Our mission is to build a detailed, realistic computer model of the human brain. And we've done, in the past four years, a proof of concept on a small part of the rodent brain, and with this proof of concept we are now scaling the project up to reach the human brain.
我們的任務是建造 一個詳細而真實的 人類大腦的計算機模型。 在過去幾年里,我們在一小塊嚙齒類動物 的腦上做了一個 用來驗證這個概念的測試, 現在根據這個試驗我們要把項目的規模擴展到 人類大腦的規模。
Why are we doing this? There are three important reasons. The first is, it's essential for us to understand the human brain if we do want to get along in society, and I think that it is a key step in evolution. The second reason is, we cannot keep doing animal experimentation forever, and we have to embody all our data and all our knowledge into a working model. It's like a Noah's Ark. It's like an archive. And the third reason is that there are two billion people on the planet that are affected by mental disorder, and the drugs that are used today are largely empirical. I think that we can come up with very concrete solutions on how to treat disorders.
為甚麼我們要做這項工作? 有三個重要的原因。 首先,是理解人類大腦對我們來將是非常重要的 如果我們想在社會中繼續前進 我認為這是進化過程中非常關鍵的一步 第二個原因是 我們不能總是繼續拿動物做試驗 還有我們必須要把我們所有的數據和知識收錄進 一個有效的模型當中。 就好像諾亞的方舟。好像是一个文庫。 第三個原因是地球上有20億人 的生活被精神障礙所影響。 而目前所廣泛使用的藥物 都是經驗性的 我認為我們能夠對治療精神障礙 提出非常堅實的方案。
Now, even at this stage, we can use the brain model to explore some fundamental questions about how the brain works. And here, at TED, for the first time, I'd like to share with you how we're addressing one theory -- there are many theories -- one theory of how the brain works. So, this theory is that the brain creates, builds, a version of the universe, and projects this version of the universe, like a bubble, all around us.
即使是在現階段 我們可以使用大腦模型 來探究一些關於大腦 如何運作的根本的問題 在這裡,TED大會上,第一次 我想與大家分享我們如何來解決這個理論 有許多的理論 其中的一個關於大腦如何工作的理論是 所以,這個理論是大腦如何 創造、建立一個宇宙版本 並將這個宇宙版本像泡泡一樣 映射在我們的周圍。
Now, this is of course a topic of philosophical debate for centuries. But, for the first time, we can actually address this, with brain simulation, and ask very systematic and rigorous questions, whether this theory could possibly be true. The reason why the moon is huge on the horizon is simply because our perceptual bubble does not stretch out 380,000 kilometers. It runs out of space. And so what we do is we compare the buildings within our perceptual bubble, and we make a decision. We make a decision it's that big, even though it's not that big.
當然這是一個經過許多個世紀爭論的話題 但是,這是歷史上第一次我們可以實際地利用 大腦模擬來解決它, 並提出非常系統性,非常嚴謹的問題 這個理論是是不是正確的 我們感覺地平線上的月亮非常大的原因 正是因為我們的感知泡泡 並沒有延伸到三十八萬公里之外 這真是太遠了 我們所做的是在我們的感知泡泡中 將其與附近的建築做比較, 接著我們做出了一個判斷 我們判斷它是那麼大的 即使在我們眼裡它並不大
And what that illustrates is that decisions are the key things that support our perceptual bubble. It keeps it alive. Without decisions you cannot see, you cannot think, you cannot feel. And you may think that anesthetics work by sending you into some deep sleep, or by blocking your receptors so that you don't feel pain, but in fact most anesthetics don't work that way. What they do is they introduce a noise into the brain so that the neurons cannot understand each other. They are confused, and you cannot make a decision. So, while you're trying to make up your mind what the doctor, the surgeon, is doing while he's hacking away at your body, he's long gone. He's at home having tea. (Laughter)
這個例子說明的是 判斷是讓我們的感知泡泡成立 並保證它活躍的關鍵因素。 失去了判斷,你既看不見東西,也不能思考 甚麼都感覺不到 你可能以為麻醉劑的工作方式是 讓你進入酣睡的狀態 或者阻撓神經受體的運作來讓你感覺不到疼痛 但事實上大多數麻醉劑並不是這樣生效的 它們所做的是在大腦中產生一種噪音來 讓神經元細胞互相之間無法理解 它們被搞糊塗了 這樣你就不能做出判斷 所以,當你還在努力着集中注意力 要搞清楚醫生在你身上動手動腳的時候 對你的身體做了些甚麼,他早已經走人了。 他已經在家喝茶了 笑聲
So, when you walk up to a door and you open it, what you compulsively have to do to perceive is to make decisions, thousands of decisions about the size of the room, the walls, the height, the objects in this room. 99 percent of what you see is not what comes in through the eyes. It is what you infer about that room. So I can say, with some certainty, "I think, therefore I am." But I cannot say, "You think, therefore you are," because "you" are within my perceptual bubble.
當你走到一扇門前並打開它的時候 為了理解周圍環境, 你不得不做出判斷, 無數的關於房間與牆壁的大小,高度 以及房間里放的是甚麼東西的判斷。 99%你所看見的東西 並不是通過眼睛觀察到的 而是你對房間所做出的推斷 所以我一定程度上同意 ‘我思故我在’ 但是我卻不能說“你思故你在” 因為“你”這個概念是存在與我的感知泡泡之中
Now, we can speculate and philosophize this, but we don't actually have to for the next hundred years. We can ask a very concrete question. "Can the brain build such a perception?" Is it capable of doing it? Does it have the substance to do it? And that's what I'm going to describe to you today.
目前我們能推測並進行在哲理層面上研究這個理論 不過不用再這樣繼續幾百年了 我們可以問這樣一個具體的問題 大腦本身可以映射出這些感覺嗎? 它是否有這種能力做到這一點? 它有沒有足夠的物質來產生感覺? 這就是我今天想要向你們描述的主題
So, it took the universe 11 billion years to build the brain. It had to improve it a little bit. It had to add to the frontal part, so that you would have instincts, because they had to cope on land. But the real big step was the neocortex. It's a new brain. You needed it. The mammals needed it because they had to cope with parenthood, social interactions, complex cognitive functions.
在這個宇宙中經過了110億年的進化出了大腦 它需要不斷地改進 需要加上一個額部以讓你能夠擁有本能 因為生物需要應付地面上的環境 真正的巨大進步是大腦新皮質 這是一個新的大腦,你需要它 哺乳動物需要它 因為它們需要撫養幼崽 互相交流 並使用複雜的識別功能
So, you can think of the neocortex actually as the ultimate solution today, of the universe as we know it. It's the pinnacle, it's the final product that the universe has produced. It was so successful in evolution that from mouse to man it expanded about a thousandfold in terms of the numbers of neurons, to produce this almost frightening organ, structure. And it has not stopped its evolutionary path. In fact, the neocortex in the human brain is evolving at an enormous speed.
所以你可以把大腦新皮質看成是 到目前為止我們所知的 宇宙中的終極產品。 它是一個巔峰,是宇宙 所製造的最後產品。 它在進化史中是如此的成功 從老鼠到人類,大腦中神經元 的數量擴展了大約一千倍, 來構成這個幾乎是嚇人的 器官,結構。 它也沒有停止進化的步伐 實際上人類大腦中的新皮質層 一直在以驚人的速度進化。
If you zoom into the surface of the neocortex, you discover that it's made up of little modules, G5 processors, like in a computer. But there are about a million of them. They were so successful in evolution that what we did was to duplicate them over and over and add more and more of them to the brain until we ran out of space in the skull. And the brain started to fold in on itself, and that's why the neocortex is so highly convoluted. We're just packing in columns, so that we'd have more neocortical columns to perform more complex functions.
如果你深入新皮質的表面 你會發現它是由微小的模塊組成 就好像電腦里的G5處理器 但大腦中有大約100萬個模塊 它們進化的如此成功 因此我們就不斷地複製它們 不斷地在大腦中加入更多的模塊 直到用盡所有頭顱中的空間 大腦自身開始摺疊起來 這就是為甚麼新皮質是非常的捲曲的 它們不斷的往縱深發展形成功能住 這樣我們就有更多的皮質功能住 來執行更複雜的機能
So you can think of the neocortex actually as a massive grand piano, a million-key grand piano. Each of these neocortical columns would produce a note. You stimulate it; it produces a symphony. But it's not just a symphony of perception. It's a symphony of your universe, your reality. Now, of course it takes years to learn how to master a grand piano with a million keys. That's why you have to send your kids to good schools, hopefully eventually to Oxford. But it's not only education. It's also genetics. You may be born lucky, where you know how to master your neocortical column, and you can play a fantastic symphony.
你也可以將大腦新皮質 看成一架巨大的鋼琴。 一部有一百萬個琴鍵的大鋼琴 其中的每一個皮質功能住 會奏出一個音符 你對它施加刺激,它奏出一部交響曲 不過這不僅僅是感覺的交響曲 是你的宇宙的交響曲,你的現實世界 當然一個人需要花費很多年來學習如何彈奏 一架有着一百萬個琴鍵的鋼琴 這就是為甚麼你送孩子去好的學校 希望最後去到牛津大學 不過不只是教育 基因也會影響結果。 你可能生來就很有天賦 或者你知道如何來操控你的新皮質功能柱 來演奏美妙的交響樂
In fact, there is a new theory of autism called the "intense world" theory, which suggests that the neocortical columns are super-columns. They are highly reactive, and they are super-plastic, and so the autists are probably capable of building and learning a symphony which is unthinkable for us. But you can also understand that if you have a disease within one of these columns, the note is going to be off. The perception, the symphony that you create is going to be corrupted, and you will have symptoms of disease.
關於自閉症有一種是 稱作“激烈世界”理論 它提出這些人的新皮質功能柱是超級功能柱 它們反應非常劇烈,而且非常有可塑性 所以自閉症患者或許可以 構造並學習一個對我們來說 無法想像的交響樂。 同樣也可以理解 如果在這些功能柱中 產生任何病變, 音調就會有偏差 這些感覺,這些你創造的交響樂 會被破壞, 你會有得到有缺陷的交響曲。
So, the Holy Grail for neuroscience is really to understand the design of the neocoritical column -- and it's not just for neuroscience; it's perhaps to understand perception, to understand reality, and perhaps to even also understand physical reality. So, what we did was, for the past 15 years, was to dissect out the neocortex, systematically. It's a bit like going and cataloging a piece of the rainforest. How many trees does it have? What shapes are the trees? How many of each type of tree do you have? Where are they positioned?
所以神經科學的終極目的是 真正地理解新皮質功能柱的設計 這不光是對神經科學 很有可能會讓人們理解感覺,理解現實 甚至理解促進對物理現實的理解 在過去的15年中我們所做的是 系統地分解大腦新皮質 這過程有點類似對一片熱帶雨林里的樹木進行分類 一共有多少樹木? 它們都有些甚麼形狀? 每一種的樹有多少?它們分布在何處?
But it's a bit more than cataloging because you actually have to describe and discover all the rules of communication, the rules of connectivity, because the neurons don't just like to connect with any neuron. They choose very carefully who they connect with. It's also more than cataloging because you actually have to build three-dimensional digital models of them. And we did that for tens of thousands of neurons, built digital models of all the different types of neurons we came across. And once you have that, you can actually begin to build the neocortical column.
但又不只是分類,因為我們還需要 描述和發現它們互相交流的規則 連接的規則 因為神經元不僅僅是與任何一個神經元細胞連接起來 它們有非常仔細地挑選與哪一個神經原連接 還有一點不同與分類的是 我們必須在三度空間中 建立它們的數位化模型 我們為所發現的所有不同種類的 神經元構建了 成千上萬的數位模型。 一旦我們有了這些模型,就可以 開始建造一個新皮質功能柱
And here we're coiling them up. But as you do this, what you see is that the branches intersect actually in millions of locations, and at each of these intersections they can form a synapse. And a synapse is a chemical location where they communicate with each other. And these synapses together form the network or the circuit of the brain. Now, the circuit, you could also think of as the fabric of the brain. And when you think of the fabric of the brain, the structure, how is it built? What is the pattern of the carpet? You realize that this poses a fundamental challenge to any theory of the brain, and especially to a theory that says that there is some reality that emerges out of this carpet, out of this particular carpet with a particular pattern.
我們正將它們纏繞起來 當我們在這樣做的時候發現 神經元的分支在 無數的地方互相交叉。 而在每一個交叉點, 他們都會形成一個突觸。 突觸是一個神經元之間利 用化學媒介互相交流的地方。 這麼多突觸一起 形成了網路, 或者說是大腦的迴路。 這種迴路也可以 看成是大腦的纖維。 當我們研究大腦的纖維 它的結構,不禁要問,它是如何構建的?按照甚麼樣的規律? 我們意識到這引出了一個 對任何關於大腦的理論的最根本的挑戰, 特別是有個理論 認為現實是從大腦中 按照特定的規律 湧現出來的。
The reason is because the most important design secret of the brain is diversity. Every neuron is different. It's the same in the forest. Every pine tree is different. You may have many different types of trees, but every pine tree is different. And in the brain it's the same. So there is no neuron in my brain that is the same as another, and there is no neuron in my brain that is the same as in yours. And your neurons are not going to be oriented and positioned in exactly the same way. And you may have more or less neurons. So it's very unlikely that you got the same fabric, the same circuitry.
因為大腦的設計中字重要的祕密 是差異化。 每個神經元都是不同的 這就好像叢林一樣,每棵松樹都是不同的 或許有許多不同種類的樹 每一棵都是不同的,大腦也是這樣 所以在我腦中的神經元絕對不會和別人的一樣 也不會和你腦中的一樣 我們的神經元的方向和 位置也不會是一樣的。 可能你的神經元會多一些或者少一些 所以不大可能 我們會有相同的纖維,相同的迴路
So, how could we possibly create a reality that we can even understand each other? Well, we don't have to speculate. We can look at all 10 million synapses now. We can look at the fabric. And we can change neurons. We can use different neurons with different variations. We can position them in different places, orient them in different places. We can use less or more of them. And when we do that what we discovered is that the circuitry does change. But the pattern of how the circuitry is designed does not. So, the fabric of the brain, even though your brain may be smaller, bigger, it may have different types of neurons, different morphologies of neurons, we actually do share the same fabric. And we think this is species-specific, which means that that could explain why we can't communicate across species.
所以我們怎麼可能創造出一個 我們在其中都能互相理解的現實? 我們不用再繼續猜疑 我們現在可以觀察這1000多萬的突觸 我們可以觀察這纖維,可以改變其中的神經元 也可以使用各種各樣的神經元 置放它們在不同的地方 讓它們朝向不同的方向 增加或者減少數量 當我們這樣去做 我們發現儘管大腦的迴路被改變 但是迴路的模式是注定不變的 所以我們的大腦纖維, 可能有小有大 可能有不同種類的神經元 或者不同形狀的神經元 我們確實擁有着 同樣的纖維。 我們認為這是物種特有的。 這可能就解釋了為甚麼我們 不能和其他物種交流溝通
So, let's switch it on. But to do it, what you have to do is you have to make this come alive. We make it come alive with equations, a lot of mathematics. And, in fact, the equations that make neurons into electrical generators were discovered by two Cambridge Nobel Laureates. So, we have the mathematics to make neurons come alive. We also have the mathematics to describe how neurons collect information, and how they create a little lightning bolt to communicate with each other. And when they get to the synapse, what they do is they effectively, literally, shock the synapse. It's like electrical shock that releases the chemicals from these synapses.
讓我們開始行動。不過要進行這個計畫,我們需要做的是 賦予它生命。 我們用各種方程式來賦予它生命, 涉及到非常多的公式和算術。 讓神經元產生電流的方程式 是由兩位劍橋大學的諾貝爾獎得主發現的 我們知道了賦予神經元生命的數學公式 我們也有用來描述神經元 如何收集信息, 如何使用電信號互相 溝通交流的數學公式。 當電流到達突觸的時候 它們會非常有效地 衝擊突觸, 就好像讓突觸釋放出 化學物質的電擊一樣。
And we've got the mathematics to describe this process. So we can describe the communication between the neurons. There literally are only a handful of equations that you need to simulate the activity of the neocortex. But what you do need is a very big computer. And in fact you need one laptop to do all the calculations just for one neuron. So you need 10,000 laptops. So where do you go? You go to IBM, and you get a supercomputer, because they know how to take 10,000 laptops and put it into the size of a refrigerator. So now we have this Blue Gene supercomputer. We can load up all the neurons, each one on to its processor, and fire it up, and see what happens. Take the magic carpet for a ride.
我們擁有描述這個過程的數學公式。 它們可以描述神經元之間互相通信。 實際上激活大腦新皮質 互相交流只需要 少量的公式就可以了。 你所需要的是一台巨大的電腦。 每一個神經元就需要 一台筆記型電腦來運算。 所以我們需要10000台筆記型電腦。 去哪裡找這麼多電腦?我們找到IBM, 在那裡我們有機會使用超級電腦,因為他們知道 怎麼把10000台筆記型電腦放進一個冰箱大小的機櫃當中。 有了這台深藍基因超級電腦。 我們就可以載入所有的神經元, 每一個神經元分配到一個處理器, 然後啓動他們 來觀察會發生甚麼情況。
Here we activate it. And this gives the first glimpse of what is happening in your brain when there is a stimulation. It's the first view. Now, when you look at that the first time, you think, "My god. How is reality coming out of that?" But, in fact, you can start, even though we haven't trained this neocortical column to create a specific reality. But we can ask, "Where is the rose?" We can ask, "Where is it inside, if we stimulate it with a picture?" Where is it inside the neocortex? Ultimately it's got to be there if we stimulated it with it.
這裡是啓動後的情形。這是第一手的資料 揭露了你的大腦接受到 外界的刺激後會發生甚麼。 這是第一批影象。 如果是第一次面對着它,你會覺得: “我的天哪,怎麼從這裡面看的出現實?” 但實際上,就算我們 還從來沒有教過這些新皮質功能柱 來創造一個專門的現實。 我們可以問它,“玫瑰在哪裡?” 我們很好奇這個現實會在大腦的哪裡湧現, 如果我們用一張照片來刺激它。 它會處在新皮質的裡面的甚麼位置呢? 如果我們用圖片刺激它,“玫瑰”最終一定會在某個地方出現。
So, the way that we can look at that is to ignore the neurons, ignore the synapses, and look just at the raw electrical activity. Because that is what it's creating. It's creating electrical patterns. So when we did this, we indeed, for the first time, saw these ghost-like structures: electrical objects appearing within the neocortical column. And it's these electrical objects that are holding all the information about whatever stimulated it. And then when we zoomed into this, it's like a veritable universe.
我們觀察的方法是 忽略神經元,忽略突觸, 只看最初始的電流活動。 因為這是大腦應該產生的, 它產生電流活動。 當我們這樣做的時候, 我們的確第一次確實地看見了 這個虛幻地結構, 電流形成的物體 出現在新皮質功能柱中。 這些電流形成的物體 承載着所有關於任何外來的刺激 所形成的信息。 我們深入進這個影像, 它就像是一個真正的宇宙。
So the next step is just to take these brain coordinates and to project them into perceptual space. And if you do that, you will be able to step inside the reality that is created by this machine, by this piece of the brain. So, in summary, I think that the universe may have -- it's possible -- evolved a brain to see itself, which may be a first step in becoming aware of itself. There is a lot more to do to test these theories, and to test any other theories. But I hope that you are at least partly convinced that it is not impossible to build a brain. We can do it within 10 years, and if we do succeed, we will send to TED, in 10 years, a hologram to talk to you. Thank you. (Applause)
下一步將是 按照大腦中的坐標再把這 產生的現實投射到感知空間。 如果這樣做, 我們就會步入 由這個機器, 由這個部份大腦 所產生的現實當中。 總的來講, 我認為宇宙進化出了 一個大腦 來觀察自己可能是 產生自我意識的第一步。 要驗證這些理論還有很多工作要做, 還有測試其他的理論。 我希望至少可以說服大家 創造一個大腦不是天方夜譚。 我們在10年內就可以做到, 如果成功了, 十年內,我們就會送一個全息圖像 到TED來跟大家交流。謝謝。 (掌聲)