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TEDGlobal 2009

Henry Markram: A brain in a supercomputer

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Henry Markram says the mysteries of the mind can be solved -- soon. Mental illness, memory, perception: they're made of neurons and electric signals, and he plans to find them with a supercomputer that models all the brain's 100,000,000,000,000 synapses.

- Neuroscientist
Henry Markram is director of Blue Brain, a supercomputing project that can model components of the mammalian brain to precise cellular detail -- and simulate their activity in 3D. Soon he'll simulate a whole rat brain in real time. Full bio

Our mission is to build
00:18
a detailed, realistic
00:21
computer model of the human brain.
00:23
And we've done, in the past four years,
00:25
a proof of concept
00:28
on a small part of the rodent brain,
00:30
and with this proof of concept we are now scaling the project up
00:33
to reach the human brain.
00:36
Why are we doing this?
00:39
There are three important reasons.
00:41
The first is, it's essential for us to understand the human brain
00:43
if we do want to get along in society,
00:47
and I think that it is a key step in evolution.
00:49
The second reason is,
00:53
we cannot keep doing animal experimentation forever,
00:55
and we have to embody all our data and all our knowledge
01:01
into a working model.
01:05
It's like a Noah's Ark. It's like an archive.
01:08
And the third reason is that there are two billion people on the planet
01:12
that are affected by mental disorder,
01:15
and the drugs that are used today
01:19
are largely empirical.
01:21
I think that we can come up with very concrete solutions on
01:23
how to treat disorders.
01:26
Now, even at this stage,
01:29
we can use the brain model
01:32
to explore some fundamental questions
01:34
about how the brain works.
01:37
And here, at TED, for the first time,
01:39
I'd like to share with you how we're addressing
01:41
one theory -- there are many theories --
01:43
one theory of how the brain works.
01:46
So, this theory is that the brain
01:50
creates, builds, a version of the universe,
01:54
and projects this version of the universe,
02:00
like a bubble, all around us.
02:03
Now, this is of course a topic of philosophical debate for centuries.
02:07
But, for the first time, we can actually address this,
02:11
with brain simulation,
02:14
and ask very systematic and rigorous questions,
02:16
whether this theory could possibly be true.
02:20
The reason why the moon is huge on the horizon
02:24
is simply because our perceptual bubble
02:27
does not stretch out 380,000 kilometers.
02:30
It runs out of space.
02:34
And so what we do is we compare the buildings
02:36
within our perceptual bubble,
02:40
and we make a decision.
02:42
We make a decision it's that big,
02:44
even though it's not that big.
02:46
And what that illustrates
02:48
is that decisions are the key things
02:50
that support our perceptual bubble. It keeps it alive.
02:52
Without decisions you cannot see, you cannot think,
02:57
you cannot feel.
02:59
And you may think that anesthetics work
03:01
by sending you into some deep sleep,
03:03
or by blocking your receptors so that you don't feel pain,
03:06
but in fact most anesthetics don't work that way.
03:09
What they do is they introduce a noise
03:12
into the brain so that the neurons cannot understand each other.
03:15
They are confused,
03:18
and you cannot make a decision.
03:20
So, while you're trying to make up your mind
03:23
what the doctor, the surgeon, is doing
03:26
while he's hacking away at your body, he's long gone.
03:28
He's at home having tea.
03:30
(Laughter)
03:32
So, when you walk up to a door and you open it,
03:34
what you compulsively have to do to perceive
03:37
is to make decisions,
03:40
thousands of decisions about the size of the room,
03:42
the walls, the height, the objects in this room.
03:45
99 percent of what you see
03:48
is not what comes in through the eyes.
03:51
It is what you infer about that room.
03:55
So I can say, with some certainty,
03:59
"I think, therefore I am."
04:03
But I cannot say, "You think, therefore you are,"
04:06
because "you" are within my perceptual bubble.
04:10
Now, we can speculate and philosophize this,
04:15
but we don't actually have to for the next hundred years.
04:18
We can ask a very concrete question.
04:21
"Can the brain build such a perception?"
04:23
Is it capable of doing it?
04:27
Does it have the substance to do it?
04:29
And that's what I'm going to describe to you today.
04:31
So, it took the universe 11 billion years to build the brain.
04:34
It had to improve it a little bit.
04:38
It had to add to the frontal part, so that you would have instincts,
04:40
because they had to cope on land.
04:43
But the real big step was the neocortex.
04:46
It's a new brain. You needed it.
04:50
The mammals needed it
04:52
because they had to cope with parenthood,
04:54
social interactions,
04:58
complex cognitive functions.
05:00
So, you can think of the neocortex
05:03
actually as the ultimate solution today,
05:05
of the universe as we know it.
05:10
It's the pinnacle, it's the final product
05:13
that the universe has produced.
05:15
It was so successful in evolution
05:19
that from mouse to man it expanded
05:21
about a thousandfold in terms of the numbers of neurons,
05:23
to produce this almost frightening
05:26
organ, structure.
05:29
And it has not stopped its evolutionary path.
05:32
In fact, the neocortex in the human brain
05:35
is evolving at an enormous speed.
05:37
If you zoom into the surface of the neocortex,
05:40
you discover that it's made up of little modules,
05:42
G5 processors, like in a computer.
05:45
But there are about a million of them.
05:47
They were so successful in evolution
05:50
that what we did was to duplicate them
05:52
over and over and add more and more of them to the brain
05:54
until we ran out of space in the skull.
05:56
And the brain started to fold in on itself,
05:59
and that's why the neocortex is so highly convoluted.
06:01
We're just packing in columns,
06:04
so that we'd have more neocortical columns
06:06
to perform more complex functions.
06:09
So you can think of the neocortex actually as
06:12
a massive grand piano,
06:14
a million-key grand piano.
06:16
Each of these neocortical columns
06:19
would produce a note.
06:21
You stimulate it; it produces a symphony.
06:23
But it's not just a symphony of perception.
06:26
It's a symphony of your universe, your reality.
06:29
Now, of course it takes years to learn how
06:32
to master a grand piano with a million keys.
06:35
That's why you have to send your kids to good schools,
06:38
hopefully eventually to Oxford.
06:40
But it's not only education.
06:42
It's also genetics.
06:45
You may be born lucky,
06:47
where you know how to master your neocortical column,
06:49
and you can play a fantastic symphony.
06:53
In fact, there is a new theory of autism
06:55
called the "intense world" theory,
06:58
which suggests that the neocortical columns are super-columns.
07:00
They are highly reactive, and they are super-plastic,
07:04
and so the autists are probably capable of
07:08
building and learning a symphony
07:11
which is unthinkable for us.
07:13
But you can also understand
07:15
that if you have a disease
07:17
within one of these columns,
07:19
the note is going to be off.
07:21
The perception, the symphony that you create
07:23
is going to be corrupted,
07:25
and you will have symptoms of disease.
07:27
So, the Holy Grail for neuroscience
07:30
is really to understand the design of the neocoritical column --
07:34
and it's not just for neuroscience;
07:38
it's perhaps to understand perception, to understand reality,
07:40
and perhaps to even also understand physical reality.
07:43
So, what we did was, for the past 15 years,
07:47
was to dissect out the neocortex, systematically.
07:50
It's a bit like going and cataloging a piece of the rainforest.
07:54
How many trees does it have?
07:58
What shapes are the trees?
08:00
How many of each type of tree do you have? Where are they positioned?
08:02
But it's a bit more than cataloging because you actually have to
08:05
describe and discover all the rules of communication,
08:07
the rules of connectivity,
08:11
because the neurons don't just like to connect with any neuron.
08:13
They choose very carefully who they connect with.
08:16
It's also more than cataloging
08:19
because you actually have to build three-dimensional
08:22
digital models of them.
08:24
And we did that for tens of thousands of neurons,
08:26
built digital models of all the different types
08:28
of neurons we came across.
08:31
And once you have that, you can actually
08:33
begin to build the neocortical column.
08:35
And here we're coiling them up.
08:39
But as you do this, what you see
08:42
is that the branches intersect
08:45
actually in millions of locations,
08:47
and at each of these intersections
08:50
they can form a synapse.
08:53
And a synapse is a chemical location
08:55
where they communicate with each other.
08:57
And these synapses together
09:00
form the network
09:02
or the circuit of the brain.
09:04
Now, the circuit, you could also think of as
09:07
the fabric of the brain.
09:11
And when you think of the fabric of the brain,
09:13
the structure, how is it built? What is the pattern of the carpet?
09:16
You realize that this poses
09:20
a fundamental challenge to any theory of the brain,
09:22
and especially to a theory that says
09:26
that there is some reality that emerges
09:28
out of this carpet, out of this particular carpet
09:30
with a particular pattern.
09:33
The reason is because the most important design secret of the brain
09:35
is diversity.
09:38
Every neuron is different.
09:40
It's the same in the forest. Every pine tree is different.
09:42
You may have many different types of trees,
09:44
but every pine tree is different. And in the brain it's the same.
09:46
So there is no neuron in my brain that is the same as another,
09:49
and there is no neuron in my brain that is the same as in yours.
09:52
And your neurons are not going to be oriented and positioned
09:55
in exactly the same way.
09:58
And you may have more or less neurons.
10:00
So it's very unlikely
10:02
that you got the same fabric, the same circuitry.
10:04
So, how could we possibly create a reality
10:08
that we can even understand each other?
10:10
Well, we don't have to speculate.
10:13
We can look at all 10 million synapses now.
10:15
We can look at the fabric. And we can change neurons.
10:18
We can use different neurons with different variations.
10:21
We can position them in different places,
10:23
orient them in different places.
10:25
We can use less or more of them.
10:27
And when we do that
10:29
what we discovered is that the circuitry does change.
10:31
But the pattern of how the circuitry is designed does not.
10:34
So, the fabric of the brain,
10:41
even though your brain may be smaller, bigger,
10:43
it may have different types of neurons,
10:45
different morphologies of neurons,
10:48
we actually do share
10:50
the same fabric.
10:53
And we think this is species-specific,
10:55
which means that that could explain
10:57
why we can't communicate across species.
10:59
So, let's switch it on. But to do it, what you have to do
11:01
is you have to make this come alive.
11:04
We make it come alive
11:06
with equations, a lot of mathematics.
11:08
And, in fact, the equations that make neurons into electrical generators
11:10
were discovered by two Cambridge Nobel Laureates.
11:14
So, we have the mathematics to make neurons come alive.
11:17
We also have the mathematics to describe
11:20
how neurons collect information,
11:22
and how they create a little lightning bolt
11:25
to communicate with each other.
11:28
And when they get to the synapse,
11:30
what they do is they effectively,
11:32
literally, shock the synapse.
11:34
It's like electrical shock
11:37
that releases the chemicals from these synapses.
11:39
And we've got the mathematics to describe this process.
11:42
So we can describe the communication between the neurons.
11:45
There literally are only a handful
11:49
of equations that you need to simulate
11:52
the activity of the neocortex.
11:54
But what you do need is a very big computer.
11:56
And in fact you need one laptop
11:59
to do all the calculations just for one neuron.
12:01
So you need 10,000 laptops.
12:04
So where do you go? You go to IBM,
12:06
and you get a supercomputer, because they know how to take
12:08
10,000 laptops and put it into the size of a refrigerator.
12:10
So now we have this Blue Gene supercomputer.
12:14
We can load up all the neurons,
12:17
each one on to its processor,
12:19
and fire it up, and see what happens.
12:21
Take the magic carpet for a ride.
12:25
Here we activate it. And this gives the first glimpse
12:28
of what is happening in your brain
12:31
when there is a stimulation.
12:33
It's the first view.
12:35
Now, when you look at that the first time, you think,
12:37
"My god. How is reality coming out of that?"
12:39
But, in fact, you can start,
12:44
even though we haven't trained this neocortical column
12:47
to create a specific reality.
12:51
But we can ask, "Where is the rose?"
12:53
We can ask, "Where is it inside,
12:57
if we stimulate it with a picture?"
12:59
Where is it inside the neocortex?
13:02
Ultimately it's got to be there if we stimulated it with it.
13:04
So, the way that we can look at that
13:08
is to ignore the neurons, ignore the synapses,
13:10
and look just at the raw electrical activity.
13:13
Because that is what it's creating.
13:15
It's creating electrical patterns.
13:17
So when we did this,
13:19
we indeed, for the first time,
13:21
saw these ghost-like structures:
13:23
electrical objects appearing
13:26
within the neocortical column.
13:29
And it's these electrical objects
13:32
that are holding all the information about
13:35
whatever stimulated it.
13:38
And then when we zoomed into this,
13:41
it's like a veritable universe.
13:43
So the next step
13:47
is just to take these brain coordinates
13:49
and to project them into perceptual space.
13:53
And if you do that,
13:57
you will be able to step inside
13:59
the reality that is created
14:01
by this machine,
14:03
by this piece of the brain.
14:05
So, in summary,
14:08
I think that the universe may have --
14:10
it's possible --
14:12
evolved a brain to see itself,
14:14
which may be a first step in becoming aware of itself.
14:17
There is a lot more to do to test these theories,
14:22
and to test any other theories.
14:24
But I hope that you are at least partly convinced
14:27
that it is not impossible to build a brain.
14:30
We can do it within 10 years,
14:33
and if we do succeed,
14:35
we will send to TED, in 10 years,
14:37
a hologram to talk to you. Thank you.
14:39
(Applause)
14:42

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About the speaker:

Henry Markram - Neuroscientist
Henry Markram is director of Blue Brain, a supercomputing project that can model components of the mammalian brain to precise cellular detail -- and simulate their activity in 3D. Soon he'll simulate a whole rat brain in real time.

Why you should listen

In the microscopic, yet-uncharted circuitry of the cortex, Henry Markram is perhaps the most ambitious -- and our most promising -- frontiersman. Backed by the extraordinary power of the IBM Blue Gene supercomputing architecture, which can perform hundreds of trillions of calculations per second, he's using complex models to precisely simulate the neocortical column (and its tens of millions of neural connections) in 3D.

Though the aim of Blue Brain research is mainly biomedical, it has been edging up on some deep, contentious philosophical questions about the mind -- "Can a robot think?" and "Can consciousness be reduced to mechanical components?" -- the consequence of which Markram is well aware: Asked by Seed Magazine what a simulation of a full brain might do, he answered, "Everything. I mean everything" -- with a grin.

Now, with a successful proof-of-concept for simulation in hand (the project's first phase was completed in 2007), Markram is looking toward a future where brains might be modeled even down to the molecular and genetic level. Computing power marching rightward and up along the graph of Moore's Law, Markram is sure to be at the forefront as answers to the mysteries of cognition emerge.

More profile about the speaker
Henry Markram | Speaker | TED.com