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I grew up watching Star Trek. I love Star Trek.

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Star Trek made me want to see alien creatures,

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creatures from a far-distant world.

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But basically, I figured out that I could find

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those alien creatures right on Earth.

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And what I do is I study insects.

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I'm obsessed with insects, particularly insect flight.

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I think the evolution of insect flight is perhaps

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one of the most important events in the history of life.

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Without insects, there'd be no flowering plants.

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Without flowering plants, there would be no

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clever, fruit-eating primates giving TED Talks.

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(Laughter)

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Now,

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David and Hidehiko and Ketaki

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gave a very compelling story about

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the similarities between fruit flies and humans,

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and there are many similarities,

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and so you might think that if humans are similar to fruit flies,

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the favorite behavior of a fruit fly might be this, for example --

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(Laughter)

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but in my talk, I don't want to emphasize on the similarities

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between humans and fruit flies, but rather the differences,

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and focus on the behaviors that I think fruit flies excel at doing.

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And so I want to show you a high-speed video sequence

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of a fly shot at 7,000 frames per second in infrared lighting,

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and to the right, off-screen, is an electronic looming predator

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that is going to go at the fly.

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The fly is going to sense this predator.

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It is going to extend its legs out.

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It's going to sashay away

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to live to fly another day.

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Now I have carefully cropped this sequence

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to be exactly the duration of a human eye blink,

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so in the time that it would take you to blink your eye,

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the fly has seen this looming predator,

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estimated its position, initiated a motor pattern to fly it away,

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beating its wings at 220 times a second as it does so.

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I think this is a fascinating behavior

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that shows how fast the fly's brain can process information.

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Now, flight -- what does it take to fly?

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Well, in order to fly, just as in a human aircraft,

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you need wings that can generate sufficient aerodynamic forces,

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you need an engine sufficient to generate the power required for flight,

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and you need a controller,

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and in the first human aircraft, the controller was basically

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the brain of Orville and Wilbur sitting in the cockpit.

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Now, how does this compare to a fly?

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Well, I spent a lot of my early career trying to figure out

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how insect wings generate enough force to keep the flies in the air.

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And you might have heard how engineers proved

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that bumblebees couldn't fly.

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Well, the problem was in thinking that the insect wings

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function in the way that aircraft wings work. But they don't.

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And we tackle this problem by building giant,

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dynamically scaled model robot insects

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that would flap in giant pools of mineral oil

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where we could study the aerodynamic forces.

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And it turns out that the insects flap their wings

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in a very clever way, at a very high angle of attack

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that creates a structure at the leading edge of the wing,

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a little tornado-like structure called a leading edge vortex,

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and it's that vortex that actually enables the wings

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to make enough force for the animal to stay in the air.

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But the thing that's actually most -- so, what's fascinating

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is not so much that the wing has some interesting morphology.

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What's clever is the way the fly flaps it,

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which of course ultimately is controlled by the nervous system,

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and this is what enables flies to perform

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these remarkable aerial maneuvers.

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Now, what about the engine?

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The engine of the fly is absolutely fascinating.

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They have two types of flight muscle:

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so-called power muscle, which is stretch-activated,

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which means that it activates itself and does not need to be controlled

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on a contraction-by-contraction basis by the nervous system.

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It's specialized to generate the enormous power required for flight,

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and it fills the middle portion of the fly,

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so when a fly hits your windshield,

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it's basically the power muscle that you're looking at.

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But attached to the base of the wing

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is a set of little, tiny control muscles

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that are not very powerful at all, but they're very fast,

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and they're able to reconfigure the hinge of the wing

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on a stroke-by-stroke basis,

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and this is what enables the fly to change its wing

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and generate the changes in aerodynamic forces

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which change its flight trajectory.

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And of course, the role of the nervous system is to control all this.

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So let's look at the controller.

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Now flies excel in the sorts of sensors

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that they carry to this problem.

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They have antennae that sense odors and detect wind detection.

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They have a sophisticated eye which is

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the fastest visual system on the planet.

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They have another set of eyes on the top of their head.

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We have no idea what they do.

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They have sensors on their wing.

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Their wing is covered with sensors, including sensors

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that sense deformation of the wing.

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They can even taste with their wings.

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One of the most sophisticated sensors a fly has

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is a structure called the halteres.

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The halteres are actually gyroscopes.

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These devices beat back and forth about 200 hertz during flight,

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and the animal can use them to sense its body rotation

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and initiate very, very fast corrective maneuvers.

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But all of this sensory information has to be processed

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by a brain, and yes, indeed, flies have a brain,

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a brain of about 100,000 neurons.

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Now several people at this conference

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have already suggested that fruit flies could serve neuroscience

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because they're a simple model of brain function.

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And the basic punchline of my talk is,

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I'd like to turn that over on its head.

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I don't think they're a simple model of anything.

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And I think that flies are a great model.

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They're a great model for flies.

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(Laughter)

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And let's explore this notion of simplicity.

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So I think, unfortunately, a lot of neuroscientists,

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we're all somewhat narcissistic.

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When we think of brain, we of course imagine our own brain.

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But remember that this kind of brain,

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which is much, much smaller

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— instead of 100 billion neurons, it has 100,000 neurons —

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but this is the most common form of brain on the planet

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and has been for 400 million years.

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And is it fair to say that it's simple?

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Well, it's simple in the sense that it has fewer neurons,

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but is that a fair metric?

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And I would propose it's not a fair metric.

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So let's sort of think about this. I think we have to compare --

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(Laughter) —

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we have to compare the size of the brain

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with what the brain can do.

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So I propose we have a Trump number,

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and the Trump number is the ratio of this man's

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behavioral repertoire to the number of neurons in his brain.

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We'll calculate the Trump number for the fruit fly.

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Now, how many people here think the Trump number

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is higher for the fruit fly?

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(Applause)

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It's a very smart, smart audience.

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Yes, the inequality goes in this direction, or I would posit it.

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Now I realize that it is a little bit absurd

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to compare the behavioral repertoire of a human to a fly.

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But let's take another animal just as an example. Here's a mouse.

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A mouse has about 1,000 times as many neurons as a fly.

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I used to study mice. When I studied mice,

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I used to talk really slowly.

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And then something happened when I started to work on flies.

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(Laughter)

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And I think if you compare the natural history of flies and mice,

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it's really comparable. They have to forage for food.

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They have to engage in courtship.

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They have sex. They hide from predators.

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They do a lot of the similar things.

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But I would argue that flies do more.

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So for example, I'm going to show you a sequence,

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and I have to say, some of my funding comes from the military,

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so I'm showing this classified sequence

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and you cannot discuss it outside of this room. Okay?

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So I want you to look at the payload

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at the tail of the fruit fly.

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Watch it very closely,

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and you'll see why my six-year-old son

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now wants to be a neuroscientist.

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Wait for it.

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Pshhew.

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So at least you'll admit that if fruit flies are not as clever as mice,

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they're at least as clever as pigeons. (Laughter)

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Now, I want to get across that it's not just a matter of numbers

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but also the challenge for a fly to compute

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everything its brain has to compute with such tiny neurons.

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So this is a beautiful image of a visual interneuron from a mouse

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that came from Jeff Lichtman's lab,

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and you can see the wonderful images of brains

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that he showed in his talk.

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But up in the corner, in the right corner, you'll see,

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at the same scale, a visual interneuron from a fly.

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And I'll expand this up.

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And it's a beautifully complex neuron.

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It's just very, very tiny, and there's lots of biophysical challenges

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with trying to compute information with tiny, tiny neurons.

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How small can neurons get? Well, look at this interesting insect.

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It looks sort of like a fly. It has wings, it has eyes,

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it has antennae, its legs, complicated life history,

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it's a parasite, it has to fly around and find caterpillars

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to parasatize,

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but not only is its brain the size of a salt grain,

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which is comparable for a fruit fly,

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it is the size of a salt grain.

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So here's some other organisms at the similar scale.

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This animal is the size of a paramecium and an amoeba,

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and it has a brain of 7,000 neurons that's so small --

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you know these things called cell bodies you've been hearing about,

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where the nucleus of the neuron is?

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This animal gets rid of them because they take up too much space.

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So this is a session on frontiers in neuroscience.

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I would posit that one frontier in neuroscience is to figure out how the brain of that thing works.

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But let's think about this. How can you make a small number of neurons do a lot?

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And I think, from an engineering perspective,

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you think of multiplexing.

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You can take a hardware and have that hardware

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do different things at different times,

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or have different parts of the hardware doing different things.

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And these are the two concepts I'd like to explore.

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And they're not concepts that I've come up with,

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but concepts that have been proposed by others in the past.

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And one idea comes from lessons from chewing crabs.

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And I don't mean chewing the crabs.

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I grew up in Baltimore, and I chew crabs very, very well.

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But I'm talking about the crabs actually doing the chewing.

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Crab chewing is actually really fascinating.

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Crabs have this complicated structure under their carapace

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called the gastric mill

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that grinds their food in a variety of different ways.

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And here's an endoscopic movie of this structure.

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The amazing thing about this is that it's controlled

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by a really tiny set of neurons, about two dozen neurons

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that can produce a vast variety of different motor patterns,

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and the reason it can do this is that this little tiny ganglion

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in the crab is actually inundated by many, many neuromodulators.

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You heard about neuromodulators earlier.

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There are more neuromodulators

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that alter, that innervate this structure than actually neurons in the structure,

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and they're able to generate a complicated set of patterns.

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And this is the work by Eve Marder and her many colleagues

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who've been studying this fascinating system

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that show how a smaller cluster of neurons

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can do many, many, many things

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because of neuromodulation that can take place on a moment-by-moment basis.

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So this is basically multiplexing in time.

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Imagine a network of neurons with one neuromodulator.

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You select one set of cells to perform one sort of behavior,

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another neuromodulator, another set of cells,

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a different pattern, and you can imagine

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you could extrapolate to a very, very complicated system.

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Is there any evidence that flies do this?

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Well, for many years in my laboratory and other laboratories around the world,

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we've been studying fly behaviors in little flight simulators.

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You can tether a fly to a little stick.

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You can measure the aerodynamic forces it's creating.

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You can let the fly play a little video game

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by letting it fly around in a visual display.

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So let me show you a little tiny sequence of this.

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Here's a fly

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and a large infrared view of the fly in the flight simulator,

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and this is a game the flies love to play.

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You allow them to steer towards the little stripe,

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and they'll just steer towards that stripe forever.

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It's part of their visual guidance system.

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But very, very recently, it's been possible

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to modify these sorts of behavioral arenas for physiologies.

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So this is the preparation that one of my former post-docs,

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Gaby Maimon, who's now at Rockefeller, developed,

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and it's basically a flight simulator

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but under conditions where you actually can stick an electrode

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in the brain of the fly and record

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from a genetically identified neuron in the fly's brain.

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And this is what one of these experiments looks like.

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It was a sequence taken from another post-doc in the lab,

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Bettina Schnell.

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The green trace at the bottom is the membrane potential

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of a neuron in the fly's brain,

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and you'll see the fly start to fly, and the fly is actually

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controlling the rotation of that visual pattern itself

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by its own wing motion,

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and you can see this visual interneuron

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respond to the pattern of wing motion as the fly flies.

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So for the first time we've actually been able to record

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from neurons in the fly's brain while the fly

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is performing sophisticated behaviors such as flight.

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And one of the lessons we've been learning

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is that the physiology of cells that we've been studying

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for many years in quiescent flies

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is not the same as the physiology of those cells

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when the flies actually engage in active behaviors

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like flying and walking and so forth.

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And why is the physiology different?

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Well it turns out it's these neuromodulators,

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just like the neuromodulators in that little tiny ganglion in the crabs.

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So here's a picture of the octopamine system.

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Octopamine is a neuromodulator

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that seems to play an important role in flight and other behaviors.

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But this is just one of many neuromodulators

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that's in the fly's brain.

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So I really think that, as we learn more,

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it's going to turn out that the whole fly brain

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is just like a large version of this stomatogastric ganglion,

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and that's one of the reasons why it can do so much with so few neurons.

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Now, another idea, another way of multiplexing

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is multiplexing in space,

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having different parts of a neuron

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do different things at the same time.

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So here's two sort of canonical neurons

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from a vertebrate and an invertebrate,

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a human pyramidal neuron from Ramon y Cajal,

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and another cell to the right, a non-spiking interneuron,

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and this is the work of Alan Watson and Malcolm Burrows many years ago,

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and Malcolm Burrows came up with a pretty interesting idea

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based on the fact that this neuron from a locust

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does not fire action potentials.

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It's a non-spiking cell.

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So a typical cell, like the neurons in our brain,

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has a region called the dendrites that receives input,

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and that input sums together

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and will produce action potentials

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that run down the axon and then activate

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all the output regions of the neuron.

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But non-spiking neurons are actually quite complicated

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because they can have input synapses and output synapses

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all interdigitated, and there's no single action potential

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that drives all the outputs at the same time.

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So there's a possibility that you have computational compartments

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that allow the different parts of the neuron

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to do different things at the same time.

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So these basic concepts of multitasking in time

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and multitasking in space,

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I think these are things that are true in our brains as well,

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but I think the insects are the true masters of this.

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So I hope you think of insects a little bit differently next time,

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and as I say up here, please think before you swat.

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(Applause)