TEDGlobal 2009
Henry Markram: A brain in a supercomputer
亨利 馬克拉姆在超級電腦中創造大腦
Filmed:
Readability: 3.8
1,469,354 views
亨利 馬克拉姆說關於大腦的謎團在不久的將來有望解開。精神疾病，記憶，感知：它們都是由神經元和電子信號產生的。他計畫要用一台超級電腦所建造出的大腦中全部的十萬億個突觸來找到它們。
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. Full bio
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
Double-click the English transcript below to play the video.
00:18
Our mission is to build
0
0
3000
我們的任務是建造
00:21
a detailed, realistic
1
3000
2000
一個詳細而真實的
00:23
computer model of the human brain.
2
5000
2000
人類大腦的計算機模型。
00:25
And we've done, in the past four years,
3
7000
3000
在過去幾年里，我們在一小塊嚙齒類動物
00:28
a proof of concept
4
10000
2000
的腦上做了一個
00:30
on a small part of the rodent brain,
5
12000
3000
用來驗證這個概念的測試，
00:33
and with this proof of concept we are now scaling the project up
6
15000
3000
現在根據這個試驗我們要把項目的規模擴展到
00:36
to reach the human brain.
7
18000
3000
人類大腦的規模。
00:39
Why are we doing this?
8
21000
2000
為甚麼我們要做這項工作？
00:41
There are three important reasons.
9
23000
2000
有三個重要的原因。
00:43
The first is, it's essential for us to understand the human brain
10
25000
4000
首先，是理解人類大腦對我們來將是非常重要的
00:47
if we do want to get along in society,
11
29000
2000
如果我們想在社會中繼續前進
00:49
and I think that it is a key step in evolution.
12
31000
4000
我認為這是進化過程中非常關鍵的一步
00:53
The second reason is,
13
35000
2000
第二個原因是
00:55
we cannot keep doing animal experimentation forever,
14
37000
6000
我們不能總是繼續拿動物做試驗
01:01
and we have to embody all our data and all our knowledge
15
43000
4000
還有我們必須要把我們所有的數據和知識收錄進
01:05
into a working model.
16
47000
3000
一個有效的模型當中。
01:08
It's like a Noah's Ark. It's like an archive.
17
50000
4000
就好像諾亞的方舟。好像是一个文庫。
01:12
And the third reason is that there are two billion people on the planet
18
54000
3000
第三個原因是地球上有20億人
01:15
that are affected by mental disorder,
19
57000
4000
的生活被精神障礙所影響。
01:19
and the drugs that are used today
20
61000
2000
而目前所廣泛使用的藥物
01:21
are largely empirical.
21
63000
2000
都是經驗性的
01:23
I think that we can come up with very concrete solutions on
22
65000
3000
我認為我們能夠對治療精神障礙
01:26
how to treat disorders.
23
68000
3000
提出非常堅實的方案。
01:29
Now, even at this stage,
24
71000
3000
即使是在現階段
01:32
we can use the brain model
25
74000
2000
我們可以使用大腦模型
01:34
to explore some fundamental questions
26
76000
3000
來探究一些關於大腦
01:37
about how the brain works.
27
79000
2000
如何運作的根本的問題
01:39
And here, at TED, for the first time,
28
81000
2000
在這裡，TED大會上，第一次
01:41
I'd like to share with you how we're addressing
29
83000
2000
我想與大家分享我們如何來解決這個理論
01:43
one theory -- there are many theories --
30
85000
3000
有許多的理論
01:46
one theory of how the brain works.
31
88000
4000
其中的一個關於大腦如何工作的理論是
01:50
So, this theory is that the brain
32
92000
4000
所以，這個理論是大腦如何
01:54
creates, builds, a version of the universe,
33
96000
6000
創造、建立一個宇宙版本
02:00
and projects this version of the universe,
34
102000
3000
並將這個宇宙版本像泡泡一樣
02:03
like a bubble, all around us.
35
105000
4000
映射在我們的周圍。
02:07
Now, this is of course a topic of philosophical debate for centuries.
36
109000
4000
當然這是一個經過許多個世紀爭論的話題
02:11
But, for the first time, we can actually address this,
37
113000
3000
但是，這是歷史上第一次我們可以實際地利用
02:14
with brain simulation,
38
116000
2000
大腦模擬來解決它，
02:16
and ask very systematic and rigorous questions,
39
118000
4000
並提出非常系統性，非常嚴謹的問題
02:20
whether this theory could possibly be true.
40
122000
4000
這個理論是是不是正確的
02:24
The reason why the moon is huge on the horizon
41
126000
3000
我們感覺地平線上的月亮非常大的原因
02:27
is simply because our perceptual bubble
42
129000
3000
正是因為我們的感知泡泡
02:30
does not stretch out 380,000 kilometers.
43
132000
4000
並沒有延伸到三十八萬公里之外
02:34
It runs out of space.
44
136000
2000
這真是太遠了
02:36
And so what we do is we compare the buildings
45
138000
4000
我們所做的是在我們的感知泡泡中
02:40
within our perceptual bubble,
46
142000
2000
將其與附近的建築做比較，
02:42
and we make a decision.
47
144000
2000
接著我們做出了一個判斷
02:44
We make a decision it's that big,
48
146000
2000
我們判斷它是那麼大的
02:46
even though it's not that big.
49
148000
2000
即使在我們眼裡它並不大
02:48
And what that illustrates
50
150000
2000
這個例子說明的是
02:50
is that decisions are the key things
51
152000
2000
判斷是讓我們的感知泡泡成立
02:52
that support our perceptual bubble. It keeps it alive.
52
154000
5000
並保證它活躍的關鍵因素。
02:57
Without decisions you cannot see, you cannot think,
53
159000
2000
失去了判斷，你既看不見東西，也不能思考
02:59
you cannot feel.
54
161000
2000
甚麼都感覺不到
03:01
And you may think that anesthetics work
55
163000
2000
你可能以為麻醉劑的工作方式是
03:03
by sending you into some deep sleep,
56
165000
3000
讓你進入酣睡的狀態
03:06
or by blocking your receptors so that you don't feel pain,
57
168000
3000
或者阻撓神經受體的運作來讓你感覺不到疼痛
03:09
but in fact most anesthetics don't work that way.
58
171000
3000
但事實上大多數麻醉劑並不是這樣生效的
03:12
What they do is they introduce a noise
59
174000
3000
它們所做的是在大腦中產生一種噪音來
03:15
into the brain so that the neurons cannot understand each other.
60
177000
3000
讓神經元細胞互相之間無法理解
03:18
They are confused,
61
180000
2000
它們被搞糊塗了
03:20
and you cannot make a decision.
62
182000
3000
這樣你就不能做出判斷
03:23
So, while you're trying to make up your mind
63
185000
3000
所以，當你還在努力着集中注意力
03:26
what the doctor, the surgeon, is doing
64
188000
2000
要搞清楚醫生在你身上動手動腳的時候
03:28
while he's hacking away at your body, he's long gone.
65
190000
2000
對你的身體做了些甚麼，他早已經走人了。
03:30
He's at home having tea.
66
192000
2000
他已經在家喝茶了
03:32
(Laughter)
67
194000
2000
笑聲
03:34
So, when you walk up to a door and you open it,
68
196000
3000
當你走到一扇門前並打開它的時候
03:37
what you compulsively have to do to perceive
69
199000
3000
為了理解周圍環境，
03:40
is to make decisions,
70
202000
2000
你不得不做出判斷，
03:42
thousands of decisions about the size of the room,
71
204000
3000
無數的關於房間與牆壁的大小，高度
03:45
the walls, the height, the objects in this room.
72
207000
3000
以及房間里放的是甚麼東西的判斷。
03:48
99 percent of what you see
73
210000
3000
99％你所看見的東西
03:51
is not what comes in through the eyes.
74
213000
4000
並不是通過眼睛觀察到的
03:55
It is what you infer about that room.
75
217000
4000
而是你對房間所做出的推斷
03:59
So I can say, with some certainty,
76
221000
4000
所以我一定程度上同意
04:03
"I think, therefore I am."
77
225000
3000
‘我思故我在’
04:06
But I cannot say, "You think, therefore you are,"
78
228000
4000
但是我卻不能說“你思故你在”
04:10
because "you" are within my perceptual bubble.
79
232000
5000
因為“你”這個概念是存在與我的感知泡泡之中
04:15
Now, we can speculate and philosophize this,
80
237000
3000
目前我們能推測並進行在哲理層面上研究這個理論
04:18
but we don't actually have to for the next hundred years.
81
240000
3000
不過不用再這樣繼續幾百年了
04:21
We can ask a very concrete question.
82
243000
2000
我們可以問這樣一個具體的問題
04:23
"Can the brain build such a perception?"
83
245000
4000
大腦本身可以映射出這些感覺嗎？
04:27
Is it capable of doing it?
84
249000
2000
它是否有這種能力做到這一點？
04:29
Does it have the substance to do it?
85
251000
2000
它有沒有足夠的物質來產生感覺？
04:31
And that's what I'm going to describe to you today.
86
253000
3000
這就是我今天想要向你們描述的主題
04:34
So, it took the universe 11 billion years to build the brain.
87
256000
4000
在這個宇宙中經過了110億年的進化出了大腦
04:38
It had to improve it a little bit.
88
260000
2000
它需要不斷地改進
04:40
It had to add to the frontal part, so that you would have instincts,
89
262000
3000
需要加上一個額部以讓你能夠擁有本能
04:43
because they had to cope on land.
90
265000
3000
因為生物需要應付地面上的環境
04:46
But the real big step was the neocortex.
91
268000
4000
真正的巨大進步是大腦新皮質
04:50
It's a new brain. You needed it.
92
272000
2000
這是一個新的大腦，你需要它
04:52
The mammals needed it
93
274000
2000
哺乳動物需要它
04:54
because they had to cope with parenthood,
94
276000
4000
因為它們需要撫養幼崽
04:58
social interactions,
95
280000
2000
互相交流
05:00
complex cognitive functions.
96
282000
3000
並使用複雜的識別功能
05:03
So, you can think of the neocortex
97
285000
2000
所以你可以把大腦新皮質看成是
05:05
actually as the ultimate solution today,
98
287000
5000
到目前為止我們所知的
05:10
of the universe as we know it.
99
292000
3000
宇宙中的終極產品。
05:13
It's the pinnacle, it's the final product
100
295000
2000
它是一個巔峰，是宇宙
05:15
that the universe has produced.
101
297000
4000
所製造的最後產品。
05:19
It was so successful in evolution
102
301000
2000
它在進化史中是如此的成功
05:21
that from mouse to man it expanded
103
303000
2000
從老鼠到人類，大腦中神經元
05:23
about a thousandfold in terms of the numbers of neurons,
104
305000
3000
的數量擴展了大約一千倍，
05:26
to produce this almost frightening
105
308000
3000
來構成這個幾乎是嚇人的
05:29
organ, structure.
106
311000
3000
器官，結構。
05:32
And it has not stopped its evolutionary path.
107
314000
3000
它也沒有停止進化的步伐
05:35
In fact, the neocortex in the human brain
108
317000
2000
實際上人類大腦中的新皮質層
05:37
is evolving at an enormous speed.
109
319000
3000
一直在以驚人的速度進化。
05:40
If you zoom into the surface of the neocortex,
110
322000
2000
如果你深入新皮質的表面
05:42
you discover that it's made up of little modules,
111
324000
3000
你會發現它是由微小的模塊組成
05:45
G5 processors, like in a computer.
112
327000
2000
就好像電腦里的G5處理器
05:47
But there are about a million of them.
113
329000
3000
但大腦中有大約100萬個模塊
05:50
They were so successful in evolution
114
332000
2000
它們進化的如此成功
05:52
that what we did was to duplicate them
115
334000
2000
因此我們就不斷地複製它們
05:54
over and over and add more and more of them to the brain
116
336000
2000
不斷地在大腦中加入更多的模塊
05:56
until we ran out of space in the skull.
117
338000
3000
直到用盡所有頭顱中的空間
05:59
And the brain started to fold in on itself,
118
341000
2000
大腦自身開始摺疊起來
06:01
and that's why the neocortex is so highly convoluted.
119
343000
3000
這就是為甚麼新皮質是非常的捲曲的
06:04
We're just packing in columns,
120
346000
2000
它們不斷的往縱深發展形成功能住
06:06
so that we'd have more neocortical columns
121
348000
3000
這樣我們就有更多的皮質功能住
06:09
to perform more complex functions.
122
351000
3000
來執行更複雜的機能
06:12
So you can think of the neocortex actually as
123
354000
2000
你也可以將大腦新皮質
06:14
a massive grand piano,
124
356000
2000
看成一架巨大的鋼琴。
06:16
a million-key grand piano.
125
358000
3000
一部有一百萬個琴鍵的大鋼琴
06:19
Each of these neocortical columns
126
361000
2000
其中的每一個皮質功能住
06:21
would produce a note.
127
363000
2000
會奏出一個音符
06:23
You stimulate it; it produces a symphony.
128
365000
3000
你對它施加刺激，它奏出一部交響曲
06:26
But it's not just a symphony of perception.
129
368000
3000
不過這不僅僅是感覺的交響曲
06:29
It's a symphony of your universe, your reality.
130
371000
3000
是你的宇宙的交響曲，你的現實世界
06:32
Now, of course it takes years to learn how
131
374000
3000
當然一個人需要花費很多年來學習如何彈奏
06:35
to master a grand piano with a million keys.
132
377000
3000
一架有着一百萬個琴鍵的鋼琴
06:38
That's why you have to send your kids to good schools,
133
380000
2000
這就是為甚麼你送孩子去好的學校
06:40
hopefully eventually to Oxford.
134
382000
2000
希望最後去到牛津大學
06:42
But it's not only education.
135
384000
3000
不過不只是教育
06:45
It's also genetics.
136
387000
2000
基因也會影響結果。
06:47
You may be born lucky,
137
389000
2000
你可能生來就很有天賦
06:49
where you know how to master your neocortical column,
138
391000
4000
或者你知道如何來操控你的新皮質功能柱
06:53
and you can play a fantastic symphony.
139
395000
2000
來演奏美妙的交響樂
06:55
In fact, there is a new theory of autism
140
397000
3000
關於自閉症有一種是
06:58
called the "intense world" theory,
141
400000
2000
稱作“激烈世界”理論
07:00
which suggests that the neocortical columns are super-columns.
142
402000
4000
它提出這些人的新皮質功能柱是超級功能柱
07:04
They are highly reactive, and they are super-plastic,
143
406000
4000
它們反應非常劇烈，而且非常有可塑性
07:08
and so the autists are probably capable of
144
410000
3000
所以自閉症患者或許可以
07:11
building and learning a symphony
145
413000
2000
構造並學習一個對我們來說
07:13
which is unthinkable for us.
146
415000
2000
無法想像的交響樂。
07:15
But you can also understand
147
417000
2000
同樣也可以理解
07:17
that if you have a disease
148
419000
2000
如果在這些功能柱中
07:19
within one of these columns,
149
421000
2000
產生任何病變，
07:21
the note is going to be off.
150
423000
2000
音調就會有偏差
07:23
The perception, the symphony that you create
151
425000
2000
這些感覺，這些你創造的交響樂
07:25
is going to be corrupted,
152
427000
2000
會被破壞，
07:27
and you will have symptoms of disease.
153
429000
3000
你會有得到有缺陷的交響曲。
07:30
So, the Holy Grail for neuroscience
154
432000
4000
所以神經科學的終極目的是
07:34
is really to understand the design of the neocoritical column --
155
436000
4000
真正地理解新皮質功能柱的設計
07:38
and it's not just for neuroscience;
156
440000
2000
這不光是對神經科學
07:40
it's perhaps to understand perception, to understand reality,
157
442000
3000
很有可能會讓人們理解感覺，理解現實
07:43
and perhaps to even also understand physical reality.
158
445000
4000
甚至理解促進對物理現實的理解
07:47
So, what we did was, for the past 15 years,
159
449000
3000
在過去的15年中我們所做的是
07:50
was to dissect out the neocortex, systematically.
160
452000
4000
系統地分解大腦新皮質
07:54
It's a bit like going and cataloging a piece of the rainforest.
161
456000
4000
這過程有點類似對一片熱帶雨林里的樹木進行分類
07:58
How many trees does it have?
162
460000
2000
一共有多少樹木？
08:00
What shapes are the trees?
163
462000
2000
它們都有些甚麼形狀？
08:02
How many of each type of tree do you have? Where are they positioned?
164
464000
3000
每一種的樹有多少？它們分布在何處？
08:05
But it's a bit more than cataloging because you actually have to
165
467000
2000
但又不只是分類，因為我們還需要
08:07
describe and discover all the rules of communication,
166
469000
4000
描述和發現它們互相交流的規則
08:11
the rules of connectivity,
167
473000
2000
連接的規則
08:13
because the neurons don't just like to connect with any neuron.
168
475000
3000
因為神經元不僅僅是與任何一個神經元細胞連接起來
08:16
They choose very carefully who they connect with.
169
478000
3000
它們有非常仔細地挑選與哪一個神經原連接
08:19
It's also more than cataloging
170
481000
3000
還有一點不同與分類的是
08:22
because you actually have to build three-dimensional
171
484000
2000
我們必須在三度空間中
08:24
digital models of them.
172
486000
2000
建立它們的數位化模型
08:26
And we did that for tens of thousands of neurons,
173
488000
2000
我們為所發現的所有不同種類的
08:28
built digital models of all the different types
174
490000
3000
神經元構建了
08:31
of neurons we came across.
175
493000
2000
成千上萬的數位模型。
08:33
And once you have that, you can actually
176
495000
2000
一旦我們有了這些模型，就可以
08:35
begin to build the neocortical column.
177
497000
4000
開始建造一個新皮質功能柱
08:39
And here we're coiling them up.
178
501000
3000
我們正將它們纏繞起來
08:42
But as you do this, what you see
179
504000
3000
當我們在這樣做的時候發現
08:45
is that the branches intersect
180
507000
2000
神經元的分支在
08:47
actually in millions of locations,
181
509000
3000
無數的地方互相交叉。
08:50
and at each of these intersections
182
512000
3000
而在每一個交叉點，
08:53
they can form a synapse.
183
515000
2000
他們都會形成一個突觸。
08:55
And a synapse is a chemical location
184
517000
2000
突觸是一個神經元之間利
08:57
where they communicate with each other.
185
519000
3000
用化學媒介互相交流的地方。
09:00
And these synapses together
186
522000
2000
這麼多突觸一起
09:02
form the network
187
524000
2000
形成了網路，
09:04
or the circuit of the brain.
188
526000
3000
或者說是大腦的迴路。
09:07
Now, the circuit, you could also think of as
189
529000
4000
這種迴路也可以
09:11
the fabric of the brain.
190
533000
2000
看成是大腦的纖維。
09:13
And when you think of the fabric of the brain,
191
535000
3000
當我們研究大腦的纖維
09:16
the structure, how is it built? What is the pattern of the carpet?
192
538000
4000
它的結構，不禁要問，它是如何構建的？按照甚麼樣的規律？
09:20
You realize that this poses
193
542000
2000
我們意識到這引出了一個
09:22
a fundamental challenge to any theory of the brain,
194
544000
4000
對任何關於大腦的理論的最根本的挑戰，
09:26
and especially to a theory that says
195
548000
2000
特別是有個理論
09:28
that there is some reality that emerges
196
550000
2000
認為現實是從大腦中
09:30
out of this carpet, out of this particular carpet
197
552000
3000
按照特定的規律
09:33
with a particular pattern.
198
555000
2000
湧現出來的。
09:35
The reason is because the most important design secret of the brain
199
557000
3000
因為大腦的設計中字重要的祕密
09:38
is diversity.
200
560000
2000
是差異化。
09:40
Every neuron is different.
201
562000
2000
每個神經元都是不同的
09:42
It's the same in the forest. Every pine tree is different.
202
564000
2000
這就好像叢林一樣，每棵松樹都是不同的
09:44
You may have many different types of trees,
203
566000
2000
或許有許多不同種類的樹
09:46
but every pine tree is different. And in the brain it's the same.
204
568000
3000
每一棵都是不同的，大腦也是這樣
09:49
So there is no neuron in my brain that is the same as another,
205
571000
3000
所以在我腦中的神經元絕對不會和別人的一樣
09:52
and there is no neuron in my brain that is the same as in yours.
206
574000
3000
也不會和你腦中的一樣
09:55
And your neurons are not going to be oriented and positioned
207
577000
3000
我們的神經元的方向和
09:58
in exactly the same way.
208
580000
2000
位置也不會是一樣的。
10:00
And you may have more or less neurons.
209
582000
2000
可能你的神經元會多一些或者少一些
10:02
So it's very unlikely
210
584000
2000
所以不大可能
10:04
that you got the same fabric, the same circuitry.
211
586000
4000
我們會有相同的纖維，相同的迴路
10:08
So, how could we possibly create a reality
212
590000
2000
所以我們怎麼可能創造出一個
10:10
that we can even understand each other?
213
592000
3000
我們在其中都能互相理解的現實？
10:13
Well, we don't have to speculate.
214
595000
2000
我們不用再繼續猜疑
10:15
We can look at all 10 million synapses now.
215
597000
3000
我們現在可以觀察這1000多萬的突觸
10:18
We can look at the fabric. And we can change neurons.
216
600000
3000
我們可以觀察這纖維，可以改變其中的神經元
10:21
We can use different neurons with different variations.
217
603000
2000
也可以使用各種各樣的神經元
10:23
We can position them in different places,
218
605000
2000
置放它們在不同的地方
10:25
orient them in different places.
219
607000
2000
讓它們朝向不同的方向
10:27
We can use less or more of them.
220
609000
2000
增加或者減少數量
10:29
And when we do that
221
611000
2000
當我們這樣去做
10:31
what we discovered is that the circuitry does change.
222
613000
3000
我們發現儘管大腦的迴路被改變
10:34
But the pattern of how the circuitry is designed does not.
223
616000
7000
但是迴路的模式是注定不變的
10:41
So, the fabric of the brain,
224
623000
2000
所以我們的大腦纖維，
10:43
even though your brain may be smaller, bigger,
225
625000
2000
可能有小有大
10:45
it may have different types of neurons,
226
627000
3000
可能有不同種類的神經元
10:48
different morphologies of neurons,
227
630000
2000
或者不同形狀的神經元
10:50
we actually do share
228
632000
3000
我們確實擁有着
10:53
the same fabric.
229
635000
2000
同樣的纖維。
10:55
And we think this is species-specific,
230
637000
2000
我們認為這是物種特有的。
10:57
which means that that could explain
231
639000
2000
這可能就解釋了為甚麼我們
10:59
why we can't communicate across species.
232
641000
2000
不能和其他物種交流溝通
11:01
So, let's switch it on. But to do it, what you have to do
233
643000
3000
讓我們開始行動。不過要進行這個計畫，我們需要做的是
11:04
is you have to make this come alive.
234
646000
2000
賦予它生命。
11:06
We make it come alive
235
648000
2000
我們用各種方程式來賦予它生命，
11:08
with equations, a lot of mathematics.
236
650000
2000
涉及到非常多的公式和算術。
11:10
And, in fact, the equations that make neurons into electrical generators
237
652000
4000
讓神經元產生電流的方程式
11:14
were discovered by two Cambridge Nobel Laureates.
238
656000
3000
是由兩位劍橋大學的諾貝爾獎得主發現的
11:17
So, we have the mathematics to make neurons come alive.
239
659000
3000
我們知道了賦予神經元生命的數學公式
11:20
We also have the mathematics to describe
240
662000
2000
我們也有用來描述神經元
11:22
how neurons collect information,
241
664000
3000
如何收集信息,
11:25
and how they create a little lightning bolt
242
667000
3000
如何使用電信號互相
11:28
to communicate with each other.
243
670000
2000
溝通交流的數學公式。
11:30
And when they get to the synapse,
244
672000
2000
當電流到達突觸的時候
11:32
what they do is they effectively,
245
674000
2000
它們會非常有效地
11:34
literally, shock the synapse.
246
676000
3000
衝擊突觸，
11:37
It's like electrical shock
247
679000
2000
就好像讓突觸釋放出
11:39
that releases the chemicals from these synapses.
248
681000
3000
化學物質的電擊一樣。
11:42
And we've got the mathematics to describe this process.
249
684000
3000
我們擁有描述這個過程的數學公式。
11:45
So we can describe the communication between the neurons.
250
687000
4000
它們可以描述神經元之間互相通信。
11:49
There literally are only a handful
251
691000
3000
實際上激活大腦新皮質
11:52
of equations that you need to simulate
252
694000
2000
互相交流只需要
11:54
the activity of the neocortex.
253
696000
2000
少量的公式就可以了。
11:56
But what you do need is a very big computer.
254
698000
3000
你所需要的是一台巨大的電腦。
11:59
And in fact you need one laptop
255
701000
2000
每一個神經元就需要
12:01
to do all the calculations just for one neuron.
256
703000
3000
一台筆記型電腦來運算。
12:04
So you need 10,000 laptops.
257
706000
2000
所以我們需要10000台筆記型電腦。
12:06
So where do you go? You go to IBM,
258
708000
2000
去哪裡找這麼多電腦？我們找到IBM,
12:08
and you get a supercomputer, because they know how to take
259
710000
2000
在那裡我們有機會使用超級電腦，因為他們知道
12:10
10,000 laptops and put it into the size of a refrigerator.
260
712000
4000
怎麼把10000台筆記型電腦放進一個冰箱大小的機櫃當中。
12:14
So now we have this Blue Gene supercomputer.
261
716000
3000
有了這台深藍基因超級電腦。
12:17
We can load up all the neurons,
262
719000
2000
我們就可以載入所有的神經元，
12:19
each one on to its processor,
263
721000
2000
每一個神經元分配到一個處理器，
12:21
and fire it up, and see what happens.
264
723000
4000
然後啓動他們
12:25
Take the magic carpet for a ride.
265
727000
3000
來觀察會發生甚麼情況。
12:28
Here we activate it. And this gives the first glimpse
266
730000
3000
這裡是啓動後的情形。這是第一手的資料
12:31
of what is happening in your brain
267
733000
2000
揭露了你的大腦接受到
12:33
when there is a stimulation.
268
735000
2000
外界的刺激後會發生甚麼。
12:35
It's the first view.
269
737000
2000
這是第一批影象。
12:37
Now, when you look at that the first time, you think,
270
739000
2000
如果是第一次面對着它，你會覺得：
12:39
"My god. How is reality coming out of that?"
271
741000
5000
“我的天哪，怎麼從這裡面看的出現實？”
12:44
But, in fact, you can start,
272
746000
3000
但實際上，就算我們
12:47
even though we haven't trained this neocortical column
273
749000
4000
還從來沒有教過這些新皮質功能柱
12:51
to create a specific reality.
274
753000
2000
來創造一個專門的現實。
12:53
But we can ask, "Where is the rose?"
275
755000
4000
我們可以問它，“玫瑰在哪裡？”
12:57
We can ask, "Where is it inside,
276
759000
2000
我們很好奇這個現實會在大腦的哪裡湧現，
12:59
if we stimulate it with a picture?"
277
761000
3000
如果我們用一張照片來刺激它。
13:02
Where is it inside the neocortex?
278
764000
2000
它會處在新皮質的裡面的甚麼位置呢？
13:04
Ultimately it's got to be there if we stimulated it with it.
279
766000
4000
如果我們用圖片刺激它，“玫瑰”最終一定會在某個地方出現。
13:08
So, the way that we can look at that
280
770000
2000
我們觀察的方法是
13:10
is to ignore the neurons, ignore the synapses,
281
772000
3000
忽略神經元，忽略突觸，
13:13
and look just at the raw electrical activity.
282
775000
2000
只看最初始的電流活動。
13:15
Because that is what it's creating.
283
777000
2000
因為這是大腦應該產生的，
13:17
It's creating electrical patterns.
284
779000
2000
它產生電流活動。
13:19
So when we did this,
285
781000
2000
當我們這樣做的時候，
13:21
we indeed, for the first time,
286
783000
2000
我們的確第一次確實地看見了
13:23
saw these ghost-like structures:
287
785000
3000
這個虛幻地結構，
13:26
electrical objects appearing
288
788000
3000
電流形成的物體
13:29
within the neocortical column.
289
791000
3000
出現在新皮質功能柱中。
13:32
And it's these electrical objects
290
794000
3000
這些電流形成的物體
13:35
that are holding all the information about
291
797000
3000
承載着所有關於任何外來的刺激
13:38
whatever stimulated it.
292
800000
3000
所形成的信息。
13:41
And then when we zoomed into this,
293
803000
2000
我們深入進這個影像，
13:43
it's like a veritable universe.
294
805000
4000
它就像是一個真正的宇宙。
13:47
So the next step
295
809000
2000
下一步將是
13:49
is just to take these brain coordinates
296
811000
4000
按照大腦中的坐標再把這
13:53
and to project them into perceptual space.
297
815000
4000
產生的現實投射到感知空間。
13:57
And if you do that,
298
819000
2000
如果這樣做，
13:59
you will be able to step inside
299
821000
2000
我們就會步入
14:01
the reality that is created
300
823000
2000
由這個機器，
14:03
by this machine,
301
825000
2000
由這個部份大腦
14:05
by this piece of the brain.
302
827000
3000
所產生的現實當中。
14:08
So, in summary,
303
830000
2000
總的來講，
14:10
I think that the universe may have --
304
832000
2000
我認為宇宙進化出了
14:12
it's possible --
305
834000
2000
一個大腦
14:14
evolved a brain to see itself,
306
836000
3000
來觀察自己可能是
14:17
which may be a first step in becoming aware of itself.
307
839000
5000
產生自我意識的第一步。
14:22
There is a lot more to do to test these theories,
308
844000
2000
要驗證這些理論還有很多工作要做，
14:24
and to test any other theories.
309
846000
3000
還有測試其他的理論。
14:27
But I hope that you are at least partly convinced
310
849000
3000
我希望至少可以說服大家
14:30
that it is not impossible to build a brain.
311
852000
3000
創造一個大腦不是天方夜譚。
14:33
We can do it within 10 years,
312
855000
2000
我們在10年內就可以做到，
14:35
and if we do succeed,
313
857000
2000
如果成功了，
14:37
we will send to TED, in 10 years,
314
859000
2000
十年內，我們就會送一個全息圖像
14:39
a hologram to talk to you. Thank you.
315
861000
3000
到TED來跟大家交流。謝謝。
14:42
(Applause)
316
864000
6000
（掌聲）
ABOUT THE SPEAKER
Henry Markram - NeuroscientistHenry 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.
Henry Markram | Speaker | TED.com