This is the Convergent Science Network podcast. Leading researchers in the domain
of neuroscience, brain theory and technology are interviewed by Paul Verschure and Tony Prescott.
So this is Paul Verschure with Parta Mitra with the Convergent Science Network podcast.
And in this episode, recorded as part of the CSN Barcelona Cognition,
Brain and Technology Summer School, we're talking to Majiem Srinivasan, also known as Srini.
And Srini, in your lecture, you focused very much on the mental life of bees,
if you want, with a special focus on memory.
Well, part of it, yeah. The first part was more on low-level vision and navigation.
Right. And the second part was really more on the cognitive bit.
So we can start with the first part or the second part, whatever you prefer.
Good. Well, before we do that, if we think about, let's say,
the vision or the cognition in bees, could you give us an intuitive description
of the world in which a bee lives? Yes.
Well, I suppose it's okay. I mean, it probably depends on whether it's flying
outdoors or staying indoors in the hive.
Outdoors, I suppose the world is very similar to what we experience.
I mean, it's mostly visually oriented or visually dominated,
I would say, with a bit of olfaction thrown into it.
When you get close to a flower, you smell it and you either go towards it because
you recognize the smell or you avoid it because the nectar is not good over there.
But within the hive, it's of course a completely different situation.
It's totally dark and there's a lot of pheromonal contact and there's a lot
of acoustic signaling going on.
And that's stuff that a lot of other people are studying. I don't know a lot
about that, but it's certainly a lot of, it's mostly acoustic,
I would say, inside the hive. Okay.
So, starting out then with the visual capabilities of bees, this would then
support their navigational skills.
Absolutely. So, how do you decompose vision in bees?
Okay. I mean, as you probably know, insects have these compound eyes,
which are many, many facets. facets, and we have the so-called simple eyes, camera lens type eyes.
And so optically, superficially, there's a difference between insect vision
and our own vision in terms of just how the information, the visual information
is collected or sampled.
But the more fundamental difference, as I was saying in the talk,
is the two eyes of an insect.
When I say two eyes, I mean the right compound eye and the left compound eye.
They're very close together.
So it becomes very difficult to do stereo if you're an insect because the baseline is very small.
And the only way you can do stereo, the only circumstance under which you can
do stereo is when your object of interest is very close to your eyes,
which is when you get a much bigger disparity, of course.
So this is where it looks like insects have gone a different route.
And they... Sorry, am I distracting you? No, no, no, no. I'm distracting myself.
Go ahead. Go ahead. Which is where they...
They have to use a very active mode of sensing the world in three dimensions,
and this is done by physically moving in it and measuring the optic flow that
various objects around them generate.
Right. So something that's very close to you, if you're moving in a straight
line, something that's very close to you moves by very rapidly in your visual
field, and at high speed tells you that this is very close.
And something that's very far away, on the other hand, moves very slowly,
and that tells you that that object is at infinity.
And what we've done is a nice series of experiments maybe the first ones I suppose
with insects to show that insects really gauge distance based on how rapidly the images are moving.
So this is done with flying them through tunnels and moving the patterns on the walls as you know.
But now in insect vision which is a very active area of research,
there's if you want let's say a prototypical design of a vision system fairly
hierarchical where you slowly move to let's say a bit more complex and integrative,
processing of vision-derived signals, but in what sense does the B visual system
really deviate from that sort of standard template?
What are the unique features? Okay, what we're finding, I suppose the fly,
most of that work, the initial work, has been done with the fly.
And there, you know, the motion-setting system has been studied very well there.
But I think it's just one part of what goes on in an insect visual pathway.
I mean, there's lots of other things is happening. For example,
in bees, again, we're finding what seems to be almost certainly multiple parallel
pathways, so some of which function the way Reichardt described it,
with this correlation type motion detector.
But all of these other mechanisms that are used to sense range,
for example, they don't seem to obey the Reichardtian laws.
They seem to be more accurate measuring devices for measuring image velocity,
independently of the spatial frequency content.
And independently, largely of the contrast of the scene as well.
And if you think about it, you need that, because you want to know how far away
a surface is, regardless of what its texture is, spatial texture,
or what its contrast is, right?
So you need a robust system, and the Rijkaard system does not deliver that robustness to you.
So there must be other systems there which are acting in parallel,
or which are processing the, you know,
using several different Rijkaard detectors maybe, multiple channels to get that,
what do you call it, that vertical information on velocity. So that's one thing.
Apart from that, of course, honeybees have excellent color vision,
trachromatic color vision, which they need to detect flowers, recognize flowers.
And they're really a beautiful learning machine. You can train the bee to learn a color in half an hour.
Five rewards is enough for a bee to learn a color. So they do great with that.
And of course, they have a beautiful polarization sense as well,
which you probably know.
So to analyze the polarization pattern of the sky and use that as a compass.
If the sun is hidden behind a cloud, then they can't use the sun anymore,
but they can still use the polarization pattern of the sky to work out which direction to fly.
So all of that is there in the bee, which I think, well, color vision certainly
is not as well developed in the fly. and with polarization vision,
no one really knows for sure. Right, okay.
You had mentioned that when you had these artificial small tunnels set up,
you could fool the bee into thinking that it's flown a smaller distance.
A larger distance. Flown a larger distance.
So it's not entirely invariant to the environmental views. Exactly, exactly.
No, you're absolutely right. But what's neat about that is that,
as I was saying, and when someone asked the question.
So you would say, for example, if bees flew in different landscapes,
they would give you different distance readings, right?
Because the optic flow they've experienced could have been different.
Let's say you fly over a plane, over a lake, for example, where there's almost
no optic flow as opposed to going through a dense forest.
But what happens is that a bee that goes and finds a food source after it's
flown over a lake comes back and does a dance and it signals a certain amount
of units of optic flow, which is the measure of distance.
And then all the other bees take the same route.
So they experience the same environment. So whatever calibrations or miscalibrations
are happening, they're the same for all the bees, and so they cancel each other out, right?
So that's the way I think, that's why the system probably works.
But again, there's a little unsolved question there because different bees can
fly at different heights above the ground.
And so if you fly higher, you will experience a lower amount of optic flow,
so the integrated flow will be lower.
The question is then, do bees actually measure height and take that into account
when they're doing their odometry, or do they all fly at the same height?
We don't know that. There's some anecdotal observations which say bees fly typically
about two meters above the ground, but whether that's really the case,
no one really knows for sure.
But if you would have something like a polarized light.
Compass, if you want. You could always use that to recalibrate and make yourself,
your distance estimate independent of your altitude.
Well, the polarization compass will tell you only which direction you're flying
in. It won't tell you your height about the ground.
Then you could ignore it because you just instruct your fellow bees to fly in
a certain orientation with respect to that reference.
How do you know how far to go, though? The optic flow will depend on how far you are.
So you have to give you have two bits of information to specify a location in
a plan, right? You need the direction and you need the distance.
So you've got to specify the position of the food source in polar coordinates.
So the polarization pattern of the sun, they give you the compass direction,
so it tells you, okay, go this way.
Then you also have to tell them how far to go before you start looking for the food.
But you could also argue that the
more minimal model will just take the orientation until you hit a target.
Yeah, you could, but you see, what is also interesting and probably subtle is
that when these bees come back home and dance and they're advertising direction
as well as distance and they're also passing out these nectar samples to these other bees.
So quite often a bee will stop this dancing bee and beg it for nectar samples
and this other bee will, you know,
regurgitate some of the nectar that's brought out. And so this bee can actually
assess how good that nectar is.
And based on the distance signaled, it can decide whether it's really worthwhile
going all that way or not.
So these bees are evaluating dances that are being produced by different forages
coming back from different food sites, which are advertising different food
sources, and they're making up their minds.
So do I go a long way to get to a good food source? Or can I fly a shorter route
to get to a not-so-good food source?
There's a trade-off there. And apparently they're working out the trade-off
in their minds. So what kind of correlations did you find?
Between? Well, for instance, the animals that decide to go off on a long foraging run.
What's the difference in the nectar? So it seems like, so this is not our own
work, but other labs have investigated it.
And it looks like what these bees are doing is that they're looking at the ratio
of calories brought in in terms of energy from the sugar, nectar,
versus calories expended to get to the food source. And they're trying to maximize that.
But then still an alternative could also be that you sample the nectar to get
the chemical fingerprint of your target.
Yeah. And this could help in your navigation because then again,
you could go for some odor plume that matches that template to get to your target.
Sure. No, you could do that.
It's harder though because what happens is typically if you say,
okay, this baby comes back and gives you a scent of lavender, right?
So because you go looking out for lavender, you don't know which direction to
go. And remember, it's not just a simple diffusion process.
People in the old days used to think, you know, these scents just diffuse nicely
and you have a nice diffusion gradient.
And you just go up the gradient and you'll find the food source, right?
No, it's not like that. When the wind is blowing, the whole thing is very turbulent
and you get these little filaments of, you know, scent that are moving around.
And you've got to be lucky to hit one of those filaments. Sure.
And then you can zigzag, as you probably know, across the filament and get to
that. You can do it, but it's tedious.
Of course. Tedious. Sure. And there's some evidence that insects do,
when they're close to the goal, they do start looking for the scent.
And certainly they're using that information, too, to guide their final thing. Yeah.
But you've shown in your controlled studies that once you've put one of the
bees through your tunnel with a controlled distance,
it can signal distance to the bees, which then will fly and will skip sources
of food to go to the food source at the right distance.
They're definitely paying attention to the dance, no doubt about that.
I mean, I guess Paul's question was, yeah, why do they even need a dance?
But there's no doubt that they are using it. That is definitely the case.
I was just trying to see what's the minimal, what would be the minimal model?
The minimal thing would certainly be, yeah.
I mean, if you got the scent given to you at the hive and the nectar tasted
good, you could just go out looking for that.
Exactly. It'll take you a long time. It won't be very efficient.
And you get some vector, some heading vector, right? Some heading vector.
Okay, we get a heading vector. Okay.
Okay. Okay, but while they're going through all that trouble,
you might as well even provide the distance, right? I mean, it's one more parameter.
But remember, we were trying to solve the problem, how to deal with altitude,
right? So actually, it's interesting.
So this is another thing that came up in the discussion.
How does a dance actually evolve, right? And so people are not really sure,
but there's a lot of evidence that other insects will dance too,
completely out of context.
Like which ones? There's a species of butterfly, for example,
apparently, which is a solitary butterfly.
So it flies a certain distance, and then when it lands, it does a waggle dance.
It waggles its abdomen, just like the bee.
But without an audience. Without an audience, exactly. But no one really knows why.
But it's there. It's some kind
of epiphenomenon. So maybe it's something metabolic or some other thing.
And so maybe that has been picked up by these bees and being exploited as a signaling mechanism.
Has anyone measured the accuracy with which the distance is signaled?
So if different bees end up at different distances from the target or at different
angles, has anyone measured the spread?
So, yeah, typically the distance, the scatter and the distance,
the Weigel durations is about 10%. And so it's always a percentage of the mean distance.
But what is interesting is that with the angular error, as we were discussing
the other day, it looks like when the food source is close by,
there's a certain angular error in the direction indication.
But when the food source is very far away, the angular error is much smaller.
So, they're trying to account for the fact that if you have the same angular
error, that will signal a much bigger patch when you're further away compared
to when you're closer, right?
So, they're trying to compensate for that. There's some built-in compensation, I guess.
But this is interesting because then apparently this compensation comes at a cost.
Because when something is nearby, so it's easier to find, they seem to put in
less effort to communicate that parameter. Yes, yes. So, what's that cost exactly?
You're saying cost in terms of doing an accurate dance? Right, exactly.
It seems to me... So you're saying, why are they being sloppy when...
Yeah. So that's interesting.
Maybe, I don't know what the energetic requirements are for doing a dance,
and whether precision involves more concentration.
Are they really being more sloppy, or is it that when the waggle dance is longer,
longer the audience gets to integrate for a longer period of time and therefore
gets a more accurate estimate.
The actual variance itself, so people have measured physically the variance
in the axis orientation, and that seems to go down.
But is that a simple consequence of having a longer down?
It's possible. It's possible that it's a measurement. It's possible that even
with the humans who are measuring these dances, maybe there is an element of
increased accuracy simply because they have a longer sample,
right? That's a good point.
But that's only true when the Wegel dance is a highly controlled,
low noise expression of these parameters. Is that true? Yeah.
Yeah. So you mean how noisy is it? It's pretty noisy. It's not clean.
It's a longer integration. Yeah.
It wouldn't possibly help you to have a bit more accurate estimate.
And the extreme limiting case is when the foot was very close to the hive,
when, as you probably know, there's no longer even a waggle dance.
So the waggle disappears completely and it becomes just a round dance.
Right. And that simply says, okay, I'm not going to tell you exactly which direction to go.
Just look within a small radius, 50 meters of the hive, and you'll find it.
So it becomes very imprecise as you get very close to the origin.
But the two interesting aspects of this, I find them interesting right now,
is that you could also argue that on the one hand, it's the dancing bee itself
who has integrated more or less information.
Information like if you have to unwind this clock you
have been winding up the clock for a shorter period of time
when you found the nectar nearby so there's less to
display in in the waggle dance would that be an alternative interpretation that
would make sense yeah i mean the the the original people think
the way this whole dance first started was in the tropical
bees which were living not inside these
dark you know hollow chambers uh you
know nests that we find in the the european bees now but
outdoors and um you know and and there most of
these primitive bees now have dance on a horizontal surface
not on a vertical surface and they're doing a kind
of a scaled down version they're simulating the
flight to the hive so i mean to the food source this is just basically pointing
in the direction of the food and they're doing it coming back so it's exactly
as though they're doing us yeah yeah a miniature version of the actual flight
so in a sense that makes sense So they're coming back and they're sort of replaying that.
But that's interesting because that creates a new problem for the observer.
Because now the observer must extract this direction of movement independent of their own position.
Okay, in the...
In the outdoor arena, that's fairly easy, I think, because you basically have
to face in the direction that the bee is waggling, and then you know where the sun is.
So I say, okay, I'll fly in such a way that I keep the sun over there,
or the polarization pattern in the sky if the sun is not visible,
if there's some patch of clear sky.
Okay, I'll say, okay, I know the orientation of the vector should be that.
I'll hold that and go along, and I'll go for a distance that tells me,
you know, how many optical flow units I should experience.
And then when I get close to that, I'll start looking. Right.
But now if I take a bee and I observe the bee dance, but now I rotate it a few
times and then I let it go, will it still orient itself correctly?
Theoretically, it should. You're not in practice.
They're not all theoretical physicists. That's a good point.
That's a good point. I don't know if anyone's done that.
Because that's a bit the issue. That's very interesting. Because the way you
describe it is that you physically orient yourself. Okay. As far as people know,
no one really knows whether bees have a vestibular organ as yet.
If they do, of course, they could get messed up.
Absolutely right. Now, the way people think, they assess, they calibrate their
orientation is the dance is done on the vertical plane inside the hive.
And the direction of the sun is symbolized by the vertically upward direction,
which is the direction of negative gravity.
And people think they have a sense of gravity.
Looking at the way, for example, your abdomen hangs in relation to the thorax,
that tells you which direction gravity is, and that's your direction calibration.
That's what people think. But in addition to that, if they have something that
senses your angular velocity, as in a vestibular thing, no one really knows.
But then how do they map that back in the horizontal plane relative to the sun?
Because then gravity is not helping you. Yeah, yeah. It's very interesting. Completely unknown.
Okay. It's very interesting. Also, how do they hold this information, you know? Exactly.
There's the automatic information. They go some distance and it can be,
you know, several minutes before they come back home and dance, right?
So where is that information stored? Is it stored as a charge in,
you know, the membrane potential of some neuron? or is it stored as an activity
in some kind of place cell?
My own feeling is that really the mushroom bodies and the insects really are
like the hippocampus invertebrates and there might be place cells.
So there could be certain place cells. As you go along to a food source,
presumably successive place cells are lighting up.
Now that there is more bee genetics and molecular biology coming on,
are people thinking of manipulating bees to knock out specific genes.
Certainly, knocking out the dancing gene, for example.
Has that been done? No, no.
I'm sure they're trying to do that. Or are people breeding bees for certain traits?
Yeah, they've been doing that for a long time, even before the gene was mapped, of course.
I mean, basically the idea is to breed bees that will produce a lot of honey.
No, I meant more in the context of the waggle dance.
Bees that are better signalers? or worse. Interesting.
It would be very interesting. If you can find out a combination of genes that,
for example, are responsible for the dance, that's a very basic thing, right?
Could one not even screen these forward mutagenesis studies?
I'm sure that's being done. I'm sure that's being done.
I'm not a molecular biologist myself, but I'm sure there are people doing it.
There's Gene Robinson in the US who's been doing stuff along those lines.
I don't know if he'd actually succeeded yet, but that would be really exciting.
And we started discussing whether this was learned behavior or innate behavior.
You had some comments on that? Yeah, this is just a guess. I can't be sure.
But I think probably the basic dance is pre-programmed. It's there like a child
that has its gate already built in.
But it's probably learned and refined during the first few days.
The first three weeks I spent just not doing any foraging. As you know,
they're just acting as nurse bees inside the hive.
So there's no need to do any dancing. but it's only after that when they start to go and forage.
That they start to need to dance. And that's the point, by the way.
I don't know if Nick mentioned that, but when a bee first starts to go out and
forage, the mushroom bodies expand hugely.
So that map is being laid down and there's another piece of evidence that...
That's interesting. It's really like the London taxi drivers.
But are the mushroom bodies this very central structure in the bee brain? Yeah.
Expanding in some uniform fashion or is it certain lobes of it that expand more than others?
Oh, I don't know about the details. The calyces are the cup-shaped structures.
They enlarge hugely. And there's still a debate about whether the number of
neurons is actually going up or whether it's just the density of the neuropil.
So it could be that the dendrites are getting longer and developing more synapses.
There's a bit of a debate, I think, about whether the number of neurons,
whether new neurons are being created.
I don't know if that's still the case. But certainly all those calyces get very big.
So then the question becomes just a bit of shift and also towards memory, if you want, right?
Because you were talking about something like a place cell type response in these mushroom bodies.
So if we start with, let's say, the basic task these bees have to solve,
which is, okay, here I go. I'm navigating out.
I'm finding some nectar and flying back home. And now I'm going to tell everybody about it. Yeah.
So where's the memory of that? What's the memory of that? What are the physiological
correlates of it? Nobody has the foggiest idea.
That's a million-dollar thing. I mean, where is this information stored?
Again, my guess is that it'll have to be somewhere in the mushroom bodies.
It'll probably be some place cell that's firing that says, okay, this is the location.
And that has to be translated somehow into producing a dance.
Right. But let's approach it maybe different. Let's see, what do we know about
B-memory structures that could help us understand answering this question?
Unfortunately, almost nothing.
Well, we know a little bit about this. Yeah, all that people know is that if
you knock out the mushroom bodies, a lot of learned associations are lost.
But that's about it, really.
That's about it. There's a little bit that you were mentioning that certain
things, the bees are easier to train, certain tasks, the bees can be trained in easier.
Whereas other tasks are difficult. And you also had some time courses associated
with that. Yeah, so there's gradations of learning. So, for example,
the simplest kind of learning would be color learning.
It's just so fast and so robust. If a bee learns a color, we learned it,
as I said, in half an hour, five visits.
But that means the bee gets rewarded to find that color, and it will find it within five trials.
That's right. So the idea is, okay, do the reinforcement five times.
So the classic von Frisch experiment was to have trained them to come on a piece of blue paper.
Which had a drop of sugar water. And then, of course, he did a very nice...
He just didn't simply do two different colors because they could be discriminating
the colors not on the basis of color, but on the basis of intensity,
on the basis of brightness.
So what he did was he had a blue sheet that he rewarded the bees on.
And then so they came five times, got rewarded on that.
And then he gave them a test where this blue sheet, he took away the sugar water,
there was no food anymore. and the blue sheet was placed in the midst of,
or in the general vicinity of a bunch of other sheets of different gray levels.
And so then he said, okay, let this train bee come and choose where it wanted
to land. It picked the blue.
So regardless of the intensity, it was perceiving hue as a separate quality.
Right, exactly. And then landing on that thing. So that, again,
takes only five rewards.
And what's the generalization capability of the bee?
If I now put this blue patch of paper among many other different kinds of blues, Oh, yeah.
The delta-lambda discrimination is almost as good as that of a human.
It's about five nanometers.
So if you do experience the spectral lights, five nanometers.
They also have very nice color constancy, the way we do.
So they can perceive the shade of blue to be, well, they can recognize this
irrespective of the illumination, largely.
So evening versus midday, so that color constancy competition is going.
All animals need it, probably.
And bees have it too, of course, which is nice.
Well, that's an exciting issue as well, but maybe we should get back to that
later before we understand memory, or at least understand what we don't understand about it.
So I think the behavior leads a long way ahead of the physiology,
I think, and the circuit knowledge of what the circuits are doing.
Right, but then let's look at it from a performance perspective.
I mean, how many landmarks can I memorize as a bee? Okay.
It depends on how you train them.
I don't know if anyone has looked at that systematically, but certainly you
can, in terms of discrimination, you can train bees to distinguish between horizontal
versus vertical patterns.
You can train them to distinguish colors, as I mentioned.
You can train them to distinguish between odors, different scents,
and you can train them to associate odors with colors.
So you can have a bee come into a maze, it gets a whiff of scent,
and then you can train it to say, if you smell lavender, then you go to a decision
chamber, you have to pick the blue disc.
If you smell lemon, when you go to a decision chamber, you've got to pick the
yellow disc, for example, so they can learn to make those associations. Um,
You can also train them to do kind of a delayed match to sample task.
You're familiar with the delayed match to sample task? Yeah,
of course. Just define it.
So the idea is that the classical experiment is you flash a stimulus,
let's say a color to a person, blue.
And then later on, a few seconds later, they're given a choice between blue and yellow.
So if you see blue as your sample stimulus, you've got to pick the matching
stimulus with blue. What's the delay the bees can handle? About five seconds. Okay.
And then after five seconds, it drops off rapidly or gradually? It drops off gradually.
In about five, it's close to random.
Okay. But they can also learn non-matching.
So pick the stimulus that does
not match. And they can also learn to match across stimulus modalities.
So you can train them to do the matching task using sense and then expose them
to a visual task on which they haven't been trained and they will do the matching.
So, they've learned the concept of matching from the smell task and they're
applying it to visual tasks. So, they generalize the rule. Exactly.
Both for matching and non-matching. Yes.
Okay. Isn't that nice? That's amazing. That's pretty amazing.
That's really cool. Okay.
Still don't know how it's useful in nature. Here we're treating these animals as a lab rat.
I can't imagine that it's… Okay. A certain context, maybe when you land on a
flower and you get some good reward there, you might want to seek out a similar
flower which has the same color.
So in that sense, there's a thing. But you see, in the lab experiment,
you're not being rewarded on the sample stimulus.
I find it hard to think of a situation in nature where this particular task has to be applied.
But they can do it, they can do it, you see. Look, but why is that so hard?
I mean, here you are, I'm Mr. B flying around.
I'm visiting different flowers, different colors, different scents and everything.
And now there might be contingent relations among these flowers.
That's the rule, right? So maybe when I visit, let's say, a yellow neutral flower,
I should not go to the blue one because I get absolutely nothing.
Well, if I first go towards the yellow one and then to the red one that was
hidden behind it, I get a reward.
So now I can start to learn contingencies in my environment. Okay, that's sure.
You could do that, but you've got to...
Okay, let me say this one thing. I don't know if that answers your question.
They can learn... It's called... What do you call it? It's symbolic delayed master sampling.
So not just direct matching of stimuli, but saying, for example,
if I see blue at the entrance,
then I should pick the vertical grating versus the horizontal grating in one decision chamber.
And then you can cascade this. You can go to a second decision chamber where
you have a choice between two other stimuli, for example, a radial pattern versus
a set of concentric circles.
So if you see blue, you pick vertical and you pick a radial.
If you see yellow, then you pick the other stripe orientation and the other pattern.
So all those contingencies, they can learn those contingencies.
Now, so you think that'll be useful in nature?
For problem solving, I mean, these animals will fight themselves in complex situations.
They have to navigate through, let's say, dense growth.
There might be relationships between flowers. Yeah. So, chances are in nature,
you probably won't find the stimulus changing in the same location.
But you could certainly apply it to two different trajectories.
For example, if I come here, if I come to this location, you know,
I recognize this blue, blue something here.
And that tells me how I should proceed. Whereas if I see something else here.
Right. Moreover, it can pick up regularities among plants.
Because let's say the plant from which you want to get the nectar,
the flower, might not be easily visible from their altitude.
But however, the tree just next to it is. Sure. Right. And maybe the flowers
you like grow close to certain trees.
This is completely reasonable. So now I can extract these regularities from
my environment and immediately apply them.
I agree. Well, thank you for pointing that out. How's my bee psychology going?
I feel a lot more encouraged about what I'm doing.
Great. We're so pleased to have you here. What got you interested?
You're giving me a reason to live.
Great. But what got you interested in bees in the first place?
Oh, it was purely accident.
Everything I've done in my life has been just not planned at all.
So I did my undergraduate in electrical engineering, as you probably know, in Bangalore.
And when I was doing my master's, my professor suggested, and that was,
by the way, my master's was, and it was called Applied Electronics and Servo
Mechanisms in those days.
So it's mostly control theory and electronics.
Too soft? I'm sorry.
And with the Masters, my professor said, why don't you do something biological?
Why don't you try to model a biological system using the control system theory
that you've been studying?
And so we decided to try and model the human eye movement system as a target
tracking system, a moving target.
So that's how I got interested in this thing. And when I went to the U.S.
To do my Ph.D., I was looking for some project in the area of interface between
biology and engineering, and it turned out the only person who was doing anything
in that area was the person who was working on insect eyes. So I got into fly vision in that way.
And then when I went to Zurich, Zurich in Switzerland was the,
you know, one of the world's, you know, sort of leading areas in bee work.
So there, there's a lady called Miriam Lehrer, who's unfortunately passed away
now, who is the world's expert in training bees.
She was with Rudiger Wehner? Exactly. So yeah, it was in Rudiger Wehner's lab.
Yeah. So that's where I learned all about bees.
So it was purely by accident, but it was a wonderful accident.
They're just amazing creatures.
Right. And so, but how long ago was that your first encounter with the bees?
Well, if you ask me to give away my age.
No, no, we won't go that far. Well, that would be, that would have been 1982
when I was a young assistant professor.
Okay. But then, so the bee is still your main target preparation in the empirical work?
I would say so, yeah. We're starting to work a little bit with birds as well,
but bees are the main thing.
Thing yeah so so then there are two issues to explore right so so with respect
to the to the b cognition we touched a little bit on this issue of memory um but now so is this sort of,
this ability to extract these symbolic rules would you see that as really the
the highest level of of of b cognition that can be achieved or other tricks in their cognition bag?
Well, there are a few things which are really kind of striking.
That's one thing. The other thing we were talking about the other day was maze learning.
They learn to go through labyrinths and various kinds of labyrinths.
But there's also the business of breaking camouflage and perceiving camouflage
objects which they normally would not see.
How does that work? Well, for example, you probably know this picture of this, uh.
Dalmatian dog that's hidden behind a pattern of camouflage dots.
It's a dog I never see. You never see it, right?
But once someone traces the outline for you and you see that same image again, it pops out.
You see the Dalmatian every time. I always say no, but it's true.
Well, bees seem to have that property too.
So if you can give them a hint, initially when you show them two camouflaged
objects and try to train them to distinguish between them, they don't seem to do it.
But if you give them a hint by showing them un-camouflaged versions of the same
two objects, and you train them to discriminate those two, and then you show
them the camouflage objects, and they can pick them out.
And not only that, once they've learned how to break the camouflage,
you can give them novel camouflage objects.
And without the pre-training, they will do the task, learn the task.
So you really taught them a different way in which to see the world.
That's pretty impressive. Which is not bad.
Do you think all these skills have given the bees an evolutionary advantage? advantage.
Are there more bees than would be there otherwise?
I wouldn't be surprised. I mean, I don't know to what extent it depends on how
much predation there is and how many creatures are out there trying to eat these creatures.
But that certainly is the fact that they can sting has certainly kept them alive for quite a while.
And as you know, there's also these wonderful bee mimics.
There are lots of insects which don't sting, which have evolved to mimic the
bee because other birds will stay away from them because they look like a bee.
They taste beautiful, but the birds just avoid them because they've been conditioned
to avoid any bee. But that's more the sting, though.
That's the sting. How are their cognitive abilities helping them?
I think all I can say is it certainly helps them become more efficient foragers.
I mean, this is where recent beautiful work that was done, not in our lab,
but in the lab of a chap called James Neer, and he discovered that when a bee comes back and.
Dances and to advertise a food source and another bee is watching this dance
and it has been to that food source and has had trouble it's been attacked by
a spider for example this bee will then head butt this dancing bee and stop
it from dancing because it's a dangerous food source and this
stopping is very target specific so it's it stops this dance only when the bee
is advertising adding that particular food, so nothing else.
And also, only if this bee has come back badly damaged.
If this bee has had a fight with a spider and it had actually won, no problem.
Okay, right. So I think that's getting to the point where these creatures are,
I would say, almost human.
That's pretty impressive. I never stung a spider though, but there's a work on that.
But the thing is that the memory of these bees that would be providing,
let's say, the core infrastructure for these cognitive capabilities seems to
have, let's say, varying time windows in which it operates and is stable.
So how many, let's say, would the distinction between the short and long term
memory be sufficient to describe bee memory or do you see more stages?
Well, there's also working memory.
So this delayed master sample is a kind of working memory, right?
And that lasts about five seconds.
And there is the short-term memory, people say, which lasts about an hour.
And then beyond that, it gets put into long-term memory.
Now, exactly where the short-term memory resides, is it in the mushroom bodies
or is it somewhere else? Right. No one really knows. I mean, the...
It's very sad. I mean, the main problem, I think, is, as usual,
the funding for insect work is not as good as it is with vertebrates.
So, really, the physiology and even the anatomy. Well, the anatomy,
thanks to people like Nick, is really doing very well. But the physiology is really suffering.
But now about the stability of this memory. For instance, if the bee comes back
and it dances, does that compromise the memory of that location,
you think, in any way? Good point.
A good point. I always looked at that. I mean, why should it?
Well, it has to recall it, right? So the memory might become instable because
it has to be recalled, replayed in some form.
So you're saying every time you recall, you might lose the memory trace?
Well, there are some theories of memory that go in that direction,
right? Because it means recall would mean you have to make the memory again
accessible and you pay a price for that. Oh, yeah, that's a good point.
I don't know. I wouldn't be able to answer that question. But as you probably
know, the dance is done only after the bee has wilted the foot several times.
And also, by then, this particular experienced bee will not even be relying
on its own dance information to get to the food source.
It'll be using the sequence of landmarks and things that it's learned to go along the way.
Right. So, that's why, although on a cloudy day, when the sun is covered and
the whole sky is cloudy and there's no polarized light or sun, there's no dancing.
The bees don't dance. But the bees that already know the food source will continue
to forage because they don't need that information anymore.
I mean, it's like you and I, you know, we know a familiar place like this beach
that we're going to. I don't know it, but you know it.
But I would be using vector information because you've given me that vector
information, but you won't be
using that. We're just using a sequence of landmarks, right? That's right.
That's how these bees do it. The experienced bees do it that way.
So the information hidden in the dance is really very crude. Yes.
Crude information about the environment. And it's kind of dispensed with,
you know, once the bee has advertised the food source and enough recruits have
been collected, they basically forget about it. The dance is not done anymore.
Okay. But when a new food source comes up, then, of course, the dancing starts again.
Okay. But so now another part of your work, which might also be sort of have
its roots in your engineering background, is you have been mapping a lot of
this understanding of insect vision and behavior onto machines, onto robots.
Yeah, that again, you know, that's also something that we didn't think of about ourselves.
And it sounds a bit stupid because, you know, this first thing that we published
on bees navigating down corridors, we didn't even think of it as anything that
had potential engineering applications.
But then after that work got published, a number of labs started to build robots
that navigated down corridors using the same principle.
And so we were actually sort of rather, you know, latecomers to this thing.
And in fact, I wasn't even really pursuing that a lot until...
A few US-based military funding agencies kind of just tapped us on the shoulder
and said, hey, you know, would you like to work on this? And here's some funding.
So that's how we got into it, really, ourselves.
But now tell me, so to what extent have these principles really generalized successfully?
What can you really achieve? How close is it to what you see in these insects and so on?
Yeah, so as I was saying briefly the other day, so we're not really doing what
you would call as biomimesis.
So we're totally not building a compound eye.
I mean, it's probably a good idea to do that. If you have the expertise and
the technology, you'll probably learn something nice.
But our idea is to implement the principle. So instead of using a compound eye,
we started out by using just a single camera, off-the-shelf camera,
but building a specially shaped reflecting surface.
Just a mirror, but a specially shaped mirror.
So you can do, you can play, we like to do things with mirrors. We're fond of mirrors.
So with a mirror, for example, you can have either a spherical mirror.
The trouble with a spherical mirror is that the radial gain is not constant.
So what happens is if you look at the world with a hemispherical mirror,
then the central part is magnified and the peripheral part is compressed, right?
So you don't have uniform gain, elevational gain, as you might say.
So we tailor the shape that produces uniform elevational gain,
which is kind of useful because then you don't lose resolution.
You make, what do you say, optimum use of the resolution, no matter where you're
looking, right? So we use that on our aircraft with a standard camera.
And that functions almost as well as a compound dye.
There are a few blind zones, of course, like directly behind the camera you
can't see, and then behind the mirror you can't see, but you've got a good field of view there.
So that's what we do. So we implement compound panoramic vision in that way.
And we don't build a flapping wing vehicle because that's too hard and we're not experts.
We leave that to people like Mike Dickinson and so on. So we just build a vision
system that uses some of the.
It's a global principle that we discover from insectivision.
So, for example, the finding that you need to measure optic flow in the two
eyes and balance them in order to fly down the middle of a corridor.
But the actual computation of the optic flow, again, we don't do it using the
biological algorithms because we find that the biological algorithms don't do the job for us. Why not?
Because they don't signal velocity reliably. They confound image velocity with spatial frequency.
They're not robust to changes in contrast. They're just a mesh.
Well, just to clarify, though, when you say biological algorithm,
you really have in mind the Reichardt model or models that people have made.
People have made. You don't actually know what is going on inside the fly's
brain. We don't. Yeah, exactly.
The true biological algorithm obviously works, right?
Yeah. The real biological algorithm actually works. It still hasn't been,
yeah, we don't know exactly what's happening in terms of the nervous system to generate that.
But behaviorally, the animal behaves as though it is sensing velocity very, very robustly.
Because what I found interesting, I mean, it sounded a bit normative what you were saying, right?
Because in some sense, the biological algorithm as a biological algorithm must
be incredibly precise and robust.
Because, you know, it's computed in this really minuscule computational system.
When I say biological algorithm, I mean I mean the known biological algorithm.
Our interpretations are hypothesized. Hypothesized, or the recordings from neurons,
the models of neurons that,
that respond to motion, do not seem to do the job. So that's perhaps the limitation
of the people who did the modeling rather than of the fly. Well, yeah, okay.
In a way, they're trying to model the neuron's response, and they're probably
right. But they have done a bad job.
Well, this particular neuron may show that response, but maybe they're not looking at the right neuron.
But there's something interesting here, Srini, that I think,
of course, I understand you want to defend your colleagues, okay? That's all right.
But the point is that, indeed, people have taken the physiology,
given that some sort of functional interpretation.
But if you apply it to your aircraft, it is exposed to just different conditions
because you're not tying this airplane to a table and show it fixed stimuli.
It's now flying around. And the input sampling, the dynamics of the stimuli
has really changed completely.
And that's where these algorithms, of course, collapse because they have been
calibrated in highly controlled, rather artificial situations.
So I think an interesting consequence of this observation is maybe that both
the experimental context, the experimental paradigms have sort of been biasing
our interpretation of what these systems do.
And on top of that, possibly, our algorithmic function interpretation has just
maybe been completely misguided.
This is actually a consequence of your work.
Yeah. I mean, the other way, I suppose, the way we're doing it presently is
to put on a different hat completely.
So when I say I want to measure optic flow, I simply, you know,
We've developed a whole bunch of just machine vision-oriented algorithms, which work well.
I mean, they give you the right answer. They've probably got nothing to do with the biology.
So that's how we do it. But in the future, maybe there's a learning method or a...
I don't know, genetically, what's the word, genetic algorithm-based approach,
which will give us something that produces a circuit that measures velocity accurately.
Because there's another aspect to this, right? That in some sense,
what you're also using is computational hardware that has certain capabilities.
Yeah. And you're exploiting, let's say, algorithms people have developed using this kind of hardware.
But biological hardware might have to optimize different parameters.
Exactly. Than the engineered hardware, because in the case of the bee,
it must be flyable, it must be very compact, it must be energy efficient,
and so on. It's optimizing so many different criteria.
Right. I agree completely, and it's not necessarily tuned to exactly what we want.
So, therefore, if you talk about biomimetics, I think it might also be a good
way to actually benchmark and validate our understanding of the real biological system.
I like to call it bioprincipics rather than biomimetics. So,
you abstract the principles.
You don't lavishly copy the biology. Just abstract the higher order principles
and try and implement them, right? Sure.
And then test our ideas that way about whether it's using this principle or not. Absolutely.
But earlier you said yourself that's not really what you do with your airplanes.
With the aircraft, no, what we do is we want to see, okay, we know the fact
that insect is now using optic flow to control its landing.
Can we get an aircraft to use optic flow again to control its landing?
Representing the exact way in which optic flow is computed.
We don't know in an insect, but we'll use our own way. We'll use our own engineering-based way.
And that works. And that works. But this is funny because in some sense,
in the literature, people would make you believe that they do know how flies
or flying insects compute motion. They say it's the Reichert Correlator.
So why do you say we don't know how it's computed?
Because it doesn't fit the behavioral data.
Okay, tell me. See, the thing with the Reichert Correlator, The only thing it
can reliably tell you, really, if it comes down to it, is the direction in which something is moving.
Beyond that, the information is very ambiguous.
So if you want to use it as a bang-bang controller to control the direction,
keep flying forward, right?
So if the world moves to the right, it means you veered off to the left,
and you generate a compensator. You're off to a turn back to the right.
For doing that kind of bang-bang control, it's great. It's very reliable.
But when you're turning to the right, you want to know how rapidly you're turning.
It did not give you a reliable answer. And then there's another thing that always
worried me about the Reichardt Correlator is that in some sense,
it tells us that neurons can multiply.
And biophysically, I find it always difficult to comprehend how you could do that.
Well, actually, we had a cute idea that I published as part of my PhD thesis
a long time ago, which I don't think anyone's really picked it.
You could do it very easily.
Very easily. Okay, just a quick...
A coincidence detector, a neuron, and two trains of random spike trains coming
in, two different frequencies, right? They're random, they jitter.
So the probability of a coincidence is proportional to F1, frequency 1, and frequency of F2.
So the spike rate at the output will be proportional to the spike rates in the
input, if you assume randomness. Right.
If there's no randomness, if they're periodic, then that doesn't work because
you could have either perfect, you know, synchrony or no synchrony.
So certainly evidence for this kind of... But the moment you have noise,
and noise is actually helpful in this case, you will get a beautiful product.
I don't know if anyone's found a neuron like that, but I'd love to see a neuron
because we published that a long time ago.
We also briefly discussed the possibility there are other sensors,
like it is actually sensing drag in the air or something like that.
Right, right, right. Possibly.
Yeah, certainly. Combining the visual information with other sources.
There are the antennae doing all kinds of things. as we were saying,
they're probably tactile sensors as well.
By the way, just as there's a beautiful rat whisker story we heard just now,
bees look like when they come in close to a surface to land,
when the surface is oriented nearly vertically, they're using their antenna
to make the first mechanical contact.
And also, it seems like the antennae tend to be perpendicular to the surface.
As they're coming in and hovering, they have a perception of the surface slant,
And you can even fool them by producing optical illusions which simulate different
surface lands by having texture gradients.
So obviously the eye is, the visual system is analyzing surface orientation.
And maybe that's one where we could apply this model too. Mm-hmm. Yeah.
Excellent. So then you haven't said much about the birds yet,
and still I want to hear something about it.
Oh, we're just starting, yeah. Okay. But at the level of intuition…,
So now we talked about the bee, we've talked about this sort of bioprincipics
and extraction of core design principles of the bee brain.
Would you believe that you will also find some of these design principles back into the bird brain?
At least so far, we found a couple of similarities.
One again, and this is, again, we haven't tested a whole range of birds.
But if you take one of the standard birds in Australia, it's called the budgeriga.
Do you get budgies here? In the animal store, yeah. In the pet stores.
There it's a kind of an iconic Australian bird, native bird.
Anyway, there, if you fly them down a tunnel, they show very similar behavior to what the bees do.
So we haven't been able to actually physically move patterns in these tunnels
yet. We're starting to do that now with a long tunnel.
But you can manipulate the optic flow by having static patterns,
which are, for example, horizontal stripes on one side and vertical stripes on the other side.
Then you imbalance the optic flow that way, and they behave in exactly the same way. Right.
This is interesting in the context of our notion of convergent evolution and
understanding engineering principles by looking at convergent evolution.
Clearly, if the birds are using the same optic flow mechanisms as the bees are,
it will have to be convergent simply because the common ancestor didn't fly.
Yeah. The other thing that seems to be uniformly true in many species,
including humans, is the fact that motion perception is largely colorblind.
So humans, as you probably know, although we have beautiful color vision,
the motion sensing system is almost colorblind.
It's driven only by the luminance pathway, red plus green.
If you look at the bee, again, it's colorblind. It's driven only by the green
receptor, all of the motion sensing.
And if you look at the spectral sensitivity function of the green receptor,
it sits bang on the sum of, if you take the red plus green cones,
into our luminous pathway, it sits exactly on top of that.
So it's as though that system is adapted exactly to our environment, exactly the same thing.
And now we're finding that even birds are like that. At least these buzzard
guys are behaving as though their perception of motion is also colorblind,
although these birds have even better color vision. They're tetrachromatic, right?
So the system is going out of the way to make the motion detection colorblind.
Exactly. Do we understand why? But wait, but there can be… Is that more efficient? Yes.
I think the common notion is that the early creatures, and then again,
correct me because I'm not an evolutionary biologist, is that most of the early
vision systems did not have color.
If you needed to have basic motion sensing just in order to move around.
And not bump into objects and just navigate safely, and then later on when flowers
evolved in the scene and it became important to recognize objects based on color,
that's when color came in.
So, many people, and this is still a hotly debated topic, but many people think,
you know, color vision sort of co-evolved with bees and flowers at the same time, pretty much.
So, there's no fundamental need to have color to begin with.
But now, on this issue of convergent evolution, right?
So, you could also argue that there can be, in the end, a common ancestor that
just had to move and crawl, right, in a visual world.
And that could have picked up these kinds of responses to motion.
You don't need to fly to pick that up. So if you just have to speculate, how would you see this?
Isn't it convergent evolution to a similar solution between flying insects and birds?
Or just really a very ancient common ancestor just crawled around?
I would say a very ancient common ancestor is my guess, yeah.
Primitive things like photo taxes, going towards a bright light source is probably
a very fundamental thing.
Going towards something that smells good is probably a good thing.
But on the other hand though I mean hummingbirds and bees fly in the same way
and the common ancestors didn't fly so not everything is convergent we have
to be a little careful about that in terms of optic flow,
do you think that it is indeed in the common ancestors are the parameter,
regimes right even though it had to sense motion do the birds and the bees have
a a more refined sense of the speed.
Than, for example?
Some crawling primitive animal, let's say.
Oh yeah, it would have a more refined sense. Like a snail, for instance,
you know? Yeah. It will respond to moving visual stimuli.
See, my guess is that...
Do flying creatures have to have more precise flow detection?
Oh, sure, yeah. Yeah, they need to have also, depending on the speed of flight,
it seems like certainly the nervous system is very different,
right? The dynamic properties are very different.
Right from the photoreceptors, you have a much higher flicker fusion frequency
if you've got a fast-flying creature.
And I would say even the motion-sensing units are tuned to their speed.
So I would say that a thing like a primitive worm would probably have, again, the same kind of,
Same kind of basic structure for motion
detection, because it's not very complicated when you think about it.
If you just want to tell which direction something is moving,
you just need to have one inhibitory synapse, right, between one photoreceptor and another one.
And that could have evolved as early as lateral inhibition, you know? Exactly.
And so you just modify that a little bit. Well, actually, for any sensor,
it is sort of the endocannulas.
But you could even go to other modalities. If you go to some mechanical sensing
or chemical sensing, all you want to do is measure a difference.
That's what this is. What is then generalized to some optic sensor.
You just measure a difference.
But as you were also pointing out, the Rijkaard detector, which does that, is not adequate.
It's not adequate. So therefore, there is something more. Sure.
Yes, there is something in common.
But there's more. Yes. And that is really necessary for this behavior.
And that's where maybe you even have parallel pathways. My guess is that,
at least in the B, there must be several parallel pathways that are sensing
different aspects of motion.
For example, this optical avoidance and flying down the middle of a corridor,
that motion sensing seems to be non-directional. It's not directional at all.
So if you think about it, if you want to avoid an obstacle, you don't really
care which way the thing is moving. You just want to avoid it because of its high speed, right?
So maybe it's computationally simpler to just do some non-directional motion
sensing rather than compute motion direction.
It's just not necessary. And this thing has completely different properties.
You know, I think things are changing now. I probably shouldn't say this,
but as long as Reichardt was alive, there was a big school of tubing and following the Reichardt model.
But I think now things are starting to unravel.
Okay. But so to finish up, there are two questions that we actually pose to
everybody we interview for the podcast.
So there But here you come out of engineering, discover the bee,
do all this fantastic work, have it fly now on these airplanes, move to the bird.
So, you've been exposed to these different disciplines, trying to extract these
engineering principles of biological systems.
So, if we would like to follow in that tradition, what's the law of Srini we
should adhere to? What's Srini's law? Srini's law? Yeah.
Follow your heart. Don't worry about where your next job is going to be.
No, just follow your heart. And if you enjoy what you're doing,
things will come to you naturally.
Do you want me to say briefly what my next thing, I don't know if I will ever
get around to doing this, but what my next, I would love to be able to look
at higher emotions in simple nervous systems.
Things like, for example, joy, disappointment, fear, anger, pain.
I think there's a whole thing there, and I believe there's an entire continuum
and there's no such thing as, you know, I find it hard to believe that invertebrates,
for example, don't feel pain, whereas vertebrates do. They know hard and fast.
So would you speculate then that
these animals would qualify for a very primitive form of consciousness?
I would think so. I would think so. Especially, you know, that headbutting that I mentioned to you.
That's, you know, I find it hard to believe that's all done in a purely a reflexive
way, especially because it's so subtle, right?
Well, headbutting is not that subtle, I have to say.
No, but the things that control when it headbutts and doesn't headbutt,
it's very precise, right?
I mean, the fact that it's exactly that food source is being signaled,
and also on how dangerous the predator there is.
Right. I mean, you can't say this is all being done by some stupid automaton, right?
Well, I think to any of us who've actually watched flies and things closely
behaviorally, there's no doubt in our minds that they are constants.
There's a lot going on, you know. For example, you jab a cat or a dog and it
flinches and you say it feels pain.
And you jab an insect and it again does the same kind of reaction,
but you say oh, it can't be pain because it's an insect. It must just be a reflex.
How do you know, right? This is a great topic for another podcast interview
that we're definitely going to have in the future. Sorry, I didn't want to launch
you off on this. No, no, this is good.
But then the final question for me is really, so since Since Partha is so successful
and generates all this money, we can travel around the world on his budget,
you know, without any trouble.
So five years from now, we're going to go visit your lab and we're going to
remind you of a hypothesis that you generate today that you feel most passionate
about and that you claim will come out five years from now. So what's that hypothesis?
Oh, my hypothesis will be that insects feel pain. Right.
You're really going to work on that? Oh, look, if I get the funding,
if I get the funding, I'd love to.
Okay. I've got some ways in which they can be tested, I think,
which will be a little more telling than just jabbing the insect and saying
it's a reflexive thing because that doesn't convince people.
It convinces me, but it doesn't convince other people.
But I think there must be ways. You can never make a conclusive proof.
But again, as for example, now that they convinced that fish feel pain,
for example, do you know how they finally did it? No. In fact, it involved a bee.
They took a bee sting. They took a sting out of a bee and stung the lip of the
fish and the fish twitched.
And they said, aha, the fish feels pain. It seems like a very primitive,
unsophisticated experiment.
But the world was at the right time to accept that at that point.
And they said, aha, fish feel pain.
And now from now on, we've got guidelines for experimenting with fish.
So that means now we have to slap a bee with a fish and see if it twitches.
I think that's what you're proposing now. Yeah, the fish will probably twitch, right?
Because the bee will have stung the fish. And the bee doesn't feel the pain, obviously.
You proved two things there. The bee doesn't feel pain and the fish feels pain.
I'm so happy we resolved that issue. Srini, thank you very much for this wonderful
interview. Thank you. Thank you, Paul.
Thank you. The CSN Podcast was produced by the Convergent Science Network of
Biometics and Biohybrid Systems,
a project funded by the European 7th Research Framework Programme.
Music.