0:00:09.480,0:00:10.945
Wow! That was great!

0:00:10.945,0:00:13.896
My name is Audrey [br]and I'm a neuroscientist at Caltech.

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And I want to tell you[br]about what I work on here at Caltech.

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So, what I work on in my lab

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is, we actually study sleep.

0:00:21.505,0:00:22.759
Let me ask you a question:

0:00:22.759,0:00:24.501
If you want to study the brain,

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do you want to look at it

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where it's kind of enclosed

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in a skull you can't see through?

0:00:30.033,0:00:33.015
Or do you want to see it [br]in a transparent box?

0:00:33.015,0:00:34.767
I think it's a transparent box!

0:00:34.767,0:00:37.239
So, one thing that's really good

0:00:37.239,0:00:40.759
is that we actually have this [br]already in nature.

0:00:40.759,0:00:42.765
We have a brain in a glass box.

0:00:42.765,0:00:44.409
We have something where we can see

0:00:44.409,0:00:48.008
straight through an organism[br]and see their brain.

0:00:48.008,0:00:50.286
What we can do [br]is zoom in on their brain

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and look at these neurons.

0:00:51.717,0:00:53.986
What I'm showing you here [br]are neurons.

0:00:53.986,0:00:56.513
and each one of them[br]is a different color.

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They're neurons in Technicolor.

0:00:58.996,0:01:02.499
These neurons are hypocretin neurons.

0:01:02.499,0:01:04.840
Does anyone know anybody with narcolepsy?

0:01:04.840,0:01:07.497
Have you guys heard of narcolepsy?

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Narcolepsy is a sleep disorder

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where you're always kind of sleepy,

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and when you get stimuli that excite you,

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or something that is,[br]I guess, your brain food,

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instead of being excited,

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which is kind of the normal thing to do,

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you actually fall asleep.

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These neurons I'm showing you here

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are the neurons[br]that are involved in that.

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So, when we have all these neurons [br]in different colors in the brain,

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the important thing is:

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How do they connect with one another?

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You have all these tracks--

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how many of you guys here [br]have taken public transportation?

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When you have public transportation,

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what's important is [br]where those points of contact are,

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and where all those [br]different tracks lead.

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And so, similar to that,

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we are constructing a system map.

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One of the ways [br]we want to do this,

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is to take cues from how[br]we interact with other humans.

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When we have another human,

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one thing that we do[br]is give a handshake.

0:02:03.517,0:02:06.021
So what professor Cori Bargmann did

0:02:06.021,0:02:09.682
is that she designed a way[br]to do a molecular grasp.

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So for example, I'm neuron Audrey,

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this is neuron Ella,

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and we each have a glove[br]that's not lighting up.

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But when we make contact,

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we actually have our gloves lighting up.

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And when we are further away,

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the lights go off.

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So, this is a way to figure out

0:02:29.523,0:02:31.759
how those neurons[br]are connecting to one other,

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and we can actually see...

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(Audience laughs at off-camera event)

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[Cori and her friends[br]Molecular Grasp]

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...we see when the neurons[br]are talking to each other,

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when they're close enough[br]to one other,

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and when they're further apart [br]from each other.

0:02:44.231,0:02:47.906
I want to talk about[br]my motivation to study science.

0:02:47.906,0:02:49.369
I want to study science

0:02:49.369,0:02:52.867
because I like to test ideas[br]and observe what happens.

0:02:53.487,0:02:57.301
What we want to do is make sure [br]we're making a fair comparison.

0:02:57.301,0:03:01.517
When we have an apple,[br]we want to compare it to an apple.

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When you make a comparison,[br]you want to make sure

0:03:03.917,0:03:06.004
you're comparing[br]an orange to an orange.

0:03:06.004,0:03:10.434
So in controlled conditions,[br]we have lots of different controls.

0:03:10.434,0:03:12.776
We have positive controls [br]and negative controls;

0:03:12.776,0:03:14.768
you have positive controls[br]to make sure

0:03:14.768,0:03:18.022
when you're actually testing something,[br]you can see an effect.

0:03:18.026,0:03:21.499
You have negative controls[br]to make sure you don't have auto-activation,

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so that when you don't have introduction

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of the experimental condition[br]or stimuli you're testing,

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it's not going to just go off.

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And then you have[br]your experimental condition.

0:03:32.256,0:03:35.505
And when you design[br]an experiment,

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you have lots of positive controls,

0:03:37.517,0:03:39.644
you have lots of negative controls,

0:03:39.644,0:03:42.234
and you have your experimental condition.

0:03:42.234,0:03:44.760
One trend that we're seeing [br]in neuroscience

0:03:44.760,0:03:49.497
is that we can observe[br]and test our idea.

0:03:49.497,0:03:50.983
And what we scientists are,

0:03:50.983,0:03:53.997
is we're kind of like little ninjas,

0:03:53.997,0:03:57.505
just quietly looking at the brain.

0:03:57.505,0:04:00.260
And that's what[br]Alex and his friends have done.

0:04:00.260,0:04:03.745
What they do is take these brains[br]that are in glass boxes,

0:04:03.745,0:04:05.777
and the brains are[br]swimming, or sleeping,

0:04:05.777,0:04:07.417
or doing whatever they want,

0:04:07.417,0:04:10.093
and the team, meanwhile,[br]captures the neural activity.

0:04:10.093,0:04:11.443
The way they do this

0:04:11.443,0:04:13.490
is that they've[br]genetically engineered

0:04:13.490,0:04:17.523
each of these neurons[br]to give off a photon of light,

0:04:17.523,0:04:19.476
and the more active[br]these brains are,

0:04:19.476,0:04:22.008
the more photons come out,

0:04:22.008,0:04:24.798
so you see a greater luminescence.

0:04:24.808,0:04:28.249
And so, just like while these fish[br]are doing their regular business,

0:04:28.249,0:04:31.789
while these brains in glass boxes[br]are doing their regular business,

0:04:31.789,0:04:35.787
and we can observe the neural activity.

0:04:35.787,0:04:38.518
Let's say we want to take this[br]a little bit further,

0:04:38.518,0:04:41.284
to the entire human population.

0:04:41.284,0:04:45.759
What if we look at[br]the entire human population,

0:04:45.759,0:04:47.516
in your native environment,

0:04:47.516,0:04:51.756
and let's say we take away[br]those positive and negative controls

0:04:51.756,0:04:54.536
and just have experimental conditions?

0:04:54.536,0:04:57.331
What if we have something [br]like a wiki log,

0:04:57.331,0:04:59.131
a wiki lab notebook,

0:04:59.131,0:05:02.149
where everyone contributes their ideas,

0:05:02.149,0:05:06.307
and as people are contributing,[br]everyone writes over one another,

0:05:06.307,0:05:08.510
kind of like Wikipedia.[br]Who would have thought

0:05:08.510,0:05:10.957
that WIkipedia would have[br]taken off the way it did,

0:05:10.957,0:05:13.904
with all its success?[br]But it did.

0:05:13.904,0:05:16.008
And what if we could[br]apply it to science?

0:05:16.008,0:05:18.852
It's slightly different than[br]how we think about things now.

0:05:18.852,0:05:21.030
We have these apples and oranges,

0:05:21.030,0:05:23.765
and usually we can't compare[br]them to one another.

0:05:23.765,0:05:26.064
But now we have [br]all these different conditions,

0:05:26.064,0:05:28.007
and things like maybe weird fruits,

0:05:28.007,0:05:29.990
like dragon fruit,

0:05:29.990,0:05:31.999
and we want to compare those.

0:05:31.999,0:05:34.250
So, I want to give you a challenge:

0:05:34.250,0:05:36.886
to be the ones,[br]to be the mathematicians

0:05:36.886,0:05:39.085
and the statisticians to design[br]

0:05:39.085,0:05:40.733
these types of algorithms;

0:05:40.733,0:05:42.517
and to decide whether or not

0:05:42.517,0:05:43.785
we can extrapolate trends

0:05:43.785,0:05:47.623
and figure out the observations[br]without all those controls.

0:05:47.623,0:05:50.152
So, change the way we do science.[br]Go change the world.

0:05:50.152,0:05:51.436
Thanks!

0:05:51.436,0:05:54.104
(Applause)