WEBVTT

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This is the Convergent Science Network podcast. Leading researchers in the domain

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of neuroscience, brain theory and technology are interviewed by Paul Verschure and Tony Prescott.

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So this is Paul Verschure with Parta Mitra with the Convergent Science Network podcast.

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And in this episode, recorded as part of the CSN Barcelona Cognition,

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Brain and Technology Summer School, we're talking to Majiem Srinivasan, also known as Srini.

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And Srini, in your lecture, you focused very much on the mental life of bees,

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if you want, with a special focus on memory.

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Well, part of it, yeah. The first part was more on low-level vision and navigation.

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Right. And the second part was really more on the cognitive bit.

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So we can start with the first part or the second part, whatever you prefer.

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Good. Well, before we do that, if we think about, let's say,

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the vision or the cognition in bees, could you give us an intuitive description

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of the world in which a bee lives? Yes.

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Well, I suppose it's okay. I mean, it probably depends on whether it's flying

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outdoors or staying indoors in the hive.

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Outdoors, I suppose the world is very similar to what we experience.

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I mean, it's mostly visually oriented or visually dominated,

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I would say, with a bit of olfaction thrown into it.

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When you get close to a flower, you smell it and you either go towards it because

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you recognize the smell or you avoid it because the nectar is not good over there.

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But within the hive, it's of course a completely different situation.

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It's totally dark and there's a lot of pheromonal contact and there's a lot

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of acoustic signaling going on.

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And that's stuff that a lot of other people are studying. I don't know a lot

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about that, but it's certainly a lot of, it's mostly acoustic,

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I would say, inside the hive. Okay.

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So, starting out then with the visual capabilities of bees, this would then

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support their navigational skills.

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Absolutely. So, how do you decompose vision in bees?

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Okay. I mean, as you probably know, insects have these compound eyes,

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which are many, many facets. facets, and we have the so-called simple eyes, camera lens type eyes.

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And so optically, superficially, there's a difference between insect vision

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and our own vision in terms of just how the information, the visual information

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is collected or sampled.

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But the more fundamental difference, as I was saying in the talk,

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is the two eyes of an insect.

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When I say two eyes, I mean the right compound eye and the left compound eye.

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They're very close together.

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So it becomes very difficult to do stereo if you're an insect because the baseline is very small.

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And the only way you can do stereo, the only circumstance under which you can

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do stereo is when your object of interest is very close to your eyes,

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which is when you get a much bigger disparity, of course.

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So this is where it looks like insects have gone a different route.

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And they... Sorry, am I distracting you? No, no, no, no. I'm distracting myself.

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Go ahead. Go ahead. Which is where they...

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They have to use a very active mode of sensing the world in three dimensions,

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and this is done by physically moving in it and measuring the optic flow that

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various objects around them generate.

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Right. So something that's very close to you, if you're moving in a straight

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line, something that's very close to you moves by very rapidly in your visual

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field, and at high speed tells you that this is very close.

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And something that's very far away, on the other hand, moves very slowly,

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and that tells you that that object is at infinity.

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And what we've done is a nice series of experiments maybe the first ones I suppose

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with insects to show that insects really gauge distance based on how rapidly the images are moving.

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So this is done with flying them through tunnels and moving the patterns on the walls as you know.

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But now in insect vision which is a very active area of research,

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there's if you want let's say a prototypical design of a vision system fairly

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hierarchical where you slowly move to let's say a bit more complex and integrative,

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processing of vision-derived signals, but in what sense does the B visual system

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really deviate from that sort of standard template?

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What are the unique features? Okay, what we're finding, I suppose the fly,

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most of that work, the initial work, has been done with the fly.

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And there, you know, the motion-setting system has been studied very well there.

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But I think it's just one part of what goes on in an insect visual pathway.

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I mean, there's lots of other things is happening. For example,

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in bees, again, we're finding what seems to be almost certainly multiple parallel

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pathways, so some of which function the way Reichardt described it,

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with this correlation type motion detector.

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But all of these other mechanisms that are used to sense range,

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for example, they don't seem to obey the Reichardtian laws.

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They seem to be more accurate measuring devices for measuring image velocity,

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independently of the spatial frequency content.

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And independently, largely of the contrast of the scene as well.

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And if you think about it, you need that, because you want to know how far away

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a surface is, regardless of what its texture is, spatial texture,

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or what its contrast is, right?

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So you need a robust system, and the Rijkaard system does not deliver that robustness to you.

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So there must be other systems there which are acting in parallel,

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or which are processing the, you know,

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using several different Rijkaard detectors maybe, multiple channels to get that,

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what do you call it, that vertical information on velocity. So that's one thing.

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Apart from that, of course, honeybees have excellent color vision,

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trachromatic color vision, which they need to detect flowers, recognize flowers.

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And they're really a beautiful learning machine. You can train the bee to learn a color in half an hour.

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Five rewards is enough for a bee to learn a color. So they do great with that.

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And of course, they have a beautiful polarization sense as well,

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which you probably know.

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So to analyze the polarization pattern of the sky and use that as a compass.

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If the sun is hidden behind a cloud, then they can't use the sun anymore,

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but they can still use the polarization pattern of the sky to work out which direction to fly.

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So all of that is there in the bee, which I think, well, color vision certainly

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is not as well developed in the fly. and with polarization vision,

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no one really knows for sure. Right, okay.

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You had mentioned that when you had these artificial small tunnels set up,

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you could fool the bee into thinking that it's flown a smaller distance.

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A larger distance. Flown a larger distance.

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So it's not entirely invariant to the environmental views. Exactly, exactly.

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No, you're absolutely right. But what's neat about that is that,

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as I was saying, and when someone asked the question.

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So you would say, for example, if bees flew in different landscapes,

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they would give you different distance readings, right?

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Because the optic flow they've experienced could have been different.

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Let's say you fly over a plane, over a lake, for example, where there's almost

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no optic flow as opposed to going through a dense forest.

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But what happens is that a bee that goes and finds a food source after it's

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flown over a lake comes back and does a dance and it signals a certain amount

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of units of optic flow, which is the measure of distance.

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And then all the other bees take the same route.

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So they experience the same environment. So whatever calibrations or miscalibrations

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are happening, they're the same for all the bees, and so they cancel each other out, right?

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So that's the way I think, that's why the system probably works.

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But again, there's a little unsolved question there because different bees can

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fly at different heights above the ground.

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And so if you fly higher, you will experience a lower amount of optic flow,

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so the integrated flow will be lower.

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The question is then, do bees actually measure height and take that into account

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when they're doing their odometry, or do they all fly at the same height?

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We don't know that. There's some anecdotal observations which say bees fly typically

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about two meters above the ground, but whether that's really the case,

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no one really knows for sure.

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But if you would have something like a polarized light.

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Compass, if you want. You could always use that to recalibrate and make yourself,

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your distance estimate independent of your altitude.

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Well, the polarization compass will tell you only which direction you're flying

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in. It won't tell you your height about the ground.

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Then you could ignore it because you just instruct your fellow bees to fly in

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a certain orientation with respect to that reference.

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How do you know how far to go, though? The optic flow will depend on how far you are.

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So you have to give you have two bits of information to specify a location in

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a plan, right? You need the direction and you need the distance.

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So you've got to specify the position of the food source in polar coordinates.

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So the polarization pattern of the sun, they give you the compass direction,

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so it tells you, okay, go this way.

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Then you also have to tell them how far to go before you start looking for the food.

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But you could also argue that the

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more minimal model will just take the orientation until you hit a target.

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Yeah, you could, but you see, what is also interesting and probably subtle is

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that when these bees come back home and dance and they're advertising direction

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as well as distance and they're also passing out these nectar samples to these other bees.

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So quite often a bee will stop this dancing bee and beg it for nectar samples

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and this other bee will, you know,

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regurgitate some of the nectar that's brought out. And so this bee can actually

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assess how good that nectar is.

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And based on the distance signaled, it can decide whether it's really worthwhile

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going all that way or not.

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So these bees are evaluating dances that are being produced by different forages

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coming back from different food sites, which are advertising different food

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sources, and they're making up their minds.

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So do I go a long way to get to a good food source? Or can I fly a shorter route

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to get to a not-so-good food source?

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There's a trade-off there. And apparently they're working out the trade-off

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in their minds. So what kind of correlations did you find?

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Between? Well, for instance, the animals that decide to go off on a long foraging run.

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What's the difference in the nectar? So it seems like, so this is not our own

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work, but other labs have investigated it.

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And it looks like what these bees are doing is that they're looking at the ratio

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of calories brought in in terms of energy from the sugar, nectar,

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versus calories expended to get to the food source. And they're trying to maximize that.

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But then still an alternative could also be that you sample the nectar to get

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the chemical fingerprint of your target.

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Yeah. And this could help in your navigation because then again,

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you could go for some odor plume that matches that template to get to your target.

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Sure. No, you could do that.

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It's harder though because what happens is typically if you say,

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okay, this baby comes back and gives you a scent of lavender, right?

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So because you go looking out for lavender, you don't know which direction to

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go. And remember, it's not just a simple diffusion process.

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People in the old days used to think, you know, these scents just diffuse nicely

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and you have a nice diffusion gradient.

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And you just go up the gradient and you'll find the food source, right?

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No, it's not like that. When the wind is blowing, the whole thing is very turbulent

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and you get these little filaments of, you know, scent that are moving around.

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And you've got to be lucky to hit one of those filaments. Sure.

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And then you can zigzag, as you probably know, across the filament and get to

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that. You can do it, but it's tedious.

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Of course. Tedious. Sure. And there's some evidence that insects do,

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when they're close to the goal, they do start looking for the scent.

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And certainly they're using that information, too, to guide their final thing. Yeah.

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But you've shown in your controlled studies that once you've put one of the

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bees through your tunnel with a controlled distance,

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it can signal distance to the bees, which then will fly and will skip sources

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of food to go to the food source at the right distance.

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They're definitely paying attention to the dance, no doubt about that.

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I mean, I guess Paul's question was, yeah, why do they even need a dance?

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But there's no doubt that they are using it. That is definitely the case.

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I was just trying to see what's the minimal, what would be the minimal model?

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The minimal thing would certainly be, yeah.

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I mean, if you got the scent given to you at the hive and the nectar tasted

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good, you could just go out looking for that.

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Exactly. It'll take you a long time. It won't be very efficient.

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And you get some vector, some heading vector, right? Some heading vector.

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Okay, we get a heading vector. Okay.

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Okay. Okay, but while they're going through all that trouble,

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you might as well even provide the distance, right? I mean, it's one more parameter.

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But remember, we were trying to solve the problem, how to deal with altitude,

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right? So actually, it's interesting.

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So this is another thing that came up in the discussion.

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How does a dance actually evolve, right? And so people are not really sure,

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but there's a lot of evidence that other insects will dance too,

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completely out of context.

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Like which ones? There's a species of butterfly, for example,

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apparently, which is a solitary butterfly.

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So it flies a certain distance, and then when it lands, it does a waggle dance.

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It waggles its abdomen, just like the bee.

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But without an audience. Without an audience, exactly. But no one really knows why.

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But it's there. It's some kind

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of epiphenomenon. So maybe it's something metabolic or some other thing.

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And so maybe that has been picked up by these bees and being exploited as a signaling mechanism.

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Has anyone measured the accuracy with which the distance is signaled?

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So if different bees end up at different distances from the target or at different

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angles, has anyone measured the spread?

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So, yeah, typically the distance, the scatter and the distance,

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the Weigel durations is about 10%. And so it's always a percentage of the mean distance.

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But what is interesting is that with the angular error, as we were discussing

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the other day, it looks like when the food source is close by,

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there's a certain angular error in the direction indication.

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But when the food source is very far away, the angular error is much smaller.

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So, they're trying to account for the fact that if you have the same angular

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error, that will signal a much bigger patch when you're further away compared

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to when you're closer, right?

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So, they're trying to compensate for that. There's some built-in compensation, I guess.

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But this is interesting because then apparently this compensation comes at a cost.

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Because when something is nearby, so it's easier to find, they seem to put in

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less effort to communicate that parameter. Yes, yes. So, what's that cost exactly?

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You're saying cost in terms of doing an accurate dance? Right, exactly.

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It seems to me... So you're saying, why are they being sloppy when...

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Yeah. So that's interesting.

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Maybe, I don't know what the energetic requirements are for doing a dance,

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and whether precision involves more concentration.

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Are they really being more sloppy, or is it that when the waggle dance is longer,

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longer the audience gets to integrate for a longer period of time and therefore

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gets a more accurate estimate.

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The actual variance itself, so people have measured physically the variance

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in the axis orientation, and that seems to go down.

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But is that a simple consequence of having a longer down?

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It's possible. It's possible that it's a measurement. It's possible that even

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with the humans who are measuring these dances, maybe there is an element of

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increased accuracy simply because they have a longer sample,

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right? That's a good point.

00:15:50.342 --> 00:15:54.462
But that's only true when the Wegel dance is a highly controlled,

00:15:54.622 --> 00:15:57.722
low noise expression of these parameters. Is that true? Yeah.

00:15:58.891 --> 00:16:03.631
Yeah. So you mean how noisy is it? It's pretty noisy. It's not clean.

00:16:03.651 --> 00:16:05.211
It's a longer integration. Yeah.

00:16:05.671 --> 00:16:08.271
It wouldn't possibly help you to have a bit more accurate estimate.

00:16:08.711 --> 00:16:14.891
And the extreme limiting case is when the foot was very close to the hive,

00:16:14.991 --> 00:16:18.651
when, as you probably know, there's no longer even a waggle dance.

00:16:18.731 --> 00:16:21.631
So the waggle disappears completely and it becomes just a round dance.

00:16:21.851 --> 00:16:25.951
Right. And that simply says, okay, I'm not going to tell you exactly which direction to go.

00:16:26.271 --> 00:16:30.831
Just look within a small radius, 50 meters of the hive, and you'll find it.

00:16:30.971 --> 00:16:34.771
So it becomes very imprecise as you get very close to the origin.

00:16:34.951 --> 00:16:38.811
But the two interesting aspects of this, I find them interesting right now,

00:16:38.891 --> 00:16:44.851
is that you could also argue that on the one hand, it's the dancing bee itself

00:16:44.851 --> 00:16:47.151
who has integrated more or less information.

00:16:47.151 --> 00:16:50.251
Information like if you have to unwind this clock you

00:16:50.251 --> 00:16:53.191
have been winding up the clock for a shorter period of time

00:16:53.191 --> 00:16:55.971
when you found the nectar nearby so there's less to

00:16:55.971 --> 00:16:59.231
display in in the waggle dance would that be an alternative interpretation that

00:16:59.231 --> 00:17:03.371
would make sense yeah i mean the the the original people think

00:17:03.371 --> 00:17:06.371
the way this whole dance first started was in the tropical

00:17:06.371 --> 00:17:09.471
bees which were living not inside these

00:17:09.471 --> 00:17:12.451
dark you know hollow chambers uh you

00:17:12.451 --> 00:17:15.631
know nests that we find in the the european bees now but

00:17:15.631 --> 00:17:18.751
outdoors and um you know and and there most of

00:17:18.751 --> 00:17:22.111
these primitive bees now have dance on a horizontal surface

00:17:22.111 --> 00:17:24.851
not on a vertical surface and they're doing a kind

00:17:24.851 --> 00:17:27.971
of a scaled down version they're simulating the

00:17:27.971 --> 00:17:30.851
flight to the hive so i mean to the food source this is just basically pointing

00:17:30.851 --> 00:17:36.431
in the direction of the food and they're doing it coming back so it's exactly

00:17:36.431 --> 00:17:39.951
as though they're doing us yeah yeah a miniature version of the actual flight

00:17:39.951 --> 00:17:46.531
so in a sense that makes sense So they're coming back and they're sort of replaying that.

00:17:47.091 --> 00:17:50.331
But that's interesting because that creates a new problem for the observer.

00:17:50.691 --> 00:17:55.751
Because now the observer must extract this direction of movement independent of their own position.

00:17:56.371 --> 00:17:57.551
Okay, in the...

00:17:58.546 --> 00:18:03.386
In the outdoor arena, that's fairly easy, I think, because you basically have

00:18:03.386 --> 00:18:08.246
to face in the direction that the bee is waggling, and then you know where the sun is.

00:18:08.566 --> 00:18:11.426
So I say, okay, I'll fly in such a way that I keep the sun over there,

00:18:11.526 --> 00:18:14.586
or the polarization pattern in the sky if the sun is not visible,

00:18:14.726 --> 00:18:16.046
if there's some patch of clear sky.

00:18:16.406 --> 00:18:19.026
Okay, I'll say, okay, I know the orientation of the vector should be that.

00:18:19.406 --> 00:18:25.306
I'll hold that and go along, and I'll go for a distance that tells me,

00:18:25.326 --> 00:18:28.786
you know, how many optical flow units I should experience.

00:18:29.106 --> 00:18:31.726
And then when I get close to that, I'll start looking. Right.

00:18:32.026 --> 00:18:36.786
But now if I take a bee and I observe the bee dance, but now I rotate it a few

00:18:36.786 --> 00:18:40.926
times and then I let it go, will it still orient itself correctly?

00:18:41.466 --> 00:18:43.386
Theoretically, it should. You're not in practice.

00:18:44.646 --> 00:18:46.826
They're not all theoretical physicists. That's a good point.

00:18:47.266 --> 00:18:49.366
That's a good point. I don't know if anyone's done that.

00:18:49.686 --> 00:18:52.446
Because that's a bit the issue. That's very interesting. Because the way you

00:18:52.446 --> 00:18:56.966
describe it is that you physically orient yourself. Okay. As far as people know,

00:18:57.246 --> 00:19:01.906
no one really knows whether bees have a vestibular organ as yet.

00:19:02.026 --> 00:19:04.646
If they do, of course, they could get messed up.

00:19:04.906 --> 00:19:10.046
Absolutely right. Now, the way people think, they assess, they calibrate their

00:19:10.046 --> 00:19:13.346
orientation is the dance is done on the vertical plane inside the hive.

00:19:13.746 --> 00:19:19.066
And the direction of the sun is symbolized by the vertically upward direction,

00:19:19.206 --> 00:19:21.486
which is the direction of negative gravity.

00:19:22.126 --> 00:19:24.406
And people think they have a sense of gravity.

00:19:25.686 --> 00:19:30.346
Looking at the way, for example, your abdomen hangs in relation to the thorax,

00:19:30.366 --> 00:19:35.446
that tells you which direction gravity is, and that's your direction calibration.

00:19:35.846 --> 00:19:39.326
That's what people think. But in addition to that, if they have something that

00:19:39.326 --> 00:19:43.566
senses your angular velocity, as in a vestibular thing, no one really knows.

00:19:44.755 --> 00:19:49.155
But then how do they map that back in the horizontal plane relative to the sun?

00:19:49.215 --> 00:19:52.895
Because then gravity is not helping you. Yeah, yeah. It's very interesting. Completely unknown.

00:19:53.235 --> 00:19:57.275
Okay. It's very interesting. Also, how do they hold this information, you know? Exactly.

00:19:57.595 --> 00:20:02.355
There's the automatic information. They go some distance and it can be,

00:20:02.435 --> 00:20:05.375
you know, several minutes before they come back home and dance, right?

00:20:05.935 --> 00:20:09.975
So where is that information stored? Is it stored as a charge in,

00:20:10.035 --> 00:20:15.075
you know, the membrane potential of some neuron? or is it stored as an activity

00:20:15.075 --> 00:20:16.255
in some kind of place cell?

00:20:16.415 --> 00:20:20.495
My own feeling is that really the mushroom bodies and the insects really are

00:20:20.495 --> 00:20:23.955
like the hippocampus invertebrates and there might be place cells.

00:20:24.175 --> 00:20:27.995
So there could be certain place cells. As you go along to a food source,

00:20:28.135 --> 00:20:30.315
presumably successive place cells are lighting up.

00:20:32.055 --> 00:20:38.155
Now that there is more bee genetics and molecular biology coming on,

00:20:38.215 --> 00:20:43.195
are people thinking of manipulating bees to knock out specific genes.

00:20:43.195 --> 00:20:46.495
Certainly, knocking out the dancing gene, for example.

00:20:46.935 --> 00:20:48.695
Has that been done? No, no.

00:20:49.635 --> 00:20:54.195
I'm sure they're trying to do that. Or are people breeding bees for certain traits?

00:20:54.855 --> 00:20:58.895
Yeah, they've been doing that for a long time, even before the gene was mapped, of course.

00:20:59.075 --> 00:21:03.095
I mean, basically the idea is to breed bees that will produce a lot of honey.

00:21:03.235 --> 00:21:05.535
No, I meant more in the context of the waggle dance.

00:21:06.995 --> 00:21:11.655
Bees that are better signalers? or worse. Interesting.

00:21:12.035 --> 00:21:15.935
It would be very interesting. If you can find out a combination of genes that,

00:21:15.975 --> 00:21:19.295
for example, are responsible for the dance, that's a very basic thing, right?

00:21:19.475 --> 00:21:25.015
Could one not even screen these forward mutagenesis studies?

00:21:25.015 --> 00:21:27.355
I'm sure that's being done. I'm sure that's being done.

00:21:27.455 --> 00:21:30.295
I'm not a molecular biologist myself, but I'm sure there are people doing it.

00:21:30.795 --> 00:21:35.115
There's Gene Robinson in the US who's been doing stuff along those lines.

00:21:35.435 --> 00:21:37.875
I don't know if he'd actually succeeded yet, but that would be really exciting.

00:21:37.875 --> 00:21:41.455
And we started discussing whether this was learned behavior or innate behavior.

00:21:41.875 --> 00:21:45.755
You had some comments on that? Yeah, this is just a guess. I can't be sure.

00:21:45.855 --> 00:21:51.315
But I think probably the basic dance is pre-programmed. It's there like a child

00:21:51.315 --> 00:21:53.775
that has its gate already built in.

00:21:53.935 --> 00:21:57.855
But it's probably learned and refined during the first few days.

00:21:58.455 --> 00:22:01.775
The first three weeks I spent just not doing any foraging. As you know,

00:22:01.795 --> 00:22:03.995
they're just acting as nurse bees inside the hive.

00:22:04.035 --> 00:22:08.035
So there's no need to do any dancing. but it's only after that when they start to go and forage.

00:22:10.475 --> 00:22:13.655
That they start to need to dance. And that's the point, by the way.

00:22:13.735 --> 00:22:18.995
I don't know if Nick mentioned that, but when a bee first starts to go out and

00:22:18.995 --> 00:22:21.615
forage, the mushroom bodies expand hugely.

00:22:22.315 --> 00:22:27.155
So that map is being laid down and there's another piece of evidence that...

00:22:27.155 --> 00:22:29.735
That's interesting. It's really like the London taxi drivers.

00:22:30.335 --> 00:22:35.035
But are the mushroom bodies this very central structure in the bee brain? Yeah.

00:22:35.595 --> 00:22:40.435
Expanding in some uniform fashion or is it certain lobes of it that expand more than others?

00:22:41.820 --> 00:22:45.140
Oh, I don't know about the details. The calyces are the cup-shaped structures.

00:22:45.420 --> 00:22:50.600
They enlarge hugely. And there's still a debate about whether the number of

00:22:50.600 --> 00:22:54.180
neurons is actually going up or whether it's just the density of the neuropil.

00:22:54.560 --> 00:22:59.840
So it could be that the dendrites are getting longer and developing more synapses.

00:22:59.960 --> 00:23:03.340
There's a bit of a debate, I think, about whether the number of neurons,

00:23:03.500 --> 00:23:04.700
whether new neurons are being created.

00:23:04.980 --> 00:23:09.340
I don't know if that's still the case. But certainly all those calyces get very big.

00:23:10.100 --> 00:23:14.460
So then the question becomes just a bit of shift and also towards memory, if you want, right?

00:23:14.520 --> 00:23:18.200
Because you were talking about something like a place cell type response in these mushroom bodies.

00:23:18.560 --> 00:23:22.280
So if we start with, let's say, the basic task these bees have to solve,

00:23:22.360 --> 00:23:24.260
which is, okay, here I go. I'm navigating out.

00:23:24.340 --> 00:23:28.240
I'm finding some nectar and flying back home. And now I'm going to tell everybody about it. Yeah.

00:23:28.700 --> 00:23:32.560
So where's the memory of that? What's the memory of that? What are the physiological

00:23:32.560 --> 00:23:35.100
correlates of it? Nobody has the foggiest idea.

00:23:35.300 --> 00:23:39.180
That's a million-dollar thing. I mean, where is this information stored?

00:23:39.500 --> 00:23:42.660
Again, my guess is that it'll have to be somewhere in the mushroom bodies.

00:23:42.740 --> 00:23:46.640
It'll probably be some place cell that's firing that says, okay, this is the location.

00:23:46.840 --> 00:23:49.720
And that has to be translated somehow into producing a dance.

00:23:50.060 --> 00:23:54.640
Right. But let's approach it maybe different. Let's see, what do we know about

00:23:54.640 --> 00:23:59.740
B-memory structures that could help us understand answering this question?

00:23:59.960 --> 00:24:01.360
Unfortunately, almost nothing.

00:24:01.700 --> 00:24:04.940
Well, we know a little bit about this. Yeah, all that people know is that if

00:24:04.940 --> 00:24:08.900
you knock out the mushroom bodies, a lot of learned associations are lost.

00:24:09.220 --> 00:24:11.040
But that's about it, really.

00:24:11.940 --> 00:24:17.720
That's about it. There's a little bit that you were mentioning that certain

00:24:17.720 --> 00:24:23.240
things, the bees are easier to train, certain tasks, the bees can be trained in easier.

00:24:24.260 --> 00:24:28.280
Whereas other tasks are difficult. And you also had some time courses associated

00:24:28.280 --> 00:24:31.440
with that. Yeah, so there's gradations of learning. So, for example,

00:24:31.580 --> 00:24:34.120
the simplest kind of learning would be color learning.

00:24:34.260 --> 00:24:39.320
It's just so fast and so robust. If a bee learns a color, we learned it,

00:24:39.380 --> 00:24:41.640
as I said, in half an hour, five visits.

00:24:41.860 --> 00:24:48.140
But that means the bee gets rewarded to find that color, and it will find it within five trials.

00:24:48.320 --> 00:24:53.500
That's right. So the idea is, okay, do the reinforcement five times.

00:24:53.620 --> 00:25:00.160
So the classic von Frisch experiment was to have trained them to come on a piece of blue paper.

00:25:01.020 --> 00:25:05.240
Which had a drop of sugar water. And then, of course, he did a very nice...

00:25:05.820 --> 00:25:10.800
He just didn't simply do two different colors because they could be discriminating

00:25:10.800 --> 00:25:13.160
the colors not on the basis of color, but on the basis of intensity,

00:25:13.360 --> 00:25:14.160
on the basis of brightness.

00:25:14.740 --> 00:25:19.260
So what he did was he had a blue sheet that he rewarded the bees on.

00:25:20.540 --> 00:25:23.720
And then so they came five times, got rewarded on that.

00:25:23.980 --> 00:25:28.020
And then he gave them a test where this blue sheet, he took away the sugar water,

00:25:28.100 --> 00:25:31.880
there was no food anymore. and the blue sheet was placed in the midst of,

00:25:31.980 --> 00:25:36.840
or in the general vicinity of a bunch of other sheets of different gray levels.

00:25:37.905 --> 00:25:42.405
And so then he said, okay, let this train bee come and choose where it wanted

00:25:42.405 --> 00:25:43.385
to land. It picked the blue.

00:25:43.685 --> 00:25:48.085
So regardless of the intensity, it was perceiving hue as a separate quality.

00:25:48.585 --> 00:25:52.825
Right, exactly. And then landing on that thing. So that, again,

00:25:52.965 --> 00:25:54.405
takes only five rewards.

00:25:54.705 --> 00:25:58.365
And what's the generalization capability of the bee?

00:25:58.425 --> 00:26:04.645
If I now put this blue patch of paper among many other different kinds of blues, Oh, yeah.

00:26:05.265 --> 00:26:10.145
The delta-lambda discrimination is almost as good as that of a human.

00:26:10.225 --> 00:26:11.345
It's about five nanometers.

00:26:11.545 --> 00:26:14.745
So if you do experience the spectral lights, five nanometers.

00:26:14.925 --> 00:26:17.825
They also have very nice color constancy, the way we do.

00:26:18.225 --> 00:26:22.485
So they can perceive the shade of blue to be, well, they can recognize this

00:26:22.485 --> 00:26:24.125
irrespective of the illumination, largely.

00:26:24.785 --> 00:26:29.505
So evening versus midday, so that color constancy competition is going.

00:26:29.605 --> 00:26:30.725
All animals need it, probably.

00:26:30.845 --> 00:26:33.685
And bees have it too, of course, which is nice.

00:26:35.125 --> 00:26:38.605
Well, that's an exciting issue as well, but maybe we should get back to that

00:26:38.605 --> 00:26:42.545
later before we understand memory, or at least understand what we don't understand about it.

00:26:42.945 --> 00:26:47.625
So I think the behavior leads a long way ahead of the physiology,

00:26:47.885 --> 00:26:51.205
I think, and the circuit knowledge of what the circuits are doing.

00:26:51.405 --> 00:26:54.045
Right, but then let's look at it from a performance perspective.

00:26:54.185 --> 00:26:58.465
I mean, how many landmarks can I memorize as a bee? Okay.

00:27:00.165 --> 00:27:02.685
It depends on how you train them.

00:27:06.065 --> 00:27:12.745
I don't know if anyone has looked at that systematically, but certainly you

00:27:12.745 --> 00:27:18.545
can, in terms of discrimination, you can train bees to distinguish between horizontal

00:27:18.545 --> 00:27:19.945
versus vertical patterns.

00:27:20.185 --> 00:27:23.625
You can train them to distinguish colors, as I mentioned.

00:27:24.325 --> 00:27:27.365
You can train them to distinguish between odors, different scents,

00:27:27.365 --> 00:27:33.705
and you can train them to associate odors with colors.

00:27:34.165 --> 00:27:37.945
So you can have a bee come into a maze, it gets a whiff of scent,

00:27:38.085 --> 00:27:42.265
and then you can train it to say, if you smell lavender, then you go to a decision

00:27:42.265 --> 00:27:44.725
chamber, you have to pick the blue disc.

00:27:45.025 --> 00:27:48.925
If you smell lemon, when you go to a decision chamber, you've got to pick the

00:27:48.925 --> 00:27:54.045
yellow disc, for example, so they can learn to make those associations. Um,

00:27:54.883 --> 00:27:59.963
You can also train them to do kind of a delayed match to sample task.

00:28:00.923 --> 00:28:02.943
You're familiar with the delayed match to sample task? Yeah,

00:28:02.943 --> 00:28:04.763
of course. Just define it.

00:28:04.923 --> 00:28:09.483
So the idea is that the classical experiment is you flash a stimulus,

00:28:09.583 --> 00:28:11.723
let's say a color to a person, blue.

00:28:12.063 --> 00:28:16.443
And then later on, a few seconds later, they're given a choice between blue and yellow.

00:28:16.603 --> 00:28:20.003
So if you see blue as your sample stimulus, you've got to pick the matching

00:28:20.003 --> 00:28:24.443
stimulus with blue. What's the delay the bees can handle? About five seconds. Okay.

00:28:24.883 --> 00:28:30.343
And then after five seconds, it drops off rapidly or gradually? It drops off gradually.

00:28:30.523 --> 00:28:33.383
In about five, it's close to random.

00:28:33.683 --> 00:28:36.243
Okay. But they can also learn non-matching.

00:28:36.803 --> 00:28:38.603
So pick the stimulus that does

00:28:38.603 --> 00:28:43.323
not match. And they can also learn to match across stimulus modalities.

00:28:43.323 --> 00:28:50.103
So you can train them to do the matching task using sense and then expose them

00:28:50.103 --> 00:28:54.323
to a visual task on which they haven't been trained and they will do the matching.

00:28:54.883 --> 00:29:01.123
So, they've learned the concept of matching from the smell task and they're

00:29:01.123 --> 00:29:03.863
applying it to visual tasks. So, they generalize the rule. Exactly.

00:29:04.463 --> 00:29:06.463
Both for matching and non-matching. Yes.

00:29:07.083 --> 00:29:09.503
Okay. Isn't that nice? That's amazing. That's pretty amazing.

00:29:09.703 --> 00:29:10.523
That's really cool. Okay.

00:29:10.883 --> 00:29:15.643
Still don't know how it's useful in nature. Here we're treating these animals as a lab rat.

00:29:17.263 --> 00:29:21.343
I can't imagine that it's… Okay. A certain context, maybe when you land on a

00:29:21.343 --> 00:29:25.663
flower and you get some good reward there, you might want to seek out a similar

00:29:25.663 --> 00:29:27.083
flower which has the same color.

00:29:27.683 --> 00:29:32.543
So in that sense, there's a thing. But you see, in the lab experiment,

00:29:32.663 --> 00:29:35.583
you're not being rewarded on the sample stimulus.

00:29:36.963 --> 00:29:44.043
I find it hard to think of a situation in nature where this particular task has to be applied.

00:29:44.945 --> 00:29:47.545
But they can do it, they can do it, you see. Look, but why is that so hard?

00:29:47.625 --> 00:29:50.485
I mean, here you are, I'm Mr. B flying around.

00:29:50.785 --> 00:29:54.225
I'm visiting different flowers, different colors, different scents and everything.

00:29:54.665 --> 00:29:57.525
And now there might be contingent relations among these flowers.

00:29:57.705 --> 00:30:02.045
That's the rule, right? So maybe when I visit, let's say, a yellow neutral flower,

00:30:02.365 --> 00:30:06.545
I should not go to the blue one because I get absolutely nothing.

00:30:07.485 --> 00:30:10.845
Well, if I first go towards the yellow one and then to the red one that was

00:30:10.845 --> 00:30:12.065
hidden behind it, I get a reward.

00:30:12.205 --> 00:30:16.825
So now I can start to learn contingencies in my environment. Okay, that's sure.

00:30:16.985 --> 00:30:20.325
You could do that, but you've got to...

00:30:24.665 --> 00:30:31.265
Okay, let me say this one thing. I don't know if that answers your question.

00:30:32.165 --> 00:30:38.205
They can learn... It's called... What do you call it? It's symbolic delayed master sampling.

00:30:38.765 --> 00:30:42.625
So not just direct matching of stimuli, but saying, for example,

00:30:42.685 --> 00:30:46.545
if I see blue at the entrance,

00:30:46.805 --> 00:30:54.665
then I should pick the vertical grating versus the horizontal grating in one decision chamber.

00:30:54.845 --> 00:30:59.645
And then you can cascade this. You can go to a second decision chamber where

00:30:59.645 --> 00:31:03.845
you have a choice between two other stimuli, for example, a radial pattern versus

00:31:03.845 --> 00:31:06.045
a set of concentric circles.

00:31:06.245 --> 00:31:10.225
So if you see blue, you pick vertical and you pick a radial.

00:31:10.425 --> 00:31:16.425
If you see yellow, then you pick the other stripe orientation and the other pattern.

00:31:16.625 --> 00:31:20.405
So all those contingencies, they can learn those contingencies.

00:31:20.825 --> 00:31:25.205
Now, so you think that'll be useful in nature?

00:31:25.565 --> 00:31:30.465
For problem solving, I mean, these animals will fight themselves in complex situations.

00:31:31.105 --> 00:31:34.645
They have to navigate through, let's say, dense growth.

00:31:34.885 --> 00:31:39.925
There might be relationships between flowers. Yeah. So, chances are in nature,

00:31:40.085 --> 00:31:43.665
you probably won't find the stimulus changing in the same location.

00:31:44.513 --> 00:31:46.633
But you could certainly apply it to two different trajectories.

00:31:46.633 --> 00:31:50.593
For example, if I come here, if I come to this location, you know,

00:31:50.633 --> 00:31:52.693
I recognize this blue, blue something here.

00:31:52.853 --> 00:31:56.293
And that tells me how I should proceed. Whereas if I see something else here.

00:31:56.373 --> 00:32:00.753
Right. Moreover, it can pick up regularities among plants.

00:32:00.953 --> 00:32:04.293
Because let's say the plant from which you want to get the nectar,

00:32:04.433 --> 00:32:07.993
the flower, might not be easily visible from their altitude.

00:32:08.353 --> 00:32:14.073
But however, the tree just next to it is. Sure. Right. And maybe the flowers

00:32:14.073 --> 00:32:16.413
you like grow close to certain trees.

00:32:17.253 --> 00:32:21.393
This is completely reasonable. So now I can extract these regularities from

00:32:21.393 --> 00:32:22.913
my environment and immediately apply them.

00:32:23.373 --> 00:32:27.273
I agree. Well, thank you for pointing that out. How's my bee psychology going?

00:32:28.053 --> 00:32:31.373
I feel a lot more encouraged about what I'm doing.

00:32:32.653 --> 00:32:37.893
Great. We're so pleased to have you here. What got you interested?

00:32:37.993 --> 00:32:39.213
You're giving me a reason to live.

00:32:41.013 --> 00:32:44.593
Great. But what got you interested in bees in the first place?

00:32:44.933 --> 00:32:45.993
Oh, it was purely accident.

00:32:47.313 --> 00:32:50.013
Everything I've done in my life has been just not planned at all.

00:32:50.133 --> 00:32:55.473
So I did my undergraduate in electrical engineering, as you probably know, in Bangalore.

00:32:55.593 --> 00:32:59.813
And when I was doing my master's, my professor suggested, and that was,

00:32:59.853 --> 00:33:03.313
by the way, my master's was, and it was called Applied Electronics and Servo

00:33:03.313 --> 00:33:04.573
Mechanisms in those days.

00:33:04.913 --> 00:33:07.093
So it's mostly control theory and electronics.

00:33:09.533 --> 00:33:10.793
Too soft? I'm sorry.

00:33:12.973 --> 00:33:17.153
And with the Masters, my professor said, why don't you do something biological?

00:33:17.373 --> 00:33:20.393
Why don't you try to model a biological system using the control system theory

00:33:20.393 --> 00:33:21.333
that you've been studying?

00:33:21.473 --> 00:33:26.293
And so we decided to try and model the human eye movement system as a target

00:33:26.293 --> 00:33:28.733
tracking system, a moving target.

00:33:29.033 --> 00:33:32.773
So that's how I got interested in this thing. And when I went to the U.S.

00:33:32.773 --> 00:33:38.273
To do my Ph.D., I was looking for some project in the area of interface between

00:33:38.273 --> 00:33:42.053
biology and engineering, and it turned out the only person who was doing anything

00:33:42.053 --> 00:33:46.253
in that area was the person who was working on insect eyes. So I got into fly vision in that way.

00:33:47.358 --> 00:33:51.298
And then when I went to Zurich, Zurich in Switzerland was the,

00:33:51.378 --> 00:33:56.078
you know, one of the world's, you know, sort of leading areas in bee work.

00:33:56.878 --> 00:34:00.758
So there, there's a lady called Miriam Lehrer, who's unfortunately passed away

00:34:00.758 --> 00:34:03.418
now, who is the world's expert in training bees.

00:34:04.198 --> 00:34:08.418
She was with Rudiger Wehner? Exactly. So yeah, it was in Rudiger Wehner's lab.

00:34:08.498 --> 00:34:10.438
Yeah. So that's where I learned all about bees.

00:34:10.538 --> 00:34:13.378
So it was purely by accident, but it was a wonderful accident.

00:34:13.538 --> 00:34:14.638
They're just amazing creatures.

00:34:15.198 --> 00:34:19.078
Right. And so, but how long ago was that your first encounter with the bees?

00:34:20.798 --> 00:34:23.958
Well, if you ask me to give away my age.

00:34:24.978 --> 00:34:28.998
No, no, we won't go that far. Well, that would be, that would have been 1982

00:34:28.998 --> 00:34:31.258
when I was a young assistant professor.

00:34:31.918 --> 00:34:38.198
Okay. But then, so the bee is still your main target preparation in the empirical work?

00:34:38.318 --> 00:34:41.358
I would say so, yeah. We're starting to work a little bit with birds as well,

00:34:41.478 --> 00:34:44.518
but bees are the main thing.

00:34:44.518 --> 00:34:49.218
Thing yeah so so then there are two issues to explore right so so with respect

00:34:49.218 --> 00:34:57.418
to the to the b cognition we touched a little bit on this issue of memory um but now so is this sort of,

00:34:58.018 --> 00:35:03.278
this ability to extract these symbolic rules would you see that as really the

00:35:03.278 --> 00:35:11.258
the highest level of of of b cognition that can be achieved or other tricks in their cognition bag?

00:35:11.418 --> 00:35:15.318
Well, there are a few things which are really kind of striking.

00:35:15.498 --> 00:35:20.218
That's one thing. The other thing we were talking about the other day was maze learning.

00:35:20.438 --> 00:35:24.018
They learn to go through labyrinths and various kinds of labyrinths.

00:35:24.978 --> 00:35:32.318
But there's also the business of breaking camouflage and perceiving camouflage

00:35:32.318 --> 00:35:34.358
objects which they normally would not see.

00:35:35.218 --> 00:35:40.018
How does that work? Well, for example, you probably know this picture of this, uh.

00:35:41.849 --> 00:35:44.489
Dalmatian dog that's hidden behind a pattern of camouflage dots.

00:35:44.489 --> 00:35:46.389
It's a dog I never see. You never see it, right?

00:35:47.089 --> 00:35:52.529
But once someone traces the outline for you and you see that same image again, it pops out.

00:35:52.589 --> 00:35:55.369
You see the Dalmatian every time. I always say no, but it's true.

00:35:56.509 --> 00:35:59.609
Well, bees seem to have that property too.

00:35:59.709 --> 00:36:02.989
So if you can give them a hint, initially when you show them two camouflaged

00:36:02.989 --> 00:36:06.169
objects and try to train them to distinguish between them, they don't seem to do it.

00:36:06.389 --> 00:36:09.789
But if you give them a hint by showing them un-camouflaged versions of the same

00:36:09.789 --> 00:36:13.969
two objects, and you train them to discriminate those two, and then you show

00:36:13.969 --> 00:36:16.489
them the camouflage objects, and they can pick them out.

00:36:16.729 --> 00:36:19.869
And not only that, once they've learned how to break the camouflage,

00:36:20.189 --> 00:36:23.269
you can give them novel camouflage objects.

00:36:23.489 --> 00:36:27.549
And without the pre-training, they will do the task, learn the task.

00:36:27.849 --> 00:36:32.609
So you really taught them a different way in which to see the world.

00:36:32.769 --> 00:36:34.249
That's pretty impressive. Which is not bad.

00:36:34.469 --> 00:36:38.529
Do you think all these skills have given the bees an evolutionary advantage? advantage.

00:36:39.189 --> 00:36:42.529
Are there more bees than would be there otherwise?

00:36:44.449 --> 00:36:48.469
I wouldn't be surprised. I mean, I don't know to what extent it depends on how

00:36:48.469 --> 00:36:54.169
much predation there is and how many creatures are out there trying to eat these creatures.

00:36:54.689 --> 00:37:01.529
But that certainly is the fact that they can sting has certainly kept them alive for quite a while.

00:37:01.549 --> 00:37:05.109
And as you know, there's also these wonderful bee mimics.

00:37:05.269 --> 00:37:09.009
There are lots of insects which don't sting, which have evolved to mimic the

00:37:09.009 --> 00:37:11.869
bee because other birds will stay away from them because they look like a bee.

00:37:12.649 --> 00:37:16.789
They taste beautiful, but the birds just avoid them because they've been conditioned

00:37:16.789 --> 00:37:20.329
to avoid any bee. But that's more the sting, though.

00:37:20.629 --> 00:37:23.909
That's the sting. How are their cognitive abilities helping them?

00:37:24.829 --> 00:37:30.489
I think all I can say is it certainly helps them become more efficient foragers.

00:37:30.749 --> 00:37:35.109
I mean, this is where recent beautiful work that was done, not in our lab,

00:37:35.169 --> 00:37:41.849
but in the lab of a chap called James Neer, and he discovered that when a bee comes back and.

00:37:42.884 --> 00:37:46.784
Dances and to advertise a food source and another bee is watching this dance

00:37:46.784 --> 00:37:51.464
and it has been to that food source and has had trouble it's been attacked by

00:37:51.464 --> 00:37:56.824
a spider for example this bee will then head butt this dancing bee and stop

00:37:56.824 --> 00:38:01.984
it from dancing because it's a dangerous food source and this

00:38:02.264 --> 00:38:09.304
stopping is very target specific so it's it stops this dance only when the bee

00:38:09.304 --> 00:38:12.344
is advertising adding that particular food, so nothing else.

00:38:12.504 --> 00:38:17.604
And also, only if this bee has come back badly damaged.

00:38:17.904 --> 00:38:22.184
If this bee has had a fight with a spider and it had actually won, no problem.

00:38:22.984 --> 00:38:28.004
Okay, right. So I think that's getting to the point where these creatures are,

00:38:28.104 --> 00:38:29.264
I would say, almost human.

00:38:30.264 --> 00:38:35.444
That's pretty impressive. I never stung a spider though, but there's a work on that.

00:38:35.444 --> 00:38:40.904
But the thing is that the memory of these bees that would be providing,

00:38:40.984 --> 00:38:46.484
let's say, the core infrastructure for these cognitive capabilities seems to

00:38:46.484 --> 00:38:50.224
have, let's say, varying time windows in which it operates and is stable.

00:38:50.904 --> 00:38:55.424
So how many, let's say, would the distinction between the short and long term

00:38:55.424 --> 00:38:58.604
memory be sufficient to describe bee memory or do you see more stages?

00:38:58.724 --> 00:38:59.844
Well, there's also working memory.

00:39:00.024 --> 00:39:03.044
So this delayed master sample is a kind of working memory, right?

00:39:03.064 --> 00:39:04.884
And that lasts about five seconds.

00:39:05.444 --> 00:39:08.784
And there is the short-term memory, people say, which lasts about an hour.

00:39:09.324 --> 00:39:13.084
And then beyond that, it gets put into long-term memory.

00:39:13.604 --> 00:39:17.744
Now, exactly where the short-term memory resides, is it in the mushroom bodies

00:39:17.744 --> 00:39:21.204
or is it somewhere else? Right. No one really knows. I mean, the...

00:39:22.378 --> 00:39:26.438
It's very sad. I mean, the main problem, I think, is, as usual,

00:39:26.518 --> 00:39:29.558
the funding for insect work is not as good as it is with vertebrates.

00:39:29.738 --> 00:39:33.018
So, really, the physiology and even the anatomy. Well, the anatomy,

00:39:33.138 --> 00:39:37.338
thanks to people like Nick, is really doing very well. But the physiology is really suffering.

00:39:37.798 --> 00:39:41.458
But now about the stability of this memory. For instance, if the bee comes back

00:39:41.458 --> 00:39:45.138
and it dances, does that compromise the memory of that location,

00:39:45.318 --> 00:39:47.838
you think, in any way? Good point.

00:39:49.178 --> 00:39:51.978
A good point. I always looked at that. I mean, why should it?

00:39:52.798 --> 00:39:56.038
Well, it has to recall it, right? So the memory might become instable because

00:39:56.038 --> 00:39:57.898
it has to be recalled, replayed in some form.

00:39:57.898 --> 00:40:00.598
So you're saying every time you recall, you might lose the memory trace?

00:40:00.598 --> 00:40:04.478
Well, there are some theories of memory that go in that direction,

00:40:04.558 --> 00:40:08.958
right? Because it means recall would mean you have to make the memory again

00:40:08.958 --> 00:40:12.038
accessible and you pay a price for that. Oh, yeah, that's a good point.

00:40:13.438 --> 00:40:16.418
I don't know. I wouldn't be able to answer that question. But as you probably

00:40:16.418 --> 00:40:21.918
know, the dance is done only after the bee has wilted the foot several times.

00:40:22.378 --> 00:40:26.498
And also, by then, this particular experienced bee will not even be relying

00:40:26.498 --> 00:40:30.538
on its own dance information to get to the food source.

00:40:30.778 --> 00:40:34.338
It'll be using the sequence of landmarks and things that it's learned to go along the way.

00:40:34.598 --> 00:40:41.318
Right. So, that's why, although on a cloudy day, when the sun is covered and

00:40:41.318 --> 00:40:45.098
the whole sky is cloudy and there's no polarized light or sun, there's no dancing.

00:40:45.278 --> 00:40:49.158
The bees don't dance. But the bees that already know the food source will continue

00:40:49.158 --> 00:40:51.718
to forage because they don't need that information anymore.

00:40:52.378 --> 00:40:56.178
I mean, it's like you and I, you know, we know a familiar place like this beach

00:40:56.178 --> 00:40:58.318
that we're going to. I don't know it, but you know it.

00:40:58.498 --> 00:41:01.918
But I would be using vector information because you've given me that vector

00:41:01.918 --> 00:41:02.758
information, but you won't be

00:41:02.758 --> 00:41:04.778
using that. We're just using a sequence of landmarks, right? That's right.

00:41:05.018 --> 00:41:07.138
That's how these bees do it. The experienced bees do it that way.

00:41:07.218 --> 00:41:10.618
So the information hidden in the dance is really very crude. Yes.

00:41:10.778 --> 00:41:12.978
Crude information about the environment. And it's kind of dispensed with,

00:41:13.058 --> 00:41:16.798
you know, once the bee has advertised the food source and enough recruits have

00:41:16.798 --> 00:41:20.418
been collected, they basically forget about it. The dance is not done anymore.

00:41:20.678 --> 00:41:24.778
Okay. But when a new food source comes up, then, of course, the dancing starts again.

00:41:25.298 --> 00:41:30.058
Okay. But so now another part of your work, which might also be sort of have

00:41:30.058 --> 00:41:33.018
its roots in your engineering background, is you have been mapping a lot of

00:41:33.018 --> 00:41:38.278
this understanding of insect vision and behavior onto machines, onto robots.

00:41:38.758 --> 00:41:43.058
Yeah, that again, you know, that's also something that we didn't think of about ourselves.

00:41:43.058 --> 00:41:47.938
And it sounds a bit stupid because, you know, this first thing that we published

00:41:47.938 --> 00:41:53.318
on bees navigating down corridors, we didn't even think of it as anything that

00:41:53.318 --> 00:41:54.598
had potential engineering applications.

00:41:54.638 --> 00:41:58.018
But then after that work got published, a number of labs started to build robots

00:41:58.018 --> 00:42:00.958
that navigated down corridors using the same principle.

00:42:01.258 --> 00:42:04.638
And so we were actually sort of rather, you know, latecomers to this thing.

00:42:05.138 --> 00:42:08.818
And in fact, I wasn't even really pursuing that a lot until...

00:42:10.459 --> 00:42:14.779
A few US-based military funding agencies kind of just tapped us on the shoulder

00:42:14.779 --> 00:42:18.899
and said, hey, you know, would you like to work on this? And here's some funding.

00:42:19.219 --> 00:42:21.399
So that's how we got into it, really, ourselves.

00:42:21.919 --> 00:42:26.879
But now tell me, so to what extent have these principles really generalized successfully?

00:42:27.299 --> 00:42:30.999
What can you really achieve? How close is it to what you see in these insects and so on?

00:42:31.139 --> 00:42:36.619
Yeah, so as I was saying briefly the other day, so we're not really doing what

00:42:36.619 --> 00:42:38.279
you would call as biomimesis.

00:42:38.999 --> 00:42:41.339
So we're totally not building a compound eye.

00:42:43.559 --> 00:42:47.779
I mean, it's probably a good idea to do that. If you have the expertise and

00:42:47.779 --> 00:42:50.259
the technology, you'll probably learn something nice.

00:42:50.439 --> 00:42:54.699
But our idea is to implement the principle. So instead of using a compound eye,

00:42:54.839 --> 00:42:58.799
we started out by using just a single camera, off-the-shelf camera,

00:42:58.979 --> 00:43:01.479
but building a specially shaped reflecting surface.

00:43:02.419 --> 00:43:06.879
Just a mirror, but a specially shaped mirror.

00:43:06.879 --> 00:43:10.979
So you can do, you can play, we like to do things with mirrors. We're fond of mirrors.

00:43:11.359 --> 00:43:14.459
So with a mirror, for example, you can have either a spherical mirror.

00:43:14.719 --> 00:43:21.179
The trouble with a spherical mirror is that the radial gain is not constant.

00:43:21.319 --> 00:43:25.219
So what happens is if you look at the world with a hemispherical mirror,

00:43:25.419 --> 00:43:31.319
then the central part is magnified and the peripheral part is compressed, right?

00:43:31.359 --> 00:43:34.659
So you don't have uniform gain, elevational gain, as you might say.

00:43:34.659 --> 00:43:38.359
So we tailor the shape that produces uniform elevational gain,

00:43:38.439 --> 00:43:41.279
which is kind of useful because then you don't lose resolution.

00:43:41.619 --> 00:43:45.039
You make, what do you say, optimum use of the resolution, no matter where you're

00:43:45.039 --> 00:43:49.019
looking, right? So we use that on our aircraft with a standard camera.

00:43:49.159 --> 00:43:51.699
And that functions almost as well as a compound dye.

00:43:52.199 --> 00:43:56.399
There are a few blind zones, of course, like directly behind the camera you

00:43:56.399 --> 00:44:00.419
can't see, and then behind the mirror you can't see, but you've got a good field of view there.

00:44:00.639 --> 00:44:05.739
So that's what we do. So we implement compound panoramic vision in that way.

00:44:06.079 --> 00:44:10.579
And we don't build a flapping wing vehicle because that's too hard and we're not experts.

00:44:10.699 --> 00:44:15.279
We leave that to people like Mike Dickinson and so on. So we just build a vision

00:44:15.279 --> 00:44:17.799
system that uses some of the.

00:44:19.281 --> 00:44:22.281
It's a global principle that we discover from insectivision.

00:44:22.401 --> 00:44:25.701
So, for example, the finding that you need to measure optic flow in the two

00:44:25.701 --> 00:44:30.601
eyes and balance them in order to fly down the middle of a corridor.

00:44:31.081 --> 00:44:36.021
But the actual computation of the optic flow, again, we don't do it using the

00:44:36.021 --> 00:44:40.261
biological algorithms because we find that the biological algorithms don't do the job for us. Why not?

00:44:40.501 --> 00:44:49.161
Because they don't signal velocity reliably. They confound image velocity with spatial frequency.

00:44:50.961 --> 00:44:54.741
They're not robust to changes in contrast. They're just a mesh.

00:44:55.021 --> 00:44:58.141
Well, just to clarify, though, when you say biological algorithm,

00:44:58.461 --> 00:45:03.621
you really have in mind the Reichardt model or models that people have made.

00:45:03.761 --> 00:45:07.461
People have made. You don't actually know what is going on inside the fly's

00:45:07.461 --> 00:45:08.301
brain. We don't. Yeah, exactly.

00:45:08.581 --> 00:45:12.241
The true biological algorithm obviously works, right?

00:45:12.381 --> 00:45:16.161
Yeah. The real biological algorithm actually works. It still hasn't been,

00:45:16.301 --> 00:45:19.421
yeah, we don't know exactly what's happening in terms of the nervous system to generate that.

00:45:19.581 --> 00:45:24.681
But behaviorally, the animal behaves as though it is sensing velocity very, very robustly.

00:45:24.701 --> 00:45:29.081
Because what I found interesting, I mean, it sounded a bit normative what you were saying, right?

00:45:29.101 --> 00:45:33.101
Because in some sense, the biological algorithm as a biological algorithm must

00:45:33.101 --> 00:45:35.321
be incredibly precise and robust.

00:45:35.401 --> 00:45:38.921
Because, you know, it's computed in this really minuscule computational system.

00:45:39.101 --> 00:45:42.461
When I say biological algorithm, I mean I mean the known biological algorithm.

00:45:42.501 --> 00:45:47.261
Our interpretations are hypothesized. Hypothesized, or the recordings from neurons,

00:45:47.421 --> 00:45:48.661
the models of neurons that,

00:45:49.457 --> 00:45:53.877
that respond to motion, do not seem to do the job. So that's perhaps the limitation

00:45:53.877 --> 00:45:57.037
of the people who did the modeling rather than of the fly. Well, yeah, okay.

00:45:57.657 --> 00:46:00.717
In a way, they're trying to model the neuron's response, and they're probably

00:46:00.717 --> 00:46:02.457
right. But they have done a bad job.

00:46:02.617 --> 00:46:06.957
Well, this particular neuron may show that response, but maybe they're not looking at the right neuron.

00:46:07.157 --> 00:46:09.797
But there's something interesting here, Srini, that I think,

00:46:09.897 --> 00:46:13.237
of course, I understand you want to defend your colleagues, okay? That's all right.

00:46:13.517 --> 00:46:16.637
But the point is that, indeed, people have taken the physiology,

00:46:16.637 --> 00:46:19.197
given that some sort of functional interpretation.

00:46:20.097 --> 00:46:23.757
But if you apply it to your aircraft, it is exposed to just different conditions

00:46:23.757 --> 00:46:28.577
because you're not tying this airplane to a table and show it fixed stimuli.

00:46:28.977 --> 00:46:33.177
It's now flying around. And the input sampling, the dynamics of the stimuli

00:46:33.177 --> 00:46:34.657
has really changed completely.

00:46:34.997 --> 00:46:38.397
And that's where these algorithms, of course, collapse because they have been

00:46:38.397 --> 00:46:41.917
calibrated in highly controlled, rather artificial situations.

00:46:41.917 --> 00:46:46.277
So I think an interesting consequence of this observation is maybe that both

00:46:46.277 --> 00:46:50.757
the experimental context, the experimental paradigms have sort of been biasing

00:46:50.757 --> 00:46:52.357
our interpretation of what these systems do.

00:46:52.557 --> 00:46:57.237
And on top of that, possibly, our algorithmic function interpretation has just

00:46:57.237 --> 00:46:58.777
maybe been completely misguided.

00:46:59.057 --> 00:47:01.457
This is actually a consequence of your work.

00:47:02.217 --> 00:47:07.237
Yeah. I mean, the other way, I suppose, the way we're doing it presently is

00:47:07.237 --> 00:47:08.917
to put on a different hat completely.

00:47:09.157 --> 00:47:13.617
So when I say I want to measure optic flow, I simply, you know,

00:47:13.617 --> 00:47:18.897
We've developed a whole bunch of just machine vision-oriented algorithms, which work well.

00:47:19.057 --> 00:47:22.277
I mean, they give you the right answer. They've probably got nothing to do with the biology.

00:47:22.697 --> 00:47:30.077
So that's how we do it. But in the future, maybe there's a learning method or a...

00:47:31.075 --> 00:47:35.535
I don't know, genetically, what's the word, genetic algorithm-based approach,

00:47:35.715 --> 00:47:40.675
which will give us something that produces a circuit that measures velocity accurately.

00:47:41.155 --> 00:47:44.295
Because there's another aspect to this, right? That in some sense,

00:47:44.395 --> 00:47:48.695
what you're also using is computational hardware that has certain capabilities.

00:47:48.815 --> 00:47:53.455
Yeah. And you're exploiting, let's say, algorithms people have developed using this kind of hardware.

00:47:53.675 --> 00:47:58.335
But biological hardware might have to optimize different parameters.

00:47:58.515 --> 00:48:02.275
Exactly. Than the engineered hardware, because in the case of the bee,

00:48:02.355 --> 00:48:05.535
it must be flyable, it must be very compact, it must be energy efficient,

00:48:05.755 --> 00:48:08.115
and so on. It's optimizing so many different criteria.

00:48:08.215 --> 00:48:12.075
Right. I agree completely, and it's not necessarily tuned to exactly what we want.

00:48:12.355 --> 00:48:17.195
So, therefore, if you talk about biomimetics, I think it might also be a good

00:48:17.195 --> 00:48:20.935
way to actually benchmark and validate our understanding of the real biological system.

00:48:21.495 --> 00:48:25.135
I like to call it bioprincipics rather than biomimetics. So,

00:48:25.135 --> 00:48:26.415
you abstract the principles.

00:48:26.775 --> 00:48:30.695
You don't lavishly copy the biology. Just abstract the higher order principles

00:48:30.695 --> 00:48:32.635
and try and implement them, right? Sure.

00:48:33.255 --> 00:48:38.615
And then test our ideas that way about whether it's using this principle or not. Absolutely.

00:48:38.855 --> 00:48:45.495
But earlier you said yourself that's not really what you do with your airplanes.

00:48:45.715 --> 00:48:50.395
With the aircraft, no, what we do is we want to see, okay, we know the fact

00:48:50.395 --> 00:48:53.055
that insect is now using optic flow to control its landing.

00:48:53.375 --> 00:48:57.275
Can we get an aircraft to use optic flow again to control its landing?

00:48:57.275 --> 00:48:59.655
Representing the exact way in which optic flow is computed.

00:48:59.855 --> 00:49:04.175
We don't know in an insect, but we'll use our own way. We'll use our own engineering-based way.

00:49:04.775 --> 00:49:09.215
And that works. And that works. But this is funny because in some sense,

00:49:09.275 --> 00:49:13.815
in the literature, people would make you believe that they do know how flies

00:49:13.815 --> 00:49:16.775
or flying insects compute motion. They say it's the Reichert Correlator.

00:49:16.915 --> 00:49:18.715
So why do you say we don't know how it's computed?

00:49:19.075 --> 00:49:20.995
Because it doesn't fit the behavioral data.

00:49:21.375 --> 00:49:25.215
Okay, tell me. See, the thing with the Reichert Correlator, The only thing it

00:49:25.215 --> 00:49:29.915
can reliably tell you, really, if it comes down to it, is the direction in which something is moving.

00:49:30.535 --> 00:49:34.175
Beyond that, the information is very ambiguous.

00:49:34.675 --> 00:49:39.615
So if you want to use it as a bang-bang controller to control the direction,

00:49:39.835 --> 00:49:41.555
keep flying forward, right?

00:49:41.635 --> 00:49:45.135
So if the world moves to the right, it means you veered off to the left,

00:49:45.195 --> 00:49:48.435
and you generate a compensator. You're off to a turn back to the right.

00:49:48.515 --> 00:49:51.635
For doing that kind of bang-bang control, it's great. It's very reliable.

00:49:52.195 --> 00:49:55.235
But when you're turning to the right, you want to know how rapidly you're turning.

00:49:55.575 --> 00:49:59.075
It did not give you a reliable answer. And then there's another thing that always

00:49:59.075 --> 00:50:02.095
worried me about the Reichardt Correlator is that in some sense,

00:50:02.215 --> 00:50:04.095
it tells us that neurons can multiply.

00:50:05.215 --> 00:50:08.955
And biophysically, I find it always difficult to comprehend how you could do that.

00:50:08.995 --> 00:50:12.435
Well, actually, we had a cute idea that I published as part of my PhD thesis

00:50:12.435 --> 00:50:14.675
a long time ago, which I don't think anyone's really picked it.

00:50:14.735 --> 00:50:15.535
You could do it very easily.

00:50:15.675 --> 00:50:17.895
Very easily. Okay, just a quick...

00:50:18.490 --> 00:50:22.810
A coincidence detector, a neuron, and two trains of random spike trains coming

00:50:22.810 --> 00:50:26.510
in, two different frequencies, right? They're random, they jitter.

00:50:26.990 --> 00:50:31.670
So the probability of a coincidence is proportional to F1, frequency 1, and frequency of F2.

00:50:31.790 --> 00:50:35.890
So the spike rate at the output will be proportional to the spike rates in the

00:50:35.890 --> 00:50:38.030
input, if you assume randomness. Right.

00:50:38.370 --> 00:50:42.070
If there's no randomness, if they're periodic, then that doesn't work because

00:50:42.070 --> 00:50:45.050
you could have either perfect, you know, synchrony or no synchrony.

00:50:45.090 --> 00:50:47.630
So certainly evidence for this kind of... But the moment you have noise,

00:50:47.630 --> 00:50:52.090
and noise is actually helpful in this case, you will get a beautiful product.

00:50:52.370 --> 00:50:57.590
I don't know if anyone's found a neuron like that, but I'd love to see a neuron

00:50:57.590 --> 00:50:59.970
because we published that a long time ago.

00:51:00.170 --> 00:51:03.350
We also briefly discussed the possibility there are other sensors,

00:51:03.650 --> 00:51:07.070
like it is actually sensing drag in the air or something like that.

00:51:07.230 --> 00:51:08.710
Right, right, right. Possibly.

00:51:08.930 --> 00:51:11.190
Yeah, certainly. Combining the visual information with other sources.

00:51:11.510 --> 00:51:17.130
There are the antennae doing all kinds of things. as we were saying,

00:51:17.230 --> 00:51:18.710
they're probably tactile sensors as well.

00:51:19.770 --> 00:51:25.010
By the way, just as there's a beautiful rat whisker story we heard just now,

00:51:25.570 --> 00:51:30.290
bees look like when they come in close to a surface to land,

00:51:30.550 --> 00:51:33.650
when the surface is oriented nearly vertically, they're using their antenna

00:51:33.650 --> 00:51:35.110
to make the first mechanical contact.

00:51:36.970 --> 00:51:42.430
And also, it seems like the antennae tend to be perpendicular to the surface.

00:51:42.470 --> 00:51:45.590
As they're coming in and hovering, they have a perception of the surface slant,

00:51:46.550 --> 00:51:51.050
And you can even fool them by producing optical illusions which simulate different

00:51:51.050 --> 00:51:53.130
surface lands by having texture gradients.

00:51:53.310 --> 00:51:57.830
So obviously the eye is, the visual system is analyzing surface orientation.

00:51:58.230 --> 00:52:03.450
And maybe that's one where we could apply this model too. Mm-hmm. Yeah.

00:52:03.950 --> 00:52:08.110
Excellent. So then you haven't said much about the birds yet,

00:52:08.150 --> 00:52:09.790
and still I want to hear something about it.

00:52:09.830 --> 00:52:13.270
Oh, we're just starting, yeah. Okay. But at the level of intuition…,

00:52:14.241 --> 00:52:18.841
So now we talked about the bee, we've talked about this sort of bioprincipics

00:52:18.841 --> 00:52:21.801
and extraction of core design principles of the bee brain.

00:52:21.901 --> 00:52:26.481
Would you believe that you will also find some of these design principles back into the bird brain?

00:52:26.681 --> 00:52:29.361
At least so far, we found a couple of similarities.

00:52:30.281 --> 00:52:33.941
One again, and this is, again, we haven't tested a whole range of birds.

00:52:34.261 --> 00:52:38.961
But if you take one of the standard birds in Australia, it's called the budgeriga.

00:52:39.561 --> 00:52:42.801
Do you get budgies here? In the animal store, yeah. In the pet stores.

00:52:44.241 --> 00:52:47.861
There it's a kind of an iconic Australian bird, native bird.

00:52:48.081 --> 00:52:54.461
Anyway, there, if you fly them down a tunnel, they show very similar behavior to what the bees do.

00:52:54.661 --> 00:52:57.721
So we haven't been able to actually physically move patterns in these tunnels

00:52:57.721 --> 00:52:59.781
yet. We're starting to do that now with a long tunnel.

00:53:00.541 --> 00:53:03.861
But you can manipulate the optic flow by having static patterns,

00:53:03.981 --> 00:53:08.041
which are, for example, horizontal stripes on one side and vertical stripes on the other side.

00:53:08.401 --> 00:53:12.901
Then you imbalance the optic flow that way, and they behave in exactly the same way. Right.

00:53:13.201 --> 00:53:19.621
This is interesting in the context of our notion of convergent evolution and

00:53:19.621 --> 00:53:23.221
understanding engineering principles by looking at convergent evolution.

00:53:23.741 --> 00:53:28.361
Clearly, if the birds are using the same optic flow mechanisms as the bees are,

00:53:28.521 --> 00:53:32.761
it will have to be convergent simply because the common ancestor didn't fly.

00:53:33.061 --> 00:53:35.941
Yeah. The other thing that seems to be uniformly true in many species,

00:53:36.041 --> 00:53:40.121
including humans, is the fact that motion perception is largely colorblind.

00:53:40.901 --> 00:53:44.641
So humans, as you probably know, although we have beautiful color vision,

00:53:44.781 --> 00:53:46.901
the motion sensing system is almost colorblind.

00:53:46.981 --> 00:53:49.141
It's driven only by the luminance pathway, red plus green.

00:53:49.361 --> 00:53:53.081
If you look at the bee, again, it's colorblind. It's driven only by the green

00:53:53.081 --> 00:53:55.301
receptor, all of the motion sensing.

00:53:55.581 --> 00:53:58.861
And if you look at the spectral sensitivity function of the green receptor,

00:53:59.261 --> 00:54:04.781
it sits bang on the sum of, if you take the red plus green cones,

00:54:04.781 --> 00:54:07.721
into our luminous pathway, it sits exactly on top of that.

00:54:08.001 --> 00:54:12.761
So it's as though that system is adapted exactly to our environment, exactly the same thing.

00:54:12.861 --> 00:54:15.221
And now we're finding that even birds are like that. At least these buzzard

00:54:15.221 --> 00:54:19.401
guys are behaving as though their perception of motion is also colorblind,

00:54:19.501 --> 00:54:23.201
although these birds have even better color vision. They're tetrachromatic, right?

00:54:24.441 --> 00:54:28.241
So the system is going out of the way to make the motion detection colorblind.

00:54:28.241 --> 00:54:31.801
Exactly. Do we understand why? But wait, but there can be… Is that more efficient? Yes.

00:54:32.453 --> 00:54:37.533
I think the common notion is that the early creatures, and then again,

00:54:37.573 --> 00:54:41.653
correct me because I'm not an evolutionary biologist, is that most of the early

00:54:41.653 --> 00:54:43.253
vision systems did not have color.

00:54:43.453 --> 00:54:47.713
If you needed to have basic motion sensing just in order to move around.

00:54:48.973 --> 00:54:56.413
And not bump into objects and just navigate safely, and then later on when flowers

00:54:56.413 --> 00:55:00.353
evolved in the scene and it became important to recognize objects based on color,

00:55:00.433 --> 00:55:01.853
that's when color came in.

00:55:02.673 --> 00:55:06.393
So, many people, and this is still a hotly debated topic, but many people think,

00:55:06.453 --> 00:55:10.233
you know, color vision sort of co-evolved with bees and flowers at the same time, pretty much.

00:55:10.573 --> 00:55:13.293
So, there's no fundamental need to have color to begin with.

00:55:13.593 --> 00:55:17.273
But now, on this issue of convergent evolution, right?

00:55:17.353 --> 00:55:22.553
So, you could also argue that there can be, in the end, a common ancestor that

00:55:22.553 --> 00:55:25.913
just had to move and crawl, right, in a visual world.

00:55:26.693 --> 00:55:30.013
And that could have picked up these kinds of responses to motion.

00:55:30.253 --> 00:55:36.673
You don't need to fly to pick that up. So if you just have to speculate, how would you see this?

00:55:36.713 --> 00:55:40.253
Isn't it convergent evolution to a similar solution between flying insects and birds?

00:55:40.453 --> 00:55:44.813
Or just really a very ancient common ancestor just crawled around?

00:55:45.193 --> 00:55:47.033
I would say a very ancient common ancestor is my guess, yeah.

00:55:48.073 --> 00:55:51.893
Primitive things like photo taxes, going towards a bright light source is probably

00:55:51.893 --> 00:55:52.833
a very fundamental thing.

00:55:53.253 --> 00:55:56.373
Going towards something that smells good is probably a good thing.

00:55:57.513 --> 00:56:01.993
But on the other hand though I mean hummingbirds and bees fly in the same way

00:56:01.993 --> 00:56:07.433
and the common ancestors didn't fly so not everything is convergent we have

00:56:07.433 --> 00:56:10.653
to be a little careful about that in terms of optic flow,

00:56:11.493 --> 00:56:18.173
do you think that it is indeed in the common ancestors are the parameter,

00:56:19.073 --> 00:56:24.833
regimes right even though it had to sense motion do the birds and the bees have

00:56:24.833 --> 00:56:29.393
a a more refined sense of the speed.

00:56:29.893 --> 00:56:31.253
Than, for example?

00:56:31.793 --> 00:56:34.873
Some crawling primitive animal, let's say.

00:56:35.853 --> 00:56:38.913
Oh yeah, it would have a more refined sense. Like a snail, for instance,

00:56:39.133 --> 00:56:42.453
you know? Yeah. It will respond to moving visual stimuli.

00:56:42.653 --> 00:56:44.953
See, my guess is that...

00:56:45.733 --> 00:56:52.273
Do flying creatures have to have more precise flow detection?

00:56:52.733 --> 00:56:55.873
Oh, sure, yeah. Yeah, they need to have also, depending on the speed of flight,

00:56:56.873 --> 00:56:59.953
it seems like certainly the nervous system is very different,

00:56:59.993 --> 00:57:01.413
right? The dynamic properties are very different.

00:57:01.533 --> 00:57:05.513
Right from the photoreceptors, you have a much higher flicker fusion frequency

00:57:05.513 --> 00:57:08.393
if you've got a fast-flying creature.

00:57:09.473 --> 00:57:12.873
And I would say even the motion-sensing units are tuned to their speed.

00:57:13.153 --> 00:57:18.313
So I would say that a thing like a primitive worm would probably have, again, the same kind of,

00:57:18.986 --> 00:57:20.946
Same kind of basic structure for motion

00:57:20.946 --> 00:57:23.446
detection, because it's not very complicated when you think about it.

00:57:23.486 --> 00:57:25.266
If you just want to tell which direction something is moving,

00:57:25.606 --> 00:57:30.066
you just need to have one inhibitory synapse, right, between one photoreceptor and another one.

00:57:30.266 --> 00:57:34.266
And that could have evolved as early as lateral inhibition, you know? Exactly.

00:57:34.426 --> 00:57:38.146
And so you just modify that a little bit. Well, actually, for any sensor,

00:57:38.206 --> 00:57:39.486
it is sort of the endocannulas.

00:57:40.126 --> 00:57:42.986
But you could even go to other modalities. If you go to some mechanical sensing

00:57:42.986 --> 00:57:46.266
or chemical sensing, all you want to do is measure a difference.

00:57:46.806 --> 00:57:50.326
That's what this is. What is then generalized to some optic sensor.

00:57:50.446 --> 00:57:51.306
You just measure a difference.

00:57:51.626 --> 00:57:56.886
But as you were also pointing out, the Rijkaard detector, which does that, is not adequate.

00:57:57.246 --> 00:57:59.866
It's not adequate. So therefore, there is something more. Sure.

00:57:59.866 --> 00:58:01.426
Yes, there is something in common.

00:58:01.826 --> 00:58:07.206
But there's more. Yes. And that is really necessary for this behavior.

00:58:07.346 --> 00:58:12.486
And that's where maybe you even have parallel pathways. My guess is that,

00:58:12.566 --> 00:58:15.526
at least in the B, there must be several parallel pathways that are sensing

00:58:15.526 --> 00:58:17.146
different aspects of motion.

00:58:17.546 --> 00:58:20.886
For example, this optical avoidance and flying down the middle of a corridor,

00:58:21.206 --> 00:58:25.366
that motion sensing seems to be non-directional. It's not directional at all.

00:58:25.846 --> 00:58:29.426
So if you think about it, if you want to avoid an obstacle, you don't really

00:58:29.426 --> 00:58:33.726
care which way the thing is moving. You just want to avoid it because of its high speed, right?

00:58:33.946 --> 00:58:37.006
So maybe it's computationally simpler to just do some non-directional motion

00:58:37.006 --> 00:58:39.646
sensing rather than compute motion direction.

00:58:39.946 --> 00:58:43.666
It's just not necessary. And this thing has completely different properties.

00:58:44.266 --> 00:58:47.926
You know, I think things are changing now. I probably shouldn't say this,

00:58:48.046 --> 00:58:54.386
but as long as Reichardt was alive, there was a big school of tubing and following the Reichardt model.

00:58:54.586 --> 00:58:58.046
But I think now things are starting to unravel.

00:59:00.126 --> 00:59:05.606
Okay. But so to finish up, there are two questions that we actually pose to

00:59:05.606 --> 00:59:07.346
everybody we interview for the podcast.

00:59:07.666 --> 00:59:11.606
So there But here you come out of engineering, discover the bee,

00:59:11.706 --> 00:59:17.866
do all this fantastic work, have it fly now on these airplanes, move to the bird.

00:59:18.698 --> 00:59:23.058
So, you've been exposed to these different disciplines, trying to extract these

00:59:23.058 --> 00:59:25.198
engineering principles of biological systems.

00:59:25.458 --> 00:59:29.138
So, if we would like to follow in that tradition, what's the law of Srini we

00:59:29.138 --> 00:59:32.838
should adhere to? What's Srini's law? Srini's law? Yeah.

00:59:33.938 --> 00:59:38.958
Follow your heart. Don't worry about where your next job is going to be.

00:59:43.078 --> 00:59:46.038
No, just follow your heart. And if you enjoy what you're doing,

00:59:46.218 --> 00:59:47.898
things will come to you naturally.

00:59:48.698 --> 00:59:52.538
Do you want me to say briefly what my next thing, I don't know if I will ever

00:59:52.538 --> 00:59:57.478
get around to doing this, but what my next, I would love to be able to look

00:59:57.478 --> 01:00:01.058
at higher emotions in simple nervous systems.

01:00:02.658 --> 01:00:11.418
Things like, for example, joy, disappointment, fear, anger, pain.

01:00:13.518 --> 01:00:17.198
I think there's a whole thing there, and I believe there's an entire continuum

01:00:17.198 --> 01:00:21.158
and there's no such thing as, you know, I find it hard to believe that invertebrates,

01:00:21.158 --> 01:00:24.658
for example, don't feel pain, whereas vertebrates do. They know hard and fast.

01:00:24.878 --> 01:00:26.738
So would you speculate then that

01:00:26.738 --> 01:00:30.798
these animals would qualify for a very primitive form of consciousness?

01:00:31.178 --> 01:00:35.358
I would think so. I would think so. Especially, you know, that headbutting that I mentioned to you.

01:00:36.078 --> 01:00:40.778
That's, you know, I find it hard to believe that's all done in a purely a reflexive

01:00:40.778 --> 01:00:44.858
way, especially because it's so subtle, right?

01:00:45.038 --> 01:00:47.278
Well, headbutting is not that subtle, I have to say.

01:00:47.538 --> 01:00:50.938
No, but the things that control when it headbutts and doesn't headbutt,

01:00:50.978 --> 01:00:52.898
it's very precise, right?

01:00:52.958 --> 01:00:56.018
I mean, the fact that it's exactly that food source is being signaled,

01:00:56.098 --> 01:00:59.398
and also on how dangerous the predator there is.

01:00:59.498 --> 01:01:03.738
Right. I mean, you can't say this is all being done by some stupid automaton, right?

01:01:03.778 --> 01:01:08.158
Well, I think to any of us who've actually watched flies and things closely

01:01:08.158 --> 01:01:11.798
behaviorally, there's no doubt in our minds that they are constants.

01:01:11.918 --> 01:01:16.038
There's a lot going on, you know. For example, you jab a cat or a dog and it

01:01:16.038 --> 01:01:17.398
flinches and you say it feels pain.

01:01:17.658 --> 01:01:20.498
And you jab an insect and it again does the same kind of reaction,

01:01:20.618 --> 01:01:25.198
but you say oh, it can't be pain because it's an insect. It must just be a reflex.

01:01:25.458 --> 01:01:29.258
How do you know, right? This is a great topic for another podcast interview

01:01:29.258 --> 01:01:31.438
that we're definitely going to have in the future. Sorry, I didn't want to launch

01:01:31.438 --> 01:01:33.018
you off on this. No, no, this is good.

01:01:33.178 --> 01:01:38.238
But then the final question for me is really, so since Since Partha is so successful

01:01:38.238 --> 01:01:41.658
and generates all this money, we can travel around the world on his budget,

01:01:41.758 --> 01:01:42.898
you know, without any trouble.

01:01:42.998 --> 01:01:45.858
So five years from now, we're going to go visit your lab and we're going to

01:01:45.858 --> 01:01:50.278
remind you of a hypothesis that you generate today that you feel most passionate

01:01:50.278 --> 01:01:54.578
about and that you claim will come out five years from now. So what's that hypothesis?

01:01:55.838 --> 01:02:00.918
Oh, my hypothesis will be that insects feel pain. Right.

01:02:02.062 --> 01:02:05.342
You're really going to work on that? Oh, look, if I get the funding,

01:02:05.522 --> 01:02:07.282
if I get the funding, I'd love to.

01:02:07.502 --> 01:02:10.062
Okay. I've got some ways in which they can be tested, I think,

01:02:10.082 --> 01:02:14.642
which will be a little more telling than just jabbing the insect and saying

01:02:14.642 --> 01:02:18.002
it's a reflexive thing because that doesn't convince people.

01:02:18.122 --> 01:02:20.002
It convinces me, but it doesn't convince other people.

01:02:20.142 --> 01:02:23.322
But I think there must be ways. You can never make a conclusive proof.

01:02:23.622 --> 01:02:27.282
But again, as for example, now that they convinced that fish feel pain,

01:02:27.382 --> 01:02:31.182
for example, do you know how they finally did it? No. In fact, it involved a bee.

01:02:32.282 --> 01:02:37.042
They took a bee sting. They took a sting out of a bee and stung the lip of the

01:02:37.042 --> 01:02:38.982
fish and the fish twitched.

01:02:39.382 --> 01:02:42.702
And they said, aha, the fish feels pain. It seems like a very primitive,

01:02:42.862 --> 01:02:43.842
unsophisticated experiment.

01:02:44.362 --> 01:02:47.842
But the world was at the right time to accept that at that point.

01:02:47.862 --> 01:02:49.702
And they said, aha, fish feel pain.

01:02:49.822 --> 01:02:52.902
And now from now on, we've got guidelines for experimenting with fish.

01:02:53.122 --> 01:02:56.842
So that means now we have to slap a bee with a fish and see if it twitches.

01:02:57.682 --> 01:03:02.442
I think that's what you're proposing now. Yeah, the fish will probably twitch, right?

01:03:03.542 --> 01:03:07.922
Because the bee will have stung the fish. And the bee doesn't feel the pain, obviously.

01:03:10.522 --> 01:03:14.042
You proved two things there. The bee doesn't feel pain and the fish feels pain.

01:03:16.302 --> 01:03:20.402
I'm so happy we resolved that issue. Srini, thank you very much for this wonderful

01:03:20.402 --> 01:03:22.002
interview. Thank you. Thank you, Paul.

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Thank you. The CSN Podcast was produced by the Convergent Science Network of

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Biometics and Biohybrid Systems,

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a project funded by the European 7th Research Framework Programme.

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Music.