - Also there, you must have identified biomechanical contributions to its ability
00:18:08 - to actually generate these waves.
00:18:10 - The student who did this work, Alex Boxerbaum,
00:18:14 - spent some time mainly focused on the questions of how would you get,
00:18:18 - what material could he put together that would give him the kinds of changes
00:18:25 - in strain that were crucial for the movements that he was looking at.
00:18:31 - And he came up with the idea of essentially and
00:18:34 - again partially inspired by what you see in the physical arrangements in muscles
00:18:38 - in animals but this idea of actually weaving the um the cable in a helical fashion
00:18:44 - one in one direction and then another in another direction and then basically
00:18:49 - pinning the cables together so that they formed a whole series of rhomboids.
00:18:54 - And now by using these cables within them again this is his idea you could shorten
00:18:59 - or lengthen different regions of the cable and because it was now pinned,
00:19:03 - instead of just shortening, it would pull together and force the others apart,
00:19:08 - causing that expansion in that particular region. Very ingenious on his part.
00:19:12 - Right. Very ingenious. But the step to understanding how this relates to the
00:19:17 - neural control of such a biomechanical system. Yeah, so that's what we're working on right now.
00:19:22 - And actually one of the things we're doing, I talked a little bit about these
00:19:25 - stable hetero channels, which we'll come to next.
00:19:28 - And we actually just got funding from the National Science Foundation,
00:19:31 - myself and my colleague, Roger Quinn,
00:19:32 - to look at this, actually trying to put together more of the degrees of freedom
00:19:39 - of this device with this stable heteroclinic channel network idea to see if
00:19:44 - we can independently control different degrees of freedom,
00:19:48 - actuate and independently control them. I don't know if it's going to work.
00:19:52 - I have a history of saying, I think this is true, and then it does actually show itself true.
00:19:57 - And I have a history of encountering lots of skepticism when I make these claims.
00:20:01 - But I'm also, more often than not, I'm also wrong.
00:20:05 - But we can actuate individual parts of the mesh.
00:20:10 - And we can thus separately control and activate them. And I think we can get
00:20:15 - the thing to bend and lift up and actually form curves and possibly also negotiate
00:20:20 - fairly tortuous terrain.
00:20:21 - It may take us several years, but I think that's all within our grasp.
00:20:25 - Okay. But then do you see the controller that's behind that or the neural control
00:20:31 - as still reducing this complexity or roughly mapping one-to-one on the complexity
00:20:38 - of this morphology? Yeah, yeah, yeah. So I think the issue is this.
00:20:41 - If I'm trying to create peristaltic waves, unified waves, I actually,
00:20:47 - and again, in the robot we showed, that's a one degree of freedom device in terms of control.
00:20:52 - Because the cam basically allows you to pull on the different cables and change
00:20:57 - their length as it circles, and that's all you need to get the peristaltic waves.
00:21:02 - So what that suggests is that under certain circumstances, if you're creating
00:21:06 - waves, a collective contraction of specific elements in sequence will be all
00:21:12 - you need, and the whole thing will do what you want.
00:21:14 - Now, if you want precise, careful movements where you're exploring your way
00:21:19 - through obstacles and then you're curling around and perhaps pulling something,
00:21:24 - you're not going to be able to create this nice unified, let's actuate everything
00:21:29 - in a simple way. You're going to have to do much more complex things.
00:21:32 - I think the way I think about it is this, that when you don't need all the excess
00:21:36 - degrees of freedom, what much of the neural control does is to try to simplify
00:21:40 - down those degrees of freedom by appropriate collective activation.
00:21:44 - And when you do need them, they're there. And again, I think the nervous system
00:21:48 - may use some of the principles I was talking about to fractionate them out and
00:21:52 - pull out the things that it needs.
00:21:54 - So the answer, once again, is it will depend on the context in which the thing is being used.
00:21:59 - And what we're You're going to move towards a situation where,
00:22:02 - and again, this is very nice in terms of the controllers I was talking about,
00:22:05 - you can gang them together so that they do one thing all at once,
00:22:09 - but you can fractionate them if necessary so they can map onto much finer detail. Right.
00:22:16 - So it's something you were structuring also in this presentation from,
00:22:21 - let's say, fairly simple sensor motor systems or cellular motor systems to more complex ones.
00:22:27 - And so after the peristaltic movement, and then this nice proof of concept,
00:22:32 - if you want, which is a one meter long worm,
00:22:35 - you then brought it to the point that you say, look, but the use of these muscles,
00:22:41 - we should think about in terms of context. And this is an important principle.
00:22:44 - And to illustrate that principle, you used also a plesia grasping. Yes. Right?
00:22:49 - So what do you really mean when you say, okay, muscles are used in context?
00:22:54 - Okay. So the example I gave in the talk, let me talk about it a little bit.
00:22:59 - So it turns out that in our hip, there are certain muscles that act for either,
00:23:05 - I think it's moving forward or moving back, depending on where the rest of the hip is.
00:23:10 - In the arm, the brachioradialis is used for, if your palm is up,
00:23:18 - this crosses the joint and it's crucial for turning your palm down.
00:23:22 - Now, so if you think about the direction of forces, it's going to rotate in one direction.
00:23:28 - Now, once you have the arm down, the hand down, that same muscle plays a role
00:23:33 - in rotating the arm back in the opposite direction.
00:23:36 - So if you look in terms of the torque arm, it actually switches direction,
00:23:39 - and it can do so in part because of the new position it finds itself in.
00:23:46 - And several people, and this was some years ago, using cadaver studies and pulling
00:23:52 - on different muscles in the context of limbs, showed that as you move the limb through the workspace,
00:23:59 - what the muscles did was not a simple relationship and was very complex. flex.
00:24:04 - And this is actually, if you go back and look at some work that people had done
00:24:08 - back even in the 19th and very early 20th century, people did very careful work
00:24:13 - on, say, frog musculature.
00:24:15 - They showed that as you move through the workspace, what the muscles did changed.
00:24:20 - So this idea of a protractor, retractor, pronation, supination,
00:24:24 - these are all fine. You teach medical students that.
00:24:26 - It's very helpful. And in standard configurations, that's how it works.
00:24:30 - We found in the aplysius system, and I'm emphasizing the vertebrate examples
00:24:34 - to begin with to show that I think this is much more general,
00:24:37 - that as one part of the structure moved relative to other parts of the structure,
00:24:41 - the actual functional role of the muscle changed.
00:24:44 - So a muscle that was thought to push a grasper back could actually move it towards
00:24:49 - the jaws in the appropriate context.
00:24:52 - And the two points then that I made, which I thought were very crucial,
00:24:55 - was that what a muscle does is a function of its mechanical context.
00:24:59 - And the second one, which I didn't illustrate but could go on at great length
00:25:02 - because we've shown this, because muscles do that, when you are trying to do
00:25:07 - different behaviors or behavioral variants,
00:25:10 - what you do is you call upon a coalition of the relevant muscles in that particular
00:25:15 - mechanical context to make that happen.
00:25:17 - And some muscles can't be used in a particular context, and other muscles must
00:25:23 - be used, and other muscles can variably be used.
00:25:26 - So the general principle I stated, which I think is a broad truth,
00:25:31 - is that it's actual coalitions, changing coalitions of muscles are what give
00:25:36 - rise to multifunctionality.
00:25:37 - And I think that that's a general principle.
00:25:40 - Right, but it's interesting, right? Because now we went from this worm to the
00:25:46 - idea to test it on this grasping response of a plesia.
00:25:50 - Right. And in some sense, we get the complexification of the whole system. That's correct.
00:25:54 - Because the strength of your, let's say, your C.
00:25:57 - Elegans model was like, well, it's one degree of freedom that they can control
00:26:01 - with very minimal control. That's right. Same for your tongue.
00:26:03 - Right. But remember, let's talk about the a plesia grasper.
00:26:07 - If I showed it to you in all its glory, and if you come to Cleveland,
00:26:11 - I'll be happy to do so. Or if you invite me and bring me slugs,
00:26:14 - I'll dissect them out and show them.
00:26:16 - Very complicated. And what you will see is it's a soft tissue structure.
00:26:20 - And if I poke it or excite it, you'll see all this complex shimmying around
00:26:24 - that it's capable of doing.
00:26:26 - So even though there are constraints because of its physical nature,
00:26:30 - and it's not jello, but it is very flexible.
00:26:35 - And there are all sorts of different things that the musculature can do.
00:26:39 - In the analysis that I'm doing, we're really simplifying down to talk about
00:26:43 - essentially two degrees of freedom, which is opening and closing and protraction and retraction.
00:26:49 - So again, I didn't give a lecture on aplysia feeding. If you asked me to do
00:26:53 - that, I would have happily done so.
00:26:54 - So we've taken this whole complicated structure that has all sorts of different
00:26:58 - potential degrees of freedoms and maybe 15 or 20 muscles.
00:27:02 - And we actually think about it and we can show that in terms of the behavior,
00:27:06 - if you just focus on opening and closing and protraction and retraction,
00:27:10 - you can create all of the different functions I talk about, biting,
00:27:13 - swallowing, and rejection.
00:27:14 - By changing the timing, the duration, and the phasing of those key two components,
00:27:20 - you can get all the different behaviors.
00:27:21 - So in fact, we see that as an enormous simplification. In addition,
00:27:26 - and again, I didn't have time to talk about this,
00:27:30 - Because the musculature is fairly slow, we can actually have done some analysis
00:27:35 - of the mechanics in detail.
00:27:37 - We can define what we call neuromechanical equilibrium points.
00:27:41 - So for a given pattern of activation in the neural outputs, we can actually
00:27:47 - predict the trajectory that the musculature will take over the next several
00:27:51 - hundreds of milliseconds.
00:27:55 - And we can talk about that as the target the animal is aiming towards.
00:27:58 - You're dealing with very low
00:28:00 - mass, a fairly viscous system, so inertial forces are not in the picture.
00:28:05 - So it's elastic and viscous forces.
00:28:10 - So velocity-dependent forces, as well as position-dependent forces.
00:28:14 - So not so much mass-dependent forces. So what you're dealing with is a system
00:28:20 - where you can actually see smooth trajectories and you can talk about these
00:28:24 - equilibrium points defined by the neural control as well as by the mechanics.
00:28:29 - And that it provides also a huge simplification for thinking about where is
00:28:33 - the nervous system trying to push the whole, the properties of the system from one set to another.
00:28:39 - So even though it's a much higher dimensional system, this idea of understanding
00:28:44 - the mechanics properly and analyzing it properly and taking the muscular hydrostatic
00:28:49 - properties and using that to see where is the system going,
00:28:53 - how to analyze it, has led to simplification.
00:28:55 - But it is a more complicated system, so it doesn't simplify down quite as much
00:29:00 - as the original one I started with.
00:29:02 - Right. But the question that this raises is that, okay, but now what are the
00:29:07 - implications of neural control, right?
00:29:09 - And how does the biomechanics actually constrain that and make it solvable?
00:29:13 - Because in some sense, isn't this sort of a restatement of the Bernstein problem in some sense?
00:29:19 - Because you say, look, I have many different ways to perform a movement,
00:29:22 - so in that sense- Yeah, exactly. Well, he pointed out two different things.
00:29:25 - First of all, he talked about the invariance of the motor field.
00:29:28 - And I think this is still something we do not understand. I can learn to sign
00:29:33 - my name under a microscope or with a paintbrush against the side of a barn.
00:29:39 - And I'm going to be, in one case, using a very, very tiny set of muscles.
00:29:43 - In the other case, I'm going to be running with my entire body,
00:29:45 - and the signature will look the same. So he identified early on this notion
00:29:49 - of motor invariance that I think is still a very deep question that has not
00:29:55 - really been properly addressed.
00:29:56 - Part of the reason I'm so excited about the neuromechanical equilibrium points
00:30:00 - in the slow system, I know about equilibrium points, the fast system,
00:30:04 - and all the debates in that area.
00:30:05 - I am agnostic about that, and I've seen the debates pro and con.
00:30:09 - But in my system, which is much slower, where I think we can much more easily
00:30:14 - make the argument and then show
00:30:15 - data for it, I think this is an incredibly powerful organizing principle.
00:30:21 - The point that Bernstein was making, though, was he doesn't ever use the term
00:30:26 - cursive dimensionality.
00:30:27 - And he doesn't ever, in his book, at least as I've read it, say excess degrees
00:30:31 - of freedom are a problem.
00:30:33 - What he talks about is how they are sculpted into these biodynamic waves that
00:30:38 - give you this patterning And that when individuals go through the lifespan.
00:30:44 - You see these pattern movements that change over age, or if someone has a wooden
00:30:50 - leg that they have to use as a prosthetic, how they incorporate it into the biodynamic waves.
00:30:54 - He had this vision of an internal dynamics that was mapped onto those degrees
00:31:01 - of freedom and that provided some kind of unification and simplification of their control. crawl.
00:31:06 - So I've been very influenced by that, I think. And so if I'm restating something
00:31:10 - that Bernstein said, you've just complimented it.
00:31:12 - No, no, look, this is what it sounds like a little bit. But what I'm looking
00:31:16 - for is then, okay, we have all these possible combinations or different,
00:31:21 - if you want, now skeletal motor contexts in which a muscle can act.
00:31:24 - On the one end, you have your biomechanical constraints that will limit this in some sense.
00:31:28 - But now the question is, how does your neural control now tune itself to then
00:31:33 - these, let's say, valid combinations, right?
00:31:35 - This valid set of muscle combinations. So you're asking a very interesting question.
00:31:40 - So this actually would jump me to the end of the talk and the question of initial conditions.
00:31:45 - So let me spell this out. So I have a colleague at Case.
00:31:48 - Her name is Lynn Landmesser, and she's been studying the early development of
00:31:51 - the nervous system, especially the spinal cord, for many, many years.
00:31:54 - One of the things that she has discovered is that well before birth,
00:31:57 - there are spontaneous simultaneous patterns that start in the nervous system, in the spinal cord.
00:32:03 - And so prior to having wired up, this is before motor neurons make contact with the periphery.
00:32:12 - Regular dynamic patterns are established.
00:32:16 - Once contact is made, there are trophic factors that allow the size of the musculature
00:32:22 - to map back onto the nervous system and increase, for example, proliferation.
00:32:27 - So a larger periphery will generate more motor neurons.
00:32:31 - But also, not just in terms of numbers, in terms of the dynamics, once there is contact.
00:32:38 - Contact. Another investigator who's studied chicks before hatching,
00:32:44 - Anne Becky, has shown that there are spontaneous movements that,
00:32:48 - and so again, any mother will tell you about the fetal kicking and stuff like that.
00:32:52 - There is an ongoing coupling that's happening prior to birth,
00:32:56 - whereby the periphery is providing feedback, sensory inputs,
00:33:00 - and the nervous system is generating these spontaneous dynamic patterns,
00:33:06 - and they're shaping each other.
00:33:09 - And that's what you have to start with when you're born.
00:33:12 - So you're not starting with a tabula rasa. And an infant doesn't lie,
00:33:16 - they're passive and immobile. It's already moving things around.
00:33:19 - And then, I mean, this is like a major, major breakthrough for my kids,
00:33:22 - when you can finally get the thumb into the mouth reliably and keep it there. That's like amazing.
00:33:28 - So there are some built-ins like suckling, and then there's these more complex
00:33:34 - things that are built in and there's this dynamic continuous reshaping between the periphery,
00:33:41 - its experiences in the world and the nervous system. That's what's going on there.
00:33:45 - So what happens is this kind of jittering around, this play,
00:33:49 - this activation, this is the dynamic way you tune that system so the nervous
00:33:54 - system knows about the degrees of freedom that are out there and it takes advantage
00:33:58 - of as many of them as it needs to.
00:34:00 - The other thing you raised in part of your question to my talk,
00:34:03 - which I just have to talk about again, because I thought it was a very important
00:34:07 - issue, especially in higher organisms like ourselves,
00:34:10 - useful repetitive patterns are speeded up and they become part of our repertoire.
00:34:17 - They are like reflexes. And that's not something you see in all animals.
00:34:21 - Many, as I said, in the lower organisms, you have these fixed action patterns
00:34:24 - and those are pre-wired and they can't be changed.
00:34:28 - I didn't talk about this in detail, but a wasp that's going through nest building,
00:34:31 - if you interrupt it and then let it continue, it's just like a machine.
00:34:35 - It just goes back and continues doing exactly what it was doing.
00:34:37 - You would not see that in a primate. You would certainly not see that in a human.
00:34:42 - And yet, if there's something like our tennis swing or piano playing,
00:34:46 - that becomes an automatic behavior.
00:34:48 - So again, very important. So this tuning, shaping, and this dynamic view that
00:34:53 - I'm trying to argue is how the nervous system does it.
00:34:57 - I don't see that as necessarily working through some kind of internal representation.
00:35:00 - I see this as this trial and error playing around, very good initial dynamics,
00:35:06 - feedback from the environment shaping the connectivity, the local connectivity,
00:35:11 - and generating an effective dynamic model.
00:35:16 - That's what's exploited subsequently. So as if the nervous system is freezing
00:35:20 - its degrees of freedom to control the periphery. When it needs to,
00:35:23 - and then unfreezing them when it doesn't. And that's, I think,
00:35:26 - a really critical insight.
00:35:28 - So that sometimes things look absolutely seamless and incredibly easy,
00:35:32 - but anyone, you say you do sports, so you can remember the difference.
00:35:36 - I mean, this is something that was very striking to me. Watching someone do the crawl.
00:35:40 - When you watch someone who's learning how to do the crawl, you see all the effort
00:35:44 - and you can see all these inefficiencies and the person is doing the movements.
00:35:50 - Then you look at someone who's a trained swimmer and it's this seamless,
00:35:54 - beautiful motion. I mean, it's just gorgeous.
00:35:57 - And there's one motion after another follows in this absolutely seamless way.
00:36:02 - There's a real beauty to it. That coordination,
00:36:05 - that choreography is something that reduces the unnecessary degrees of freedom
00:36:12 - and allows the person to do it incredibly effectively and efficiently.
00:36:15 - Again, I think it's a tuning of dynamics.
00:36:18 - But then we should try to understand a little bit what these key organizing principles are.
00:36:25 - Exactly. And for that, you start to look, this is of control,
00:36:30 - you tied very much to also ideas about, let's say, attractor dynamics and how
00:36:35 - attractor dynamics are regulated.
00:36:36 - So how is that giving us insight in organizational principles?
00:36:40 - Okay, so again, the notion of attractor dynamics and why I'm attracted to attractor
00:36:44 - dynamics is the following.
00:36:46 - First of all, although these are qualitative dynamics in many cases,
00:36:51 - there are both, in lower dimensions, there are very, very rigorous mathematical
00:36:55 - things you can do, and in higher dimensions, very interesting numerical things you can do.
00:36:59 - And what's, again, interesting is that you don't need to have representations
00:37:02 - internally to get very complex behavior, and you get smoothness and robustness
00:37:08 - and the ability to handle noise and perturbations essentially for free.
00:37:12 - You don't have to put in all of that. You don't have to plan for the combinational
00:37:18 - complexity explosion that's going to happen if you have to deal with all the
00:37:22 - possible different cases.
00:37:23 - That comes for free. So attractor dynamics is inherently robust to perturbation.
00:37:29 - That's why we call it an attractor.
00:37:31 - And it has a lot of architecture and it has a lot of tools that allow you to
00:37:37 - set things up and to try to map them onto things like nervous systems.
00:37:42 - So I didn't have time to talk about this in my talk, but Rabinovich has shown,
00:37:46 - and we're starting to do some of this, that some of the ideas that I talked
00:37:49 - about from attractor dynamics can be mapped very naturally onto known neural architectures.
00:37:55 - And what that means then is that doesn't guarantee that you'll know that neuron
00:37:59 - A makes this connection to neuron B.
00:38:02 - But it will say that if this hypothesis is correct, this class of neurons should
00:38:08 - be tightly coupled to one another, and they should be activated together.
00:38:12 - They should inhibit these other groups in such a way that they can't take over
00:38:17 - while this one is activated.
00:38:19 - And then there There should be appropriate, you'll excuse the term,
00:38:23 - recurrent loops such that you can destabilize that pattern of activity and allow
00:38:28 - another one now to generate a burst, be active,
00:38:32 - also have its own internal coupling and keep the other parts silent for that period of time.
00:38:37 - So what's very nice is as I see these descriptions and I look at the models,
00:38:42 - and again, the math is something I can follow so I can actually understand what
00:38:45 - they're talking about. I immediately
00:38:46 - start thinking in terms of the neural circuitry that we're studying.
00:38:49 - And I have some ideas for experiments that I would like to try,
00:38:52 - which would test whether these ideas are of any value.
00:38:55 - And again, as I stressed in my talk, when it comes to theory,
00:38:58 - I'm an absolute agnostic.
00:39:00 - I'm, I do not, I'm not a believer in the sense that, you know,
00:39:03 - this theory is right. That theory is wrong.
00:39:04 - If a piece of a theory will work, I'm great. That's great. Let me take it and
00:39:08 - I'll use it. If it doesn't work. Okay.
00:39:10 - So we'll come up with a better theory or use a different theory.
00:39:13 - But there are two things about the attractor networks.
00:39:16 - So on the one hand, it could also possibly mislead you because maybe in a tractor
00:39:21 - that, that you observe at some, at some level of the state space of this organism,
00:39:27 - because it's up to you on what level you describe it. Right, right.
00:39:30 - Right. These attractors might be some mixture of neural states and biomechanics. That's right.
00:39:34 - And in some sense, you want to tease these factors apart.
00:39:40 - Well, that's the question. But now it's starting to become, because as you said
00:39:42 - yourself, you then interpret your attractor in terms of some recurrent neural structure.
00:39:46 - Okay. So the interesting point that you're raising is this, and this is something
00:39:49 - that came up in the second talk, which we may get to or not.
00:39:53 - I just found that really inspiring. But the issue is that when we look and do
00:39:58 - folk psychology or folk philosophy or anything like that, we tend to carve nature up in certain ways.
00:40:04 - So the elements are earth, air, fire, and water, right?
00:40:09 - Because that's what we see. but then as science progresses we begin to discover
00:40:13 - that that's actually not how nature is carved up at all but we that doesn't
00:40:16 - lead us to abandon the notion that there might be elements.
00:40:20 - It just requires us to rigorously refine what we mean by that and then come
00:40:26 - up with good operational definitions for what is an element what's a molecule
00:40:29 - and what's what's not similarly it seems to me that the formulation of the neuromechanical
00:40:34 - equilibrium point hypothesis orthosis that, again, I didn't talk about in my talk,
00:40:38 - but it's something we're actively working with, may or may not carve nature
00:40:42 - at its joints. We don't know.
00:40:44 - But it's an organizing principle that allows us to look at the bursts of activity
00:40:48 - that come out of the nervous system and the musculature that's active at a particular
00:40:51 - time and say, ah, we can make a good prediction as to where this is going to go next.
00:40:56 - Now, one thing that illustrates the likelihood that this is going to work is the following.
00:41:01 - And again, this is very qualitative, and I immediately admit that.
00:41:06 - But my students set up the animals and they have them instrumented so that they're
00:41:11 - recording from that key muscle I talked about that pushes the grasper forward
00:41:14 - in these three motor neurons.
00:41:15 - I walk in and I don't look at the animal. I look at the recordings. I say, oh, it's biting.
00:41:20 - And my student says, yeah, you're right.
00:41:23 - Now, again, I'm using my visual cortex as a stand-in for some other analyzer.
00:41:31 - But what I'm suggesting is that if we can begin,
00:41:34 - as I and my students have begun, to recognize certain motor patterns as they're
00:41:39 - starting to unfold and make a prediction as to what's going to happen,
00:41:43 - that suggests that these ways of thinking about it and conceptualizing what's
00:41:48 - going on may be very powerful for understanding the organization of the system.
00:41:53 - They may be artificial impositions that we've created that have nothing to do with nature.
00:41:58 - At least being aware of that is the basis for going in and probing the system
00:42:02 - to try to show that you're wrong.
00:42:04 - And so that's one of the things that we make a lot of effort to do.
00:42:07 - So if the systems actually do have a mix of the neural and mechanical,
00:42:13 - and that is actually what's going on, then if we find in the nervous system
00:42:17 - a tractor dynamics which mix those those two appropriately.
00:42:21 - Again, it's not a proof. It's very suggestive. The key point that you're getting
00:42:25 - at, which I take very strongly and is, again, I'm hoping to press this forward,
00:42:29 - depends on resources and depends on students, would be to disrupt the system
00:42:34 - in predictive ways and show that if I take out a particular key interneuron
00:42:41 - that is supposed to instantiate what I'm arguing is one of these attractors,
00:42:44 - and I prematurely turn it off,
00:42:47 - or I turn on another, other, which ordinarily would be inhibited.
00:42:50 - I can predict what that perturbation is likely to do based on the attractor
00:42:53 - dynamics and show that the behavioral changes are similar.
00:42:56 - Now, that's still not a complete proof, but it's a much stronger sense that,
00:43:03 - yes, the way I manipulate the system and modify it is reflecting what's going on.
00:43:08 - It's not just a useful summary for the way I think about it.
00:43:11 - It might actually be how the system itself is built. Right.
00:43:14 - But this is also an important point here because in
00:43:17 - your in your modeling in the end uh
00:43:20 - of these of these tracker dynamics um what we look at in the end was some sort
00:43:25 - of recurrently coupled network where you both had sort of self-excitation and
00:43:29 - then full lateral excitation right in the network and then the obvious question
00:43:34 - that then i also pose is of course well but but why would i believe that that
00:43:40 - reflects, let's say, the nervous system of a plesia.
00:43:42 - And for that, don't we need to impose a few more constraints that relate to
00:43:48 - the sociology of the anatomy?
00:43:49 - And again, again, I didn't have time to talk about this. There's been,
00:43:51 - as I mentioned, now about...
00:43:56 - Nearly it's three and a half decades of work i
00:43:59 - mean irving started focusing on implicit feeding his first
00:44:01 - publication is 1974 but he'd started
00:44:04 - doing that a year or two beforehand so it may be nearly um four decades actually
00:44:10 - and as i said we have about 200 or so of the elements now each of the in the
00:44:19 - controller for the um feeding apparatus is what's called the buckle ganglia
00:44:22 - it's an an organization.
00:44:23 - It's a collection of nerve cells. They're two paired ganglia.
00:44:28 - And in those, there are about 1,000 nerve cells each.
00:44:31 - About 150 of them are motor neurons.
00:44:34 - Some tens of them are key interneurons, and the rest of them are sensory neurons.
00:44:38 - We know many of the motor neurons. We know some of the interneurons.
00:44:41 - We know relatively few, but at least a few of the sensory neurons.
00:44:45 - And we've mapped a lot of the connections. So when I talk about recurrent neural networks, works.
00:44:49 - We can show that this neuron actually not only inhibits this,
00:44:53 - it excites that, and that one comes back and after a delay can inhibit that one or excite that one.
00:44:58 - So we see huge numbers of loops that are based on binary recordings from the nerve cells.
00:45:05 - But still, in these loops, you would have very characteristic transduction delays
00:45:09 - that you might not capture in these models because these models in that sense are more uniform.
00:45:14 - Well, the interesting issue though is remember that for the real nervous system,
00:45:19 - it still has to deal with the low-pass filtering of the musculature, the real musculature.
00:45:23 - So what's going to happen is some of the fast phenomena,
00:45:26 - which are important for the dynamics of the nervous system unfolding in detail,
00:45:31 - are going to be lost when you push them through the periphery because the periphery
00:45:36 - is only going to be getting a low-pass filtered version of that.
00:45:40 - So if our models capture the low-pass filtering properties reasonably well,
00:45:44 - Well, even if not all of the details of the dynamics at higher,
00:45:49 - at faster time scales are captured, we may nevertheless capture some aspects
00:45:54 - of the dynamics of the nervous system that matter for the periphery.
00:45:57 - Sure, but then you still have to show that in terms of the time constants within
00:46:01 - that system, they sort of match that low pass shielded version of the periphery.
00:46:04 - And in fact, what we do see, for example, the B31, B32 interneurons,
00:46:08 - which I very briefly mentioned and have been characterized. So back in 96,
00:46:12 - this was a collaboration between my lab and Avi Susswein.
00:46:15 - This was published in Journal of Neurophysiology, two companion papers.
00:46:18 - These neurons generate plateau-like potentials. So when they get turned on,
00:46:24 - they actually fire for an extended period of time.
00:46:27 - And their axons go out to the periphery, and they activate the grasper's protractor
00:46:32 - muscle, the I2 muscle. Awesome.
00:46:34 - So the time course of their activation is very well tuned to the muscle.
00:46:40 - And in fact, we did a paper in 1999.
00:46:42 - This was a collaboration with Pat Crago.
00:46:45 - And a student that he and I shared, we actually did a detailed biomechanical
00:46:49 - study with a servomotor to look at what happens as you stimulate the nerve and
00:46:54 - looked at the force frequency, length, tension, and force velocity properties of the muscle.
00:46:58 - And we actually built a model of it based on the neural inputs.
00:47:02 - And we show that if you took EMG recordings, you could actually get movements
00:47:05 - out of that model that look very similar to the ones that you actually measured.
00:47:09 - So again, when I talk about that kind of transduction stuff,
00:47:13 - it's not based on just, well, I think it's okay.
00:47:15 - We actually have done a lot of that hard work. Not as much as,
00:47:18 - I mean, it's a sensitive point for me, for any biologist, because there's always more that I can do.
00:47:23 - And so immediately you're going to get me on the defensive correctly,
00:47:26 - because talking to my fellow biologists, they're going to demand even more,
00:47:30 - and I demand that of myself.
00:47:32 - But we have gone quite a far ways to argue that the things that we're seeing are right.
00:47:38 - So for example, one of the things we found, which is quite interesting,
00:47:41 - if you look at the force-frequency relationship, if the neuron fires at 6 hertz
00:47:48 - or 5 hertz, very little happens in the muscle.
00:47:51 - If it goes above 10 hertz for a period of about 200 or 300 milliseconds,
00:47:57 - force begins to develop.
00:47:58 - And that was just something we found from actually doing the stimulation.
00:48:02 - Now you look in the animal or you look on the muscle and you look at its activation
00:48:06 - pattern, and lo and behold, When protraction is occurring, it goes on very intensely
00:48:12 - above that frequency for that period of time at least.
00:48:15 - And what we also showed was that if you increase the duration of the activation,
00:48:20 - you can increase the force the muscle generates.
00:48:22 - It's very sensitive to that. So yeah, we are very sensitive to those issues
00:48:27 - and our system is sensitive to those issues.
00:48:29 - Right, but you do agree that that's sort of right now in the future, right?
00:48:33 - To match these ideas about… Well, what I'm saying is that for this one particular
00:48:36 - example, this one muscle and this particular set of interneurons,
00:48:39 - we actually have done the matching and it matches well.
00:48:41 - Right. So does that tell me that the rest of it matches? Of course not.
00:48:45 - I take your point very strongly.
00:48:46 - But what it does is it encourages me to say, hmm, we might really be on the right track.
00:48:51 - Right. So would you claim that the plesia brain is also like a liquid state machine now?
00:48:56 - No, I don't think I would.
00:48:59 - Okay. Nor would I claim that it's a quantum computer. All right. Good. That's progress.
00:49:08 - The last type of experiments that you were describing were these simulations
00:49:12 - of, let's say, sort of a very hybrid kind of model.
00:49:17 - Yes, the artificial insect. Exactly, yeah, different bits and pieces.
00:49:20 - How many insects did you, or different species did you combine there?
00:49:24 - We used probably five or six different stories that people had developed over
00:49:28 - the years and put them together.
00:49:30 - I mean, one of the things that was amusing to me, people had warned me about
00:49:33 - this, but you can have 30 or 40 years of hard neurobiological work,
00:49:37 - And one model can eat all that up in about a month or two. It's amazing.
00:49:41 - It's so scary. And then the person's coming back to you and say,
00:49:44 - well, what should we do here?
00:49:45 - I said, well, they haven't done the measurements yet. So what am I supposed to do?
00:49:49 - Now, Randy wasn't like that. He actually mastered the literature and he had good ideas himself.
00:49:55 - But the parts of this that we were the least satisfied with were ones where
00:49:59 - we had to actually make things up.
00:50:03 - And to the extent that we could use dynamics and connectivity,
00:50:07 - that actually emerged from the literature but had never been looked at this
00:50:11 - way we were both much happier and it was very interesting because no one had
00:50:15 - to at the time we did this try to actually number one take all these different
00:50:20 - things and put them into a model but number two try to create an entire functioning.
00:50:26 - Agent that could actually get around and do stuff I mean this is quite some
00:50:31 - time back already Yeah, we did this in the late 80s, and that review article
00:50:35 - that came out in American Scientist was in 1990.
00:50:38 - Right. So this was quite a while. So Rodney Brooks was at that time just starting
00:50:43 - to do some of his interesting work.
00:50:44 - And it was very interesting to me because I think he was reasonably successful
00:50:48 - when he was down to the level of doing these, you know, Genghis and these other robotic insects.
00:50:55 - When he went up to cog and things like that, the progress slowed down very substantially.
00:50:59 - Absolutely. No, this is clear.
00:51:03 - But then, so also there, so earlier we discussed the issue of validation and
00:51:08 - let's say the neuronal level.
00:51:09 - And then with these experiments, we had to make this issue of validation of the behavioral level.
00:51:13 - Yes. Because at what point, when can you really say, look, behaviorally this is valid, right?
00:51:17 - Is it all about, okay, as long as it survives my simulated environment,
00:51:21 - or should we look at very, let's say, as long as it displays the same behavioral
00:51:25 - patterns I would observe in the animal on these conditions?
00:51:27 - This goes back, and again, at the beginning, very beginning of my talk,
00:51:30 - I talked a little bit about the role that modeling plays.
00:51:33 - And I didn't specify this, but let me sort of clarify this because it's something
00:51:37 - I've given a lot of thought to, and I like that question very, very much.
00:51:40 - There are two really different ways of thinking about what a model or a theory can do.
00:51:46 - One is a quantitative predictor of what an actual system will do next.
00:51:52 - And the other is almost a kind of applied philosophical exploration of the space of possibilities.
00:51:59 - Those are often much less respected, but they can be very important because
00:52:04 - they can say, look, we didn't even realize that you could create from these
00:52:09 - various ideas that are out in the literature,
00:52:11 - a device that could actually survive in this very simplified environment for
00:52:15 - an extended period of time, that already changes how people couple with the process of modeling.
00:52:22 - That gets people who would ordinarily scoff and say, models,
00:52:25 - waste of time, to say, wow, you could really do that?
00:52:30 - I would love to do something like that. And then my immediate response when
00:52:34 - someone comes to me and does that is your question.
00:52:37 - What is your question? What do you want to do with it?
00:52:40 - Are you interested in trying to capture a behavioral phenomenon here?
00:52:44 - Are you interested in a level crossing model? You want to test that some underlying
00:52:48 - mechanism actually generates the macroscopic thing you're seeing?
00:52:52 - Or are you actually trying to make a very quantitative prediction?
00:52:54 - Or are you just an engineer who would like to take some key ideas from biology
00:52:58 - and run with them and make it into a methodology that may no longer have made
00:53:02 - contact with the biology, but could be used in a general way? What's your interest?
00:53:07 - That's the next question. And then the validation, and I gave you a little bit
00:53:11 - of this, comes from how useful is it for those particular very different applications?
00:53:16 - So for an engineer, to the extent that, I mean, as a neuroscientist,
00:53:21 - I think that feed forward neural networks with backprop is a pathetic caricature
00:53:25 - of the complexity and the richness of the dynamics of real nervous systems,
00:53:29 - but as a way of generating interesting mappings,
00:53:32 - it's fascinating.
00:53:34 - If, on the other hand, vector array machines can do just as good a job or faster,
00:53:38 - then they're going to take over. And I have no problems with that.
00:53:41 - But then if I say, well, understanding the nervous system isn't that useful,
00:53:44 - see what happened with neural networks.
00:53:46 - I say, well, that was because the engineers took the part that they could actually
00:53:51 - controllably work with and claim that that was the whole thing.
00:53:55 - And if it wasn't, then maybe they missed some of the parts that matter, like the dynamic.
00:54:00 - Right. But the two issues you highlighted here is on the one hand to be able
00:54:06 - to predict for the system itself what it would do next in a certain situation. Right.
00:54:11 - But what you seem to exclude in that summary is to predict back into the empirical domain.
00:54:17 - So are you excluding this? No, not at all. Not at all. Because again,
00:54:20 - I talked a little bit about level crossing models before you came,
00:54:23 - and I want to emphasize this.
00:54:25 - And I showed that again when I showed in the Eplizy example.
00:54:29 - We had this hypothesis about how this muscle could do essentially two different
00:54:34 - things, push the grasper forward or backwards.
00:54:36 - And building a physical instantiation said, yes, it could.
00:54:39 - It didn't prove it, but it said, yes, it could. Moreover, it said,
00:54:43 - if that's true, it might have to be activated.
00:54:46 - Again, there was this sequence of activations that we had to use.
00:54:50 - That suggested something for what you might see in the animal,
00:54:54 - which could be tested. it.
00:54:55 - And in fact, we've been spending time looking at the different domains.
00:54:59 - It turns out that jaw muscle actually has different domains,
00:55:01 - and there are different motor neurons addressed to the different domains,
00:55:04 - anterior and posterior, and their activation patterns change in time depending
00:55:09 - on what behavior the animal's doing.
00:55:11 - So some of the things that the robot was doing was actually suggesting something
00:55:15 - about what you might see at this broad level if you actually look carefully
00:55:19 - at the biological system.
00:55:23 - So I love when that happens, and I'm very much aware that that has to happen
00:55:27 - for it to validate it as useful for the biology.
00:55:30 - Okay. But then for the different models you described, which of these comes
00:55:35 - the closest to actually satisfying all these different levels of constraints?
00:55:39 - Well, I think the argument I was making about stable heteroclinic channels and
00:55:43 - the possibility of mapping that onto neural architectures.
00:55:47 - The reason I'm so excited about that, of course, is we haven't done all the
00:55:50 - hard tests. So I can pretend in my mind that it all works.
00:55:54 - But my sense is that that could actually be a way of breaking open these problems,
00:55:59 - and especially in systems where you are trying to generate a stable pattern
00:56:04 - of activation of some number of elements.
00:56:07 - Because, again, the low-pass filtering of the periphery is an enormous opportunity to simplify.
00:56:13 - Amplify, because what it means is that really, really fast transition changes
00:56:17 - are going to all be filtered out, so that you're going to have to maintain state
00:56:21 - in the nervous system for long enough for that message to get out and have an
00:56:25 - impact on the periphery.
00:56:27 - And that means that some neurons are going to go into saturation,
00:56:29 - others are going to be off, and that's going to happen for hundreds of milliseconds.
00:56:34 - So that then means that you can define, it's a little artificial,
00:56:38 - but you can define stable patterns
00:56:39 - in the nervous system and look for the mechanisms that generate them.
00:56:43 - And that's the thing that we would then be looking for. And that would be corresponding
00:56:46 - to these various attractors that we've talked about.
00:56:49 - So that's, I think, kind of exciting. Yes, absolutely.
00:56:52 - So in the end, when you also try to make this step towards engineering,
00:56:59 - you highlighted four principles that you felt, look, if you want to talk about biomimetic,
00:57:04 - engineering or biologically based engineering, then what you have to think about
00:57:09 - is evolution, learning, development, and initial condition. Yes.
00:57:12 - Right. So why are these now the four key principles you would like to generalize into engineering?
00:57:17 - All right. So let me go through them. And that's a wonderful question.
00:57:23 - Engineering, design.
00:57:25 - Um, and then there's manufacturing, then there's control, and then there's history.
00:57:33 - I think those are the four things. So if we look at, uh, cars,
00:57:38 - so there's a, in Cleveland, there's the auto aviation, uh, museum because Cleveland
00:57:43 - was actually, people forget this, one of the birthplaces of various different
00:57:46 - kinds of automobile companies. journeys.
00:57:49 - And the initial version of cars is really a horseless buggy.
00:57:56 - They look exactly like buggies did in the late 19th century,
00:57:59 - and there's a motor in front. And that's what they look like.
00:58:02 - They don't look at all like what we think of as cars.
00:58:04 - It took some time for people to free themselves from that.
00:58:08 - Now, to some extent, engineers, because of the desire to create disruptive technologies
00:58:14 - are very into this idea of coming up with something completely new.
00:58:18 - So one of the things you're doing to take notes is an iPad, which I think is
00:58:23 - a beautiful example of disruptive technology.
00:58:25 - People had come up with various tablet architectures and none of them had this
00:58:29 - sort of seamless integration and this way of doing media and this way of really
00:58:34 - just beautifully, elegantly putting all the different things together.
00:58:37 - And so this is displacing and it's defining a whole new market.
00:58:41 - So there's an enormous press for that in engineering.
00:58:44 - In biological systems, history matters a great deal, because as I said,
00:58:49 - when an organism is born, it has to be up and running instantly.
00:58:53 - And so initial conditions become absolutely crucial.
00:58:57 - If you start off with an organism that more or less has to be programmed from
00:59:01 - birth in very complex ways,
00:59:03 - then you had better build around it a social structure that allows it to be
00:59:07 - taken care of in its helpless state for years and years until it finally is
00:59:13 - released into the world.
00:59:14 - Human infants fall into that category. But if you're an insect,
00:59:19 - you don't have that luxury. You get up out of the egg and you walk away.
00:59:23 - And when you molt, you go someplace, you're still for a while,
00:59:26 - and then you discard your old body and walk away from it and immediately start doing things.
00:59:31 - You don't have the luxury to relearn how to do various things. It just has to work.
00:59:37 - Okay? So that's why it's stressed initial conditions. And that initial conditions
00:59:41 - also, I wanted to, that really dovetails with all the other things.
00:59:45 - What the tendency among engineers, and I completely understand that having done
00:59:49 - some engineering myself, you want to come up with this really brilliant idea.
00:59:52 - And then you want it to be a design principle.
00:59:54 - And so you have your hammer and everything is going to be a nail, okay?
00:59:59 - So if you are a control theorist, then everything is going to put in the rubric
01:00:03 - of control theory. If you're an information theorist, everything can be stated
01:00:06 - in terms of information theory.
01:00:07 - If you realize the power of Kalman filters, then everything is going to be done
01:00:12 - using a Kalman filter, et cetera, et cetera, et cetera.
01:00:14 - But biological systems, the initial conditions is just an example of how much history matters.
01:00:22 - Evolution, which is not a design process of the sort that engineers are used
01:00:26 - to, is one that builds on what already exists.
01:00:30 - It's canalized by all the things that have happened beforehand.
01:00:34 - And it basically is a proof of principle system for design.
01:00:39 - If you leave offspring, you're good.
01:00:42 - And if you don't, you disappear from the gene pool and you become extinct and that doesn't work.
01:00:46 - And it doesn't mean that there was anything wrong. Many of the solutions that
01:00:49 - dinosaurs came up with, if we had a way of recreating a dinosaur,
01:00:53 - we might find the things that they're actually much better at than organisms
01:00:56 - that live in our world now.
01:00:59 - It wasn't a statement that they were somehow inadequate.
01:01:02 - Changes in their environment at that time were ones they couldn't tolerate,
01:01:05 - so they went extinct. And that could happen to us just as easily as it happened to the dinosaurs.
01:01:09 - But engineers might say that actually right now in their practice already,
01:01:15 - they have included things like learning and initial conditions.
01:01:18 - Yes, they've started to. But the realities are that the way they do it is very
01:01:23 - different from the way that biology still does, largely.
01:01:26 - And again, this goes back to when I talked earlier about the wiring up of the
01:01:31 - nervous system, the early nervous system.
01:01:34 - So what happens? So you have, over evolutionary time, you have a genetic code
01:01:38 - that then gives rise to an exponentially
01:01:41 - cascading process that takes one fertilized cell into, I mentioned,
01:01:47 - trillions of cells that are precisely organized.
01:01:50 - Organized, and the plasticity that allowed the development to unfold because
01:01:53 - there are all these local rules that allow the system to wire itself up,
01:01:57 - their gradients, their local cues.
01:01:59 - And so this whole system is not based on some central organizer that looks from
01:02:04 - the top and says, you go over here and you go over there.
01:02:06 - The system is building these cues as it's unfolding and generating new cues
01:02:12 - that help the next stage of unfold.
01:02:14 - It's really amazing. And people have really not wrapped their minds around it,
01:02:18 - either in biology fully or in modeling or in engineering.
01:02:22 - And I see that as a total frontier that we could do amazing things with.
01:02:25 - And that's also to which you apply, let's say, your Swiss army knife metaphor, right?
01:02:30 - Precisely. What we would do, and I have no complaints about this.
01:02:33 - I write code because I program, and I loved it. I mean, it's enormous fun.
01:02:37 - You are the god of your own world. You can make anything happen.
01:02:41 - But if you're a good coder, you are trained or you learn very quickly.
01:02:45 - You don't want self-modifying code.
01:02:47 - That's a bad thing. And you don't want to use other people's old code because you can't figure it out.
01:02:51 - And you want to make it modular and you want to give functions that have well-defined
01:02:55 - functionalities. You want to have clean interfaces.
01:02:57 - I mean, the Unix operating system, the pipes, forks, setting up processes,
01:03:03 - killing the processes, it's beautiful.
01:03:07 - It's beautiful. So again, I actually, unlike many biologists,
01:03:11 - I have immersed myself in how
01:03:13 - engineers do things, and I've built and used those tools, and I love them.
01:03:17 - But when I look at the biological systems, I don't see anything like that going on inside.
01:03:21 - And so what that tells me is, wait, if I really want to do this,
01:03:26 - certainly a digital computer can emulate anything, but the architecture is so
01:03:31 - different that it's not going to fall naturally on it.
01:03:33 - And I'm going to be fighting with it, and I'm going to run into all these computational
01:03:37 - bottlenecks because I don't even have an architecture that's massively parallel,
01:03:41 - asynchronous, and even though it's much slower, can do all of this stuff just
01:03:46 - naturally. And that's what I have in the nervous system.
01:03:48 - So what's been fun for me, but it's also sometimes frustrating working with
01:03:52 - engineers, is trying to open their eyes to the fact that really there is this
01:03:58 - other technology and there's another way of thinking about it.
01:04:01 - So why engineers do what they do is something that completely makes sense to
01:04:05 - me, and I don't want to encourage them not to do that.
01:04:07 - I want them to explore, take forays into, be willing to take risks,
01:04:11 - and try thinking about things really in a different way.
01:04:15 - And that's where immersing yourself in the biology and spending time with someone
01:04:19 - who, like myself, really cares about talking to engineers, knows math,
01:04:24 - actually enjoys these kinds of conversations, is a valuable exercise. size.
01:04:28 - One of the things I've done in the past with other engineers have approached me.
01:04:32 - So I will sit down with, for example, molecular biology of the cell and work
01:04:35 - through the chapter on development with someone who is interested in that.
01:04:39 - And it blows their mind. But having a guide, it really is like, you know...
01:04:44 - I think of the Aeneid. You really need to have your Virgil. You need to have
01:04:47 - someone with a lamp guiding you through all the acronyms and all this sort of stuff.
01:04:52 - Okay. Excellent. Without that, it becomes almost impossible to understand what's going on.
01:04:58 - So now to get to the finish line. Yes. Two questions.
01:05:02 - So if I now want to exercise this sort of Bernstein invariance of motor control,
01:05:07 - I'm painting the wall of the campus here.
01:05:10 - There's one law that we should apply to a biometric understanding of biology and technology.
01:05:18 - So this is the Hillel-Cheels law. Right, right. What's the one law?
01:05:24 - Must have got it right there. Pay attention to the biology. Okay.
01:05:28 - Pay attention to biology. And then?
01:05:31 - But I would say to the biology. That's the law for the engineers.
01:05:35 - Right. And from the point of view for the biologists, I paint on the other wall. Yeah.
01:05:40 - Um, focus on the principles because the difficulty you have when you come to
01:05:47 - most standard biology talks, even I have difficulty with this because I think
01:05:50 - differently than many biologists. I like math. I like abstraction.
01:05:54 - I'm not focused about, you know, one detail after another. They just bury you in details.
01:05:59 - They love the details. They can't get enough of the details.
01:06:02 - So you're sitting there going, they've got all these names, and they've got
01:06:05 - all these structures, and they have all these details. What matters, please?
01:06:10 - And the answer is, part of the reason it's so difficult to answer that is they
01:06:13 - don't know for sure because it may depend on context.
01:06:16 - But if you're focused on trying to articulate principles, if you think about
01:06:21 - the principles as a biologist, and if you pay attention to the biology as an
01:06:25 - engineer, I think you could make enormous advances in the next decade.
01:06:29 - Decade or two. Okay, but now if I have less patients in a whole decade and I
01:06:34 - want to go visit you five years from now.
01:06:36 - Yes. And I want to say, okay, in 2011, you made this one prediction.
01:06:41 - Today I'm checking whether it actually came out or not. Right,
01:06:43 - right, right. What's this one prediction that you're most enthusiastic about today?
01:06:47 - I would go with the stable heteroclinic channels as something that I hope within
01:06:51 - five years we have some more evidence that they might be relevant to how the system works.
01:06:56 - That would be the one I would go for in terms of my personal work.
01:06:59 - Because that's something I have control over and I see the experiments I would do.
01:07:03 - In terms of fields as a whole, there I have less, I'm less sanguine.
01:07:08 - There are rare places where people are trying to get biologists to actually
01:07:13 - do good biology, to talk to engineers who do good engineering.
01:07:16 - There are a few places. I would hope in five years a few of them have had some
01:07:20 - hits and have gotten other groups to say, oh, wait, you mean a collaboration
01:07:25 - between a biologist and engineer is not just that we get money together,
01:07:28 - we talk once every six months or a year, and then we get more money together?
01:07:33 - You mean we actually have to talk to each other on a daily basis?
01:07:36 - And I would like to see more of that happening, because if we saw more of that
01:07:39 - happening, some of the things I was pointing to, like focusing on development,
01:07:43 - like trying to come up with plasticity that affects global dynamics,
01:07:48 - really, that's going to take an interdisciplinary, cross-disciplinary effort.
01:07:52 - It's going to take the minds, the best minds of people who really are working.
01:07:57 - They're in the trenches, in the biological systems, and they're in the trenches
01:08:01 - building engineered systems, really working together and coming back and forth with each other.
01:08:07 - And again, I've been doing this for years with different colleagues,
01:08:09 - and it's been enormous fun. But it's the reason I've accomplished what I've accomplished.
01:08:14 - That constant willingness to go out of your immediate comfort area and to start
01:08:18 - to talk someone else's language and to see the world through their eyes and
01:08:22 - to recognize that that's the way you have to try to take what you're learning
01:08:26 - and help them see it that way.
01:08:28 - That's, I think, where the most productive things are going to happen over the
01:08:31 - next five years. Excellent.
01:08:32 - So, Lucille, Chiel, thank you very much for this conversation.
01:08:35 - It's a pleasure. Pleasure. Thank you. Sure.
01:08:40 - The CSN Podcast was produced by the Convergent Science Network of Biometrics
01:08:45 - and Biohybrid Systems, a project funded by the European Sevens Research Framework Program.
01:08:53 - For more interviews, recorded lectures, or upcoming conferences in the field
01:08:59 - of biometrics and biohybrid systems, go to csnnetwork.eu.
01:09:06 - And thank you for listening.
01:09:06 - Music.

CSN Podcasts

Hillel Chiel on biomechanics and neural control

Season 2012

Description

CSN Podcasts

Episodes

Peter Gärdenfors on conceptual spaces and knowledge representation

Friedemann Pulvermuller on word meaning and embodied semantics

Nick Strausfeld on brain evolution and cambrian explosion

Moshe Bar on proactive brain and prediction

Mandyam Srinivasan on honeybee cognition and waggle dance

Timestamp

Be the first to leave a comment

Leave a commentCancel

Legal

Follow us on

Hillel Chiel on biomechanics and neural control

Description

Episodes

Timestamp

Be the first to leave a comment

Leave a commentCancel

Tags

Legal

Follow us on

Login to enjoy full advantages

Please login or subscribe to continue.

Go Premium!

Enjoy the full advantage of the premium access.

Stop following

Cancel subscription