How the Brain Is Computing the Mind

作者:Ed Boyden @ 2016-02-12

The history of science has shown us that you need the tools first. Then you get the data. Then you can make the theory. Then you can achieve understanding.

Ed Boyden is a professor of biological engineering and brain and cognitive sciences at the MIT Media Lab and the MIT McGovern Institute. He leads the Synthetic Neurobiology Group.
Ed Boyden是MIT媒体实验室和MIT麦戈文研究院的一名生物工程和大脑与认知科学方向的教授。他领导着MIT合成神经生物学研究小组。


How can we truly understand how the brain is computing the mind? Over the last 100 years, neuroscience has made a lot of progress. We have learned that there are neurons in the brain, we have learned a lot about psychology, but connecting those two worlds, understanding how these computational circuits in the brain in coordinated fashion are generating decisions and thoughts and feelings and sensations, that link remains very elusive. And so, over the last decade, my group at MIT has been working on technology, ways of seeing the brain, ways of controlling brain circuits, ways of trying to map the molecules of the brain.


At this point, what I’m trying to figure out is what to do next. How do we start to use these maps, use these dynamical observations and perturbations to link the computations that these circuits make, and things like thoughts and feelings and maybe even consciousness?


There are a couple of things that we can do. One idea is simply to go get the data. A lot of people have the opposite po(more...)

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How the Brain Is Computing the Mind 大脑是如何计算意识的 作者:Ed Boyden @ 2016-02-12 译者:Veidt(@Veidt) 校对:混乱阈值(@混乱阈值) 来源:Edge,http://edge.org/conversation/ed_boyden-how-the-brain-is-computing-the-mind The history of science has shown us that you need the tools first. Then you get the data. Then you can make the theory. Then you can achieve understanding. 科学的历史告诉我们,首先你需要合适的工具,然后去收集数据,之后你就可以创造理论了,最终你才能获得对事物的理解。 Ed Boyden is a professor of biological engineering and brain and cognitive sciences at the MIT Media Lab and the MIT McGovern Institute. He leads the Synthetic Neurobiology Group. Ed Boyden是MIT媒体实验室和MIT麦戈文研究院的一名生物工程和大脑与认知科学方向的教授。他领导着MIT合成神经生物学研究小组。 HOW THE BRAIN IS COMPUTING THE MIND 大脑是如何计算意识的 How can we truly understand how the brain is computing the mind? Over the last 100 years, neuroscience has made a lot of progress. We have learned that there are neurons in the brain, we have learned a lot about psychology, but connecting those two worlds, understanding how these computational circuits in the brain in coordinated fashion are generating decisions and thoughts and feelings and sensations, that link remains very elusive. And so, over the last decade, my group at MIT has been working on technology, ways of seeing the brain, ways of controlling brain circuits, ways of trying to map the molecules of the brain. 我们如何才能真正地认识到大脑是如何计算着意识的?在过去百年中,神经科学研究在这方面获得了长足进步。我们已经了解到大脑中有着巨量的神经元,也对心理学有了许多认识,但想要把这两个领域联系起来,去理解这些大脑中的计算电路是如何通过合作来产生决策、思想、感觉和情感,则并非易事,人们目前对其中的关联仍知之甚少。正因此,在过去十年中,我在MIT领导的研究小组一直致力于研究相关方面的技术,以期找到观测大脑,控制脑内回路,以及在大脑内部定位分子的方法。 At this point, what I’m trying to figure out is what to do next. How do we start to use these maps, use these dynamical observations and perturbations to link the computations that these circuits make, and things like thoughts and feelings and maybe even consciousness? 目前,我正在试图弄清我们下一步应该做些什么。我们能够如何利用这些分子定位图——也就是一些动态的观测和扰动——来将脑内电路的计算过程与思想,感觉,甚至是意识这些东西联系在一起? There are a couple of things that we can do. One idea is simply to go get the data. A lot of people have the opposite point of view. You want to have an idea about how the brain computes, the concept of how the mind is generating thoughts and feelings and so forth. Marvin Minsky, for example, is very fond of thinking about how intelligence and artificial intelligence can be arrived at through sheer thinking about it. 的确有一些我们能做的事情。其中的一个主意就只是从这些分子定位图中获取数据。但有很多人持有相反的意见。他们希望获得关于大脑是如何进行计算的,意识是如何产生出思想和感觉的,以及诸如此类的一些观点和概念。例如,Marvin Minsky(译者注:马文·明斯基,计算机科学家,人工智能领域的奠基人之一)就非常热衷于通过纯粹的思考来解决智能和人工智能是如何实现的这个问题。【编注:这句原文的字面意思是『Marvin Minsky就非常热衷于思考如何能够通过纯粹的思考来解决智能和人工智能是如何实现的这个问题』,Minsky的工作重点好像不是这种二阶思考,疑似作者笔误。】 On the other hand, and it’s always dangerous to make analogies and metaphors like this, but if you look at other problems in biology like, what is life? how do species evolve? and so forth, people forget that there are huge amounts, centuries sometimes but at least decades of data that was collected before those theories emerged. 但另一方面,做出这样的类比和隐喻总是十分危险的。如果你去看看生物学中的其它一些问题,例如“什么是生命?”“物种是如何进化的?”以及类似的种种,人们在提这些问题的时候忘记了一个事实:那就是在理论出现之前,研究者们已经收集了大量的数据,数据的时间跨度有时长达数个世纪,至少也有几十年。 Darwin roamed the Earth looking at species, looking at all sorts of stuff until he wrote the giant tome, On the Origins of Species. Before people started to try to hone in on what life is, there was the tool development phase: people invented the microscope. 达尔文在他的环球旅行中观察了许多物种,他仔细观察着关于这些物种的一切,最终写出伟大的巨著《物种起源》。在人们开始尝试研究“生命是什么”这个问题之前,必经的一步是工具的发展:有人发明了显微镜。 People started looking at cells and watching them divide and so forth, and without those data, it would be very hard to know that there were cells at all, that there were these tiny building blocks, each of which was a self-compartmentalized, autonomous building block of life. 在那之后人们才开始观察细胞,观察它们的分裂和其它种种行为,如果没有这些数据,人们甚至很难发现细胞的存在,而生命正是由这些微小的,独立自治的“小积木”搭建而来的。 The approach I would like to take is to go get the data. Let’s see how the cells in the brain can communicate with each other. Let’s see how these networks take sensation and combine that information with feelings and memories and so forth to generate the outputs, decisions and thoughts and movements. And then, one of two possibilities will emerge. 在这里,我想采用的方法是从其中获取数据。让我们来看看大脑中的细胞是如何彼此交流信息的,看看这些细胞构成的网络如何获得感觉,并将这种信息与感情,记忆,还有其它类似的东西组合在一起来生成输出信号,决策,思想和动作。之后,我们将会看到两种可能性之一的出现。 One will be that patterns can be found, motifs can be mined, you can start to see sense in this morass of data. The second might be that it’s incomprehensible, that the brain is this enormous bag of tricks and while you can simulate it brute force in a computer, it’s very hard to extract simpler representations from those datasets. 一种可能性是,我们可以从中发现一些模式,挖掘出一些主旨,并开始从这堆乱糟糟的数据中寻找一些理论了。另一种可能性则是,我们仍然无法理解其中的奥妙,由于大脑中所包含的复杂机制是如此之多,虽然我们可以简单粗暴地在计算机中进行模拟,但想要从这些数据集中抽取出一些相对简单一点的模型仍然是非常困难的。 In some ways, it has to be the former because it’s strange that we can predict our behaviors. People walk through a city, they communicate, they see things, there are commonalities in the human experience. So that’s a clue; that’s a clue that it’s not an arbitrary morass of complexity that we’re not going to ever make sense of. 从某种角度说,第一种可能性应该是对的,虽然很奇怪,但人们的确已经获得了预测自身行为的能力。人们会在城市中穿行,会相互交流,会看到形形色色的事物,在人类的生存体验中存在诸多这样的共同之处。所以现在我们至少有了点线索,我们知道自己所面临的并不是一堆混乱到我们完全无法搞清楚其中意义的随机复杂性。 Of course, being a pessimist, we should still always hold open the possibility that it will be incomprehensible. But the fact that we can talk in language, that we see and design shapes and that people can experience pleasure in common, that suggests that there is some convergence that it’s not going to be infinitely complex and that we will be able to make sense of it. 当然,从悲观主义者的视角来看,我们仍然需要对第二种可能性抱以开放的态度,也就是我们的确可能无法理解这个问题。但人们能够使用语言交谈,能够辨别并设计不同形状,还能够获得共同的愉悦体验,这些事实都表明我们所要研究的对象是存在一些收敛性质的,至少我们所面对的不是无穷无尽的复杂性,而我们也的确能够从中找到一些规律。 Biology and brain science are not fundamental sciences in the sense that physics is. In physics, there are particles and there are forces, and you could write down a very short list of those things. But if you’re thinking about the brain and the brain is going to have these cells called neurons, and the neurons have all these molecules that generate their electrical functions and their chemical exchanges of information, those are encoded for by the genome. 生物学和脑科学并不是像物理一样的基础科学。在物理学中有质点和各种力的概念,你可以很容易地将所有这些概念列在一张很短的清单上。但想想大脑吧,大脑中有一些被称为神经元的细胞,这些神经元又是由许多不同的分子构成的,神经元正是靠这些分子来产生电信号并通过化学递质交流信息,而所有这些分子则都被编码在了基因组中。 In the genome, we have, depending on who you ask, 20,000- to 30,000-odd genes, and those genes produce gene products like proteins, and those proteins generate the electrical potentials of neurons and they specify at least some parts of the wiring. The way that I look at it is we’re going to want to understand the brain in terms of these fundamental building blocks, and we can always try to ignore some detail, this concept of the abstraction layer. 基因组中大约有2万到3万个基因(研究者们在基因的具体数目这个问题上存在一些分歧),这些基因能够生产出像蛋白质这样的基因产物,而其中一些蛋白质又生成了神经元中的高低电位,因而它们也指定了神经电路中至少某些部分的构成方式。关于大脑,我认为目前我们所要了解的是这些基础的组成部件,而我们总是可以尝试去忽略掉一些细节,从抽象层上去理解其中的概念。 Can we ignore everything below a certain level of description and just focus on the higher level concepts? But modern neuroscience is now almost 130 years old, since the neuron was discovered, and so far, the attempts to ignore below certain levels of description have not yielded universally accepted and explanatory theories of how our brains are computing our thoughts or feelings or movements. 我们真的能够忽略掉某个特定描述层次之下的一切,而仅仅把注意力集中在更高层次的概念上吗?自从神经元被发现至今,现代神经科学的发展已经有近130年历史了,但那些尝试忽略掉某些特定描述层次以下的微观机制的努力,至今还没能产生出能够被广泛接受并具有解释力的理论,来回答大脑是如何计算出我们的思想、感受或是动作的这些问题。 The way that we approach things is pretty radically different from the past. The premise that I launched my research group at MIT on was that we needed new technology. The reason people are shying away from these very, very detailed measurements of brain function, getting the deep data, was because we didn’t have the tools. The history of science has shown us that you need the tools first. Then you get the data. Then you can make the theory. Then you can achieve understanding. No theory with no technology. It’s very difficult to know that you’ve solved it. 而我们团队目前处理问题的方式与之前的则有着非常明显的区别。我在MIT成立这个研究小组所基于的一个前提就是我意识到我们需要新的技术。人们之所以会回避这些对于大脑功能非常细节化的测量,原因在于我们并没有获得合适的工具。科学的历史告诉我们,首先你需要合适的工具,然后才能去收集数据,之后你就可以创造理论了,而最终你将获得对事物的理解。没有合适的技术就无法创造出好的理论。因为你很难确定自己的理论是否真的解决了问题。 Before Newton’s Laws, there were lots of people like Kepler and Galileo who were watching the planets, and they had decades and decades of data. Why don’t we have that for the brain? We need tools for the brain like the telescope and the microscope, and now, we need to collect the data, ground truth data, if you will, where we can see all those cells and molecules in action, and then, we’re going to see a renaissance in our ability to think of and learn about the brain at a very detailed level, but to extract true insight from these datasets. 在牛顿定律之前,很多人都曾经观察过行星的运动(例如开普勒和伽利略),而他们已经积累了数十年的数据。在针对大脑的研究中,我们为什么不做相同的事情呢?在对大脑的研究中,我们首先需要找到像天文学中的望远镜和生物学中的显微镜一样的有效工具,之后我们所要做的就是收集真实的基础数据,如果你愿意的话,我们现在已经能从数据中看到所有的那些细胞和分子的运动,之后,我们将能够欣喜地看到自己获得了从非常细节的层次上思考和学习大脑的能力,同时也能够从那些收集到的数据集中获得一些真正的洞见。 Let’s think for a second about the hypothesis that biology is not a fundamental science. If you think about books like The Structure of Scientific Revolutions, this and other attempts to explain the path of science, we often have these models: here’s my hypothesis, somebody comes along and disproves it, and if it’s a big enough disproof, you get a revolution. 让我们花点时间想想“生物学不是一门基础科学”这个假说。想象一下托马斯·库恩的《科学革命的结构》这本书,还有其他一些试图解释科学发展之路径的著作,在这些书中我们通常会看到这样的模式:首先我提出了一个假说,然后有人出来对这个假说提出反对意见,如果这个反对意见足够重大,那么这就可以被称之为一项“革命”。 But let’s think about biology: suppose I want to figure out how a gene in the genome relates to an emergent property like intelligence or behavior or a disease like Alzheimer's. There are so many genes in the genome, most hypotheses are probably wrong just by chance. What are the chances that you got the exact gene that’s most important for something? And even if you did, how do you know what other genes modulate it? It’s an incredibly complicated network. 但让我们想想生物学吧:假设我想要找到基因组中的某个基因是如何与某个重要属性(例如智力、行为或者是阿尔茨海默症这样的疾病)发生关联的。基因组中的基因数量如此之多,从概率上看,大多数假说大概都是错误的。对于某种属性,你能准确地找到对它而言最重要的那个基因的概率有多大?而即使你找到了这个基因,你又如何知道有哪些其它的基因会对它发挥调控作用?这个网络的复杂程度简直令人难以置信。 If you started thinking of how different genes of the genome, how their products interact to generate functions in cells or in neurons or networks, it’s a huge combinatorial explosion. Most hypotheses about what a gene is doing, or especially what a network of genes is doing, much less a network of cells in the brain, they’re going to be incorrect. That’s why it’s so important to get these ground truth descriptions of the brain. 而如果你开始思考基因组中的不同基因所生产出的基因产物之间是如何通过互动在细胞中,或者神经元和神经网络中,产生不同的功能的,那么你将面临一个组合大爆炸了。关于某个基因的功能是什么,尤其是某个基因网络的功能是什么,人们所提出的绝大多数假说都将被证明是错误的,更不用提大脑中的某个细胞网络的功能是什么了。这就是为什么我们需要获得真实的关于大脑的基础性描述的原因。 Why can't we map the circuits and see how the molecules are configured, and turn on or off different cells in the brain and see how they interact? Once you have those maps, we can make much better hypotheses. I don’t think the maps of the brain equal the understanding of the brain, but the maps of the brain can help us make hypotheses and make them less assumption-prone, make them less likely to be wrong. 为什么我们不能绘制出大脑中的神经电路并看看其中的分子是如何装配的,然后通过打开或者关闭大脑中的不同细胞来看看它们是如何交互的呢?一旦你能够绘制出这些电路图,我们就能够提出比现在好得多的假说了。我认为这种将大脑比作一张神经电路图的观点并不等于对大脑的正确理解,但将大脑比作神经电路图的做法的确能够帮助我们提出更好的假说,并让这些假说变得不那么依赖于前提假设,同时也降低它们的错误概率。 One thing that I hope a circuit description of the brain will help us understand about humanity is, as we know from psychology, there are countless unconscious processes that happen. One of the most famous such experiments is you can find regions of the brain or even single cells in the brain that will be active even seconds before people feel like they’re making a consciously-willed decision. That leads to what you might maybe slightly jokingly say, we have free will but we’re not conscious of it. Our brains are computing what we’re going to do, and that we’re conscious after the fact is one interpretation of these studies. 我希望这种关于大脑的神经电路式描述能够帮助我们理解人性,而其中一个方面就是我们已经从心理学中所了解到的无数无意识过程的发生。在这方面最著名的实验之一就是人们发现大脑中的某些区域或者甚至是某些细胞会在人们感受到自己正在做出一个意识清醒的决定的数秒之前就开始变得活跃。这让我们能够半开玩笑地说,人们的确拥有自由意志,只是自己还没意识到而已。对这些研究结果的一种解读方式是,大脑已经计算出了我们会在接下来做什么,而我们是在之后才意识到这一点。 What I suggest though is that if we peek under the hood, if we look at what the brain is computing, we might find evidence for the implementation or the mechanisms of feelings and thoughts and decisions that are completely inaccessible if we only look at behavior, or if we only look at the kinds of things that people do, whereas if you find evidence that something you’re about to do, something you’re about to consciously decide, your brain already has that information in advance. Wouldn’t it be interesting to know what’s generating that information? Maybe there are free will circuits, quote, unquote, in the brain that are generating these decisions. 但我想要建议的是,如果我们试着去一窥面纱之下的风景,去看看大脑到底在计算些什么的话,我们也许能够找到一些关于感受、思想和决策的实现方法或机制的证据,而仅仅通过观察人们的行为或是人们会做哪些事情是完全无法获得这些证据的,因为当你意识到你将会做某件事情,或者是清醒地做出某个决定的时候,你的大脑已经提前获得了这些信息。了解是什么产生了这些信息难道不是一件很有趣的事情吗?也许在大脑中存在着生成“ 自由意志”的神经电路来负责产生这些决策呢。 We know all sorts of other things that occur, feelings that our brains are generating, and we have no idea about what’s causing them. There are very famous examples where somebody who has an injury to a part of their brain that is responsible for conscious vision, but you tell them when you see something, I want you to have a certain feeling, or when you see something, I want you to imagine a certain kind of outcome, and people will have that occur even though they’re not consciously aware of what they’re seeing. 我们还知道很多大脑中会发生的其它事情,例如大脑会产生感受,但我们完全不知道是什么导致了这些事情的发生。在这些方面有些著名的例子,例如有些人大脑中负责有意识的视力的部分受到了损伤,但如果你告诉他们“当你看见某种东西的时候,我希望你能产生某种特定的感受”,或者“当你看见某种东西的时候,我希望你能够想象某种特定的结果”,那么他们就真的会产生这种感受或是想象出这种特定的结果,即使他们并不能清醒地意识到自己看见了什么。 There is so much processing that we have no access to, and yet, it’s so essential to the human condition for and decisions and thoughts, and if we can get access to the circuits that generate them, that might be the fastest route to understanding those aspects of the human condition. 至今我们仍然没有任何途径去研究大脑中很大一部分处理过程,然而它们对于人类的决策和思维至关重要,一旦我们能够找到办法去研究那些生成它们的神经电路,那么这也许将成为理解人类状态中的这些方面最快捷的途径。 I’ve been thinking a lot over the last decade primarily about the technology that helped us figure out what we need to understand about the brain in terms of circuits and how they work together. But now that those tools are maturing, I’m thinking a lot about how we use these tools to understand what we all care about. 在过去十年中,我花了很多时间去思考如何发展那些能够帮助我们从神经电路和它们共同工作的机制方面去理解大脑的技术。现在这些工具开始慢慢成熟了,我会花更多时间去思考我们能够如何利用这些工具去理解我们共同关心的那些问题。 Up until now, we mostly have been giving our tools out to other neuroscientists to use. We’ve been focusing very much on technology invention, and other groups have been discovering profound things about the brain. I’ll just give you a couple of examples. 目前为止,我们主要还是在将这些工具提供给其他的一些神经科学家使用。我们主要关注的是技术的研发,而其它一些研究小组则致力于探索关于大脑的一些意义重大的事情。这里我将举两个例子。 There’s a group at Caltech and they use one of our technologies, a technology that makes neurons activatable by pulses of light. They put these molecules into neurons deep, deep in the brain, and when you shine light, those neurons are electrically active, just like when they’re normally being used. They found that there are neurons deep in the brain that trigger aggression or violence in mice, so they would activate these neurons and the mice would attack whatever was next to them, even if it was just a rubber glove. 加州理工大学的一个研究小组使用了我们的一种技术,这种技术能够通过光脉冲让神经元处于可激活状态。他们将这些分子放置在大脑非常深处的神经元中,当你发出光信号时,这些神经元就会处于电活跃状态,就像它们平时发挥作用时一样。他们发现大脑深处的某些神经元会触发小白鼠的攻击性或暴力倾向,于是他们就激活了这些神经元,而之后小白鼠就会攻击它们身旁的一切东西,即使是一只橡胶手套。 I find it fascinating to think about something as ethically charged, as essential to the human condition, as involved with our justice system and all sorts of stuff, as violence. You can find a very small cluster of neurons that, when they’re activated, are sufficient to trigger an act of aggression or violence. So of course, now, the big question is what neurons connect to those? Are they violence detectors? Oh, here is the set of stimuli that makes us now decide, oh, I should go attack this thing next to me even if it’s just a glove. 我发现思考诸如暴力之类概念是一件非常令人着迷的事情,它们在道德上受到谴责,但对人类具有重要影响,并且被包含在我们的司法系统中。你能够找到一小簇神经元,当它们被激活时,就足以触发攻击性或是暴力行为。那么当然,现在最大的问题就是哪些神经元是与它们相关的?这些神经元能够用于探测暴力行为的发生吗?“噢,这儿有一组让我们马上作出决定的刺激信号,噢,我应该去攻击我身边的这个东西了,即使它是一只手套。” And then, of course, where do these neurons project? What are they driving? Are they driving an emotion, and downstream of that emotion comes the violent act? Or are they just driving a motor command: go attack the glove next to you? For the first time, you can start to activate very specific sets of cells deep in the brain and have them trigger an observable behavior, but you can also ask, what are these cells getting, what are these cells sending messages to, and looking at the entire flow of information. 然后,理所当然的问题就是这些神经元是在哪里得到表现的?它们驱动的又是什么?是它们驱动了某种情感,然后这种情感顺流而下的发展导致了暴力行为的发生吗?或者说它们只是驱动了某种机械指令:攻击你身边的那只手套!?有史以来第一次,你能够去激活大脑深处的那些特定的细胞组,并且触发它们的某种可观测的行为,但同时你还可以发问,这些细胞获得了什么,它们在向哪些对象发送消息,而你能够看到这其中完整的信息流。 I’ll give you another example that is fascinating. One of my colleagues at MIT, Susumu Tonegawa, trained mice on a learning task, so that certain neurons in the brain become activatable by light. They used some genetic tricks to do that. 还有另一个令人着迷的例子。我在MIT的一位同事,利根川进(译者注:日本生物学家,因“发现抗体多样性的遗传学原理”获1987年诺贝尔生理学或医学奖)用一个学习任务来训练小白鼠,使小白鼠脑内的某些特定神经元处于可被光信号激活的状态。他们使用了一些基因技巧来进行这个实验。 Now, what happens is those mice can be doing something else much later, they shine light on the brain, and those neurons, the ones that had been activated earlier when they were learning, they get reactivated and the mice make a memory recall. It’s like they were there in the earlier place and time. 而之后所发生的事情是,那些小白鼠在神经元处于该状态很久之后可能正在做着某些别的事情,但一旦研究者们在小白鼠的脑内点亮光信号,那些之前在它们进行学习任务时就已经被激活的神经元会被重新激活,而那些小白鼠则经历了一次记忆唤醒的过程,就像它们还处在之前的时间和地点一样。 That’s interesting because for the first time, they can show that you can cause the recall of a specific memory, and now they are doing all sorts of interesting things. For example, you can activate those cells again, and let’s say that’s a happy memory; let’s say it’s associated with pleasure or a reward. 这个例子的有趣之处在于,这些研究者们第一次证明了人们的确可以唤醒某段特定的记忆,而现在他们仍然在做着各种有趣的事情。例如,你能够再一次激活那些神经元,我们假设那代表着一段快乐的回忆,或者说它与愉悦感或是奖励是联系在一起的。 They have shown that that can have antidepressant effects, that you can have an animal recall, a memory when you shine light on certain neurons, now the memory that is recalled triggers happy emotions; this is how they interpreted it. And that can counteract other stressors or other things that make the animal normally feel not so good. 这些研究者们已经证明了这种记忆唤醒能够产生抗抑郁的效果,他们对此的解释是,你能够通过用光信号照射某些特定的神经元来唤醒动物的某段记忆,这段被唤醒的记忆会触发动物的一些欢快的情感,这些情感能够抵抗某些压力源或其它一些通常会让动物产生不良感受的东西。 Literally, hundreds and hundreds of groups are using this technology that we developed for activating neurons by light to trigger things that are of clinical and maybe even sometimes philosophical interest. 毫不夸张地说,现在已经有数以百计的研究小组采用了我们开发的这种通过光信号来激活神经元的技术,他们使用这种技术来触发一些具有临床意义,有时甚至具有哲学意义的东西。


I studied chemistry and electrical engineering and physics in college, and decided that I cared about understanding the brain. To me, that was the big unknown. This will seem kind of cheesy, but I started thinking about how our brains understand the universe, and the universe, of course, gives us things like the laws of physics upon which are built chemistry and biology, upon which is built our brain. It’s kind of a loop. I was trying to think about what to do in a career; I thought, what’s the weak point in the loop? And it seemed like the brain was very unknown. 我曾在大学里学过化学,电气工程和物理学,而我最终认定自己最牵挂的是对于大脑的理解。对我来说,那是一个大大的未知领域。下面这段话可能看起来有点肉麻,但当时我开始思考我们的大脑是如何去理解宇宙的,正是宇宙给了我们物理定律,而化学和生物又是建立在这些物理定律的基础上的。这某种程度上构成了一个环。而我试图去思考在我的职业生涯中应该做些什么,我当时所想的是,在这些环节中最弱的一个是什么?看起来大脑是存在最多未知的地方。 I was very impressed by people who would go build technology to tackle big problems, sometimes very simple technology. All the chemists in the 1700s and 1800s who built ways of looking at pressure and volume and stoichiometry, without that, it’s inconceivable that we would have things like the Periodic Table of the Elements and quantum mechanics and so forth. 那些愿意去创造技术以解决重大问题的人们给我留下了深刻的印象,而有时那些技术其实非常简单。例如所有在18世纪和19世纪尝试用各种方法测量压力、体积和其他化学量的科学家们,没有他们的工作,难以想象我们今天能够拥有元素周期表,量子力学和其它的一些科学工具。 What stuck out in my mind was you need to have that technological era, and that then gives you the data that you want, that then yields the most parsimonious and elegant representations of knowledge. And for neuroscience, it seemed like we had never gone through that technological era. There were bits and pieces, don’t get me wrong, like electrodes and the MRI scanner, but never a concerted effort to be able to map everything, record all the dynamics, and to control everything. And that’s what I wanted to do. 当时我脑子里一个挥之不去的念头就是,你只有经历过一个那样的技术时代,才能获得自己想要的数据,然后才能从中产生出最精细最优雅的知识。对于神经科学来说,看起来我们还从未经历过一个那样的技术时代。别误会我的意思,当然我们已经拥有了一些零散的技术手段,例如电极技术和核磁共振扫描仪,但我们从未同心协力去努力获得定位大脑内部发生的一切,记录脑内的所有动态过程,并控制大脑的所有活动的能力。而那正是我想要做的事情。 At the time I started graduate school at Stanford, I went around telling everybody I wanted to build technologies for the brain and to bring the physical sciences into neuroscience. A lot of people thought it was a bad idea, frankly, and I think the reason why was at the time, many people who are physicists and inventors were trying to build tools for studying the brain. But they were thinking forwards from what was fun for them to do, and not backwards from the deep mysteries of the brain. 当时我刚开始在斯坦福大学的研究生生涯,我告诉身边的所有人我希望为探索大脑开发技术手段并将神经科学变成一门自然科学。老实说,当时有很多人都不认为这是个好主意,我觉得他们这么想的原因是,在当时,有许多物理学家和发明家都在试图为研究大脑创造工具,但是他们所想的都是向前看,去研究那些对他们而言有趣的事情,而并没有回过头去探索那些埋藏在大脑深处的谜题。 The key insight that I got during graduate school was if you don’t think backwards from the big mysteries of the brain, and you only think forwards from what you find fun in physics, the technologies you built might not be that important. They might not solve a big problem. What I learned was we have to take the brain at face value. We have to accept its complexity, work backwards from that, and survey all the areas of science and engineering in order to build those tools. 我在研究生阶段所获得的最重要的洞见就是,如果你不能回过头去思考那些关于大脑的谜题,而只是向前去思考那些让你在物理学中觉得有趣的东西,那么你所创造出来的技术可能就不那么重要,它们可能无法被用来解决大的问题。我所学到的是我们需要直面大脑本来的面目,要想创造出那些真正有用的工具,我们就需要接受大脑的复杂性,回过头来以此为目的去调研所有的科学和工程领域。 During the first decade that I’ve been a Professor at MIT, we have mostly been building tools. We built tools for controlling the brain, tools for mapping the detailed molecular and circuit structure of the brain, and tools for watching the brain in action. 在我成为MIT的一名教授之后的首个十年中,我们的主要精力都放在创造工具上。我们创造了用于控制大脑的工具,能够绘制大脑中具体的分子和神经电路结构的工具,还有用于观测大脑活动的工具。 Right now, we’re at a turning point; we’re ready to start deploying these tools systematically and at scale. Don’t get me wrong, the tools still need improvements to be equal to the challenge of studying the brain, but for small organisms like worms and flies and fish, or for small parts of mammalian brains, we’re ready to start mapping them and trying to understand how they’re computing. 现在我们来到了一个重要的转折点上,我们已经做好准备去系统化地大规模部署这些工具来研究大脑了。但请不要误解我的意思,这些工具目前仍然需要得到改进才能足以胜任研究人类大脑这一巨大的挑战性任务,但对于一些较小的有机体,例如蠕虫,蝇类和鱼类,以及哺乳类动物大脑中的一些较小部分,我们已经做好准备去绘制它们的结构并尝试去理解它们是如何进行计算的了。 The work progresses through primarily philanthropic as well as government grant funding. We have been very lucky that there has been a bit of an increase in people interested in funding high risk, high reward things. That’s one reason why I’m at the MIT Media Lab, and you might ask why is a neuroscience Professor in the School of Architecture at MIT? 这些工作的推进主要由慈善基金和政府资助基金提供资金上的支持。我们非常幸运,越来越多的人开始对资助这类高风险,高回报的研究项目感兴趣。而那也是我在MIT媒体实验室工作的原因之一,可能你想问为什么一个神经科学教授会在MIT的建筑学院任职。 As we were discussing earlier, neuroscientists long had a deep distrust of technology, that technologies often didn’t work, the brain was so complicated that the tools could only solve toy problems. When I was looking for a professor job, the search was hit-or-miss. 正如我们之前所讨论过的,神经科学家们长久以来都对技术怀有一种深深的不信任感,他们认为技术通常都起不了什么作用,而大脑是如此复杂,那些被创造出来的工具只能解决一些玩具般的小问题。当我在寻求教职的时候,找工作的过程不确定性很高。 My collaborator, Karl Deisseroth and I had already published a paper showing we could activate neurons with light, a technology that we’ve called ever since “optogenetics,” “opto” for light and “genetics” because it’s a gene that we borrow from a plant to make the neurons light-sensitive. 我和我的合作者Karl Deisseroth当时已经发表了一篇论文表明我们能够通过光信号来激活神经元,这项技术后来一直被我们称作“光基因”(optogenetics),”opto”代表“光”,而”genetics”则代表这是我们从一种植物中提取出的能够让神经元对于光信号敏感的基因。 But a lot of people at the time were still deeply skeptical: is this the real deal or is this yet more not-quite working technology that will be a footnote? I went to the Media Lab to complain about how political and complicated academia was, and I was very lucky; they were wrapping up a failed job search and they said, "Why don’t you come here?" And so I went, and we’ve been incubating a lot of neurotechnology there since then. 但当时很多人仍然对这项技术抱着深深的怀疑态度:这真的是一项重大的技术突破,还是又一种没什么用的仅仅会在将来成为一项脚注的技术?我去MIT的媒体实验室向他们抱怨学术圈的政治和勾心斗角,而这时我的运气来了,当时他们正在总结一次并不成功的求职,于是他们对我说,“要么你到我们这儿来吧?”于是我就去了他们实验室,从那以后我们就开始在这个实验室里培育一大堆的神经技术。 When I first got to Media Lab, a lot of people were deeply puzzled about what I would do there. Was I going to switch into, "classical publicly-perceived Media Lab technology," like would I have developed ways of having cell phones diagnose mental illness or other things like that? I wanted to get to the ground truth of the brain. 当我刚到MIT媒体实验室的时候,很多人都完全搞不清楚我会在那里做些什么。他们怀疑我会不会转向开发一些“经典的受到公众认可的‘媒体实验室技术’”,比如开发一些方法通过手机来诊断精神疾病,或者诸如此类的一些东西。而我所想做的是获得关于大脑的一些基础事实。 In some ways, the Media Lab was a perfect place to start. We could incubate these ideas, these tools out of the cold light of day until they were good enough that neuroscientists could see their value. And that took several years. 从某些角度上看,媒体实验室对我来说的确是一个完美的起点。我们可以避开人们的冷眼,专注于培育创意和技术,直到它们变得足够好,能够让那些神经科学家们看到它们的价值。而这一过程持续了好几年。 It was about a three-year period until this started to get mainstream acceptance, and then, there was another three-year period where people said, wow, how do we get more technology, and that led to initiatives like the Obama BRAIN Initiative, which is an attempt to get widespread technology development throughout neuroscience. 让我们的这些技术受到学界主流的认可花了大约三年时间,而又过了三年时间后有人开始问,哇,太棒了,我们怎么才能获得更多的这类技术?而这导致了之后的一些诸如奥巴马总统的BRAIN计划之类的项目,该计划试图在整个神经科学领域发展一些能够被广泛应用的技术。 The BRAIN Initiative started at the instigation of the Kavli Foundation. They were hosting a series of brainstorms about what nanoscientists and neuroscientists could do together, and Paul Alivisatos and George Church and Rafael Yuste and many people at that border were at these early sessions. BRAIN始于Kayli基金会的大力推动。他们举办了一系列的头脑风暴式的会议以讨论纳米科学家们能够和神经科学家们一同做些什么,Paul Aliyisatos,George Church, Refael Yuste还有其他一些相关领域的科学家们参加了这些早期的会议。 And in late 2012, I was invited to one of these sessions where many inventors were invited and we started talking about maybe brain activity mapping is great and all, but the technologies might be much more broad than that; you might need more than just maps. 2012年末,我应邀参加其中的一次会议。这次会议邀请了许多技术的发明者,我们开始谈论也许绘制出大脑活动的电路图是个伟大的主意之类的话题,但涉及其中的技术范围可能会更宽,因为我们需要的可能不仅仅是一些电路图。 You might need ways to control the brain, ways to rewire the brain. 我们可能需要一些能够控制大脑的方法,还有重连大脑电路的方法。 That was an interesting turning point because it went from activity mapping to broadly technology, and four or five months later, Obama announced this BRAIN initiative which, somewhat recursively, stands for Brain Research for Advancing Innovative Neurotechnologies, and they are now devoting tens to hundreds of millions of dollars a year, depending upon which year, to try to get more technology made to help understand the brain. 那次会议是一个很有趣的转折点,因为从此之后,我们的工作从绘制大脑的电路图拓展到了更宽的技术领域,又过了四五个月,奥巴马总统宣布了他的BRAIN计划,这个计划的首字母缩写看起来像个递归——致力于推动创新神经科技的大脑研究(Brain Research for Advancing Innovative Neurotechnologies),目前人们在这项计划中每年投入高达数千万甚至数亿美元的资金以获得更多能够帮助我们理解大脑的技术。 The BRAIN initiative now is run by different government agencies. They have their own priorities, so, for example, DARPA is very interested in short-term human prosthetics, for example, no surprise there. The National Science Foundation is interested in more basic science, and so forth. The different agencies have their own agendas now. 整个BRAIN计划目前由多个不同的政府机构负责运营。这些机构都有着自己的优先任务,例如,DARPA【编注:全称Defense Advanced Research Projects Agency,美国国防部所辖研究机构】最感兴趣的是短期的人类大脑修复技术(这一点毫不令人惊讶),而国家科学基金会则对于更基础的科学课题更感兴趣,如此种种。而不同的机构现在也都有了他们自己的日程表。 IARPA is involved. They are trying to do a hard push for short-term mammalian brain circuit mapping based upon existing technology, and sort of a small part of that more on the technology development side. Most of the money is on the application side. But we have some new tools that we think can be very, very helpful. IARPA【编注:全称Intelligence Advanced Research Projects Activity,是美国国家情报总监辖下一个研究部门】也同样参与了进来。他们正在努力推进一个通过使用已有的技术绘制哺乳类动物大脑神经电路图的短期计划,其中的一小部分主要是关于技术开发的,而主要的资金则投入到了技术应用上。我觉得我们开发的一些新工具能够在其中派上很大用场。


Companies are great if you can work hard and be smart and solve the problem. But if you’re tackling something like the brain, or the biggest challenges in biology in general, a lot of it’s serendipity. A lot of it is the chance connections when you bring multiple fields together, when you connect the dots, when you kind of engineer the serendipity and make something truly unpredictable, and that’s hard to do if you have closed doors. That’s hard to do if you don’t allow open, free collaboration. 如果你工作足够努力并且有足够的聪明才智去解决问题,那么企业对你来说会是个不错的去处。但如果你的研究对象是大脑,则可以说是生物学史上最大的挑战,要想获得成功就必须依赖于一些意外的收获了。当你试图将多个领域的知识结合在一起,将点连成线,当你试图去驾驭偶然性并且做出一些真正不可预测的成果时,联系和接触的机会非常重要,如果你关起门来闭门造车,如果你不能允许开放而自由的合作,要想获得成功就太难了。 Our group is very big; I think we’re the second biggest research group at all of MIT. But we work with probably about 100 groups, people who are genomics experts and chemistry experts and people making nanodiamonds and all sorts of stuff. The reason is that the brain is such a mess and it’s so complicated, we don’t know for sure which technologies and which strategies and which ideas are going to be the very best. And so, we need to combinatorially collaborate in order to guarantee, or at least maximize the probability that we’re going to solve the problem. 我们的研究团队规模很大,我想它应该是整个MIT第二大的研究团队了。但我们还与大约100个其它的研究小组进行合作,这些小组中有染色体方面的专家,有化学专家,还有些人的工作是制造纳米金刚石,可以说研究什么的都有。这么做的原因在于,大脑是如此混乱而复杂的一个系统,我们并不能肯定哪些技术,哪些研究策略,哪些主意是最好的。所以,我们需要组合式的合作模式来保证问题被解决,或者至少是将解决问题的概率最大化。 You want to have academia for that serendipitous ability to connect dots and collaborate, and you want companies when it’s time to push hard and just get the thing done and scale up and get it out the door. What I would hope to engineer in the coming maybe decade or so are hybrid institutions where we can have people go back and forth because you might need to have an idea that would go back and forth a bit until it matures. 当你需要一些驾驭偶然性的能力以连点成线并推进合作时,学术圈的氛围是最合适的;但当你需要施加压力来搞定某件事情,并将技术推广以得到广泛应用时,企业又成了最合适的地方。在未来的也许十年中我希望能够做到的是建立起一个混合型的研究机构,这样我们就能够让研究者们在学术和企业的氛围之间迅速地切换,因为我们未来的研究思路可能也需要在两种模式间切换直到它变得足够成熟。 I’ll give you an example. We’re building new kinds of microscopes and new kinds of nanotechnologies to record huge amounts of data from the brain. One of our collaborators was estimating that soon some of these devices we’re making might need some significant fraction of the bandwidth of the entire internet in order to record all the brain data that we might be getting at some point. Now, we need some electronics, right? We need electronics to store all the data and computers to analyze the data. But that’s an industrial thing. 让我给你举个例子。我们正在制造一些新型的显微镜和一些新型的纳米技术以记录大脑中的海量数据。我们的一位合作者曾估计,我们正在制造的这些仪器可能很快就需要整个因特网带宽中不小的一部分以记录我们在某些关于大脑的研究过程中获得的所有数据。现在我们需要电子技术了,对吧?我们需要电子技术以记录所有这些数据,同时还需要足够强大的计算机来对这些数据进行分析。但这就是一个更适合让企业来解决的问题了。 It’s much easier to get that done in a company than in academia because people in industry can turn the crank and make incredible computers, so we started a collaboration. A small startup here in Cambridge, Massachusetts, does these computers with us. Now we’re working on the nanotechnologies, and that fusion of two different institutional designs allows us to rapidly move faster than companies alone or academics alone. These new hybrid models are going to be essential to balance the need for luck and the need for skill and ability. 在企业中搞定这类事情要比在学术界容易得多,因为在企业中人们能够开足马力制造出拥有令人难以置信的计算能力的电脑,所以我们启动了意向合作。在马萨诸塞州剑桥市的一家创业公司和我们合作开发了这些电脑。现在我们又开始研发纳米技术了,将企业和学术这两种类型的机构融合在一起则让我们的研究进程推进得比单独依靠企业或是单独依靠学术界要快得多。在对运气的需求与对技术和能力的需求间取得平衡来说,这类混合型机制将是必不可少的。 The thing that I’m excited about also is how do we get rid of the risk in biology and medicine? Most medicines, most strategies for treating patients, they are found in large part by luck. How do we get rid of the risk? We talked a bit about how there are fundamental sciences like physics, and then, you have higher order sciences like biology. Medicine also might have different scientific methods for different kinds of disease. We have made huge inroads against bacteria and viruses because of antibiotics, because of vaccines. 现在让我感到兴奋的是我们如何能够消除一些生物学和医学研究中的风险。目前多数的药物和治疗策略的发现,在很大程度上都是依靠运气。我们能够如何消除风险?我们之前曾经谈论过一点关于物理这样的基础科学的话题,而之后,我们又有了更高阶的科学领域,例如生物学。医学也同样可能在对待不同类型的疾病时使用不同类型的科学方法。由于有了抗生素和疫苗,我们在对抗细菌和病毒的战斗中获得了巨大的进展。 Why have these been so successful? It’s because we’re trying to help our body fight a foreign invader, right? But if you look at the big diseases, the ones that nobody has anybody clue what to do about, there are brain disorders, a lot of cancers, autoimmune conditions, these are diseases where it’s our body fighting ourselves, and that’s much harder because you can’t just give a drug that wipes out the foreign invader because the foreign invader is you. 为什么我们在这方面做得如此成功?这是因为我们在尝试帮助我们的身体对抗某种来自外界的入侵者,对吧?但其它的一些重大疾病,那些没人知道该怎么对付的疾病,例如大脑的功能紊乱,各种类型的癌症,还有自体免疫病,这些疾病实质上都是我们的身体在与自身进行对抗,要解决这些疾病就困难多了,因为如果入侵者就是你自身的话,你就无法为身体提供一种药物去清除这个入侵者。 How do we understand how to de-risk the tough parts of medicine? We have to think about drug development and therapeutic development from a different point of view. The models that give us new antibiotics and new vaccines and so forth might not be quite right for subtly shifting the activity levels of certain circuits in the brain, for subtly tuning the immune system to fight off a cancer but not so much that you’re going to cause an autoimmune attack, right? 我们该如何化解这些医学难点所蕴含的风险?我们必须从另外一个角度去思考药物和治疗方法的开发。那些引导我们研发出新的抗生素,疫苗和其它一些药物的模型也许在精细地切换大脑中的某些特定神经电路的活跃程度方面并不适用。它们或许也无法既精细地调整免疫系统以击败特定癌症,同时又避免引发对自身免疫系统的攻击,对吧? One thought is, well, if it’s your body fighting yourself, what you want is very deep knowledge about the building blocks of those cells and how they’re configured in the body. The basic premises behind ground truthing the understanding of the brain might be also right what we need in order to de-risk medicine, in order to understand how cells and organs and systems go awry in these intractable disorders. That’s something I’ve been thinking a lot about recently as well: how do we de-risk the goal and methodology and path towards curing diseases? 有一种想法是:如果身体在和自身作战,那就需要深入了解关于那些细胞的基础构成单元以及它们是如何在身体中配置成形的。对大脑的基础事实真正理解的一些基本前提也许在我们降低医学方面的风险,以及理解细胞,器官和组织是如何在顽疾中功能失调方面同样适用。这同样是我最近经常思考的一个问题:我们如何在治疗疾病方面降低那些蕴藏在目标,方法和实现路径之中的风险? There was just a study released about how taking a drug from idea to market can cost $2.5 billion now. And if you look at the really tough diseases like brain diseases, like cancers and so forth, the failure rate to be approved for human use is over 90 percent. 最近发表的一项研究成果显示现在研发一种药物从最初的想法开始到最终被推向市场可能需要花掉25亿美元。而如果你看看那些真正严重的疾病,例如大脑疾病和癌症等,治疗这类疾病的药物最终无法被批准投入使用的概率超过了90%。 This got me thinking that maybe this is the same kind of intellectual problem as why we don’t understand how brain circuits compute thoughts and feelings. We have these large 3D systems, whether it’s a brain circuit or a cancer or the immune system, and knowing how to tweak those cells, make them do the right thing, means finding the subtle differences that make those cells different from the normal cells in our body. I’ve been thinking a lot about how we can try to take these tools that we’ve been developing for mapping the brain, for controlling the brain, for watching the brain in action and applying it to the rest of medicine. 这些事实让我想到也许这与为什么我们无法理解大脑的神经电路是如何计算出思想和感受是同一类的问题。我们现在已经拥有了这些大型的3D系统,不论是大脑电路,癌症或是免疫系统,我们都能得到它们的3D图像,而了解如何通过牵引这些细胞让它们去做正确的事情则意味着找到这些细胞区别于正常细胞的细微不同之处。我花了很多时间思考如何使用这些我们开发的工具,将它们用于绘制大脑神经电路图,控制大脑,观察大脑的活动的工具,并将它们应用在其它医学领域。


I can tell you about a collaboration that we have with George Church. George’s group for about fifteen years now has been trying to work on a technology called in situ sequencing, and what that means is can you sequence the genetic code and also the expressed genes, the recipes of cells, right there inside the cells? 我可以向你描述一下我们和George Church之间的一项合作。George的团队已经在一项名为“就地排序”的技术研究上花了大约十五年的时间,这项技术意味着你能够直接在细胞内部对遗传序列和那些被表达出来的基因——也就是细胞自身的配方——进行测序。 Now, why is that important? It’s important because if you just sequence the genome, or you sequence the gene expression patterns after grinding up all the cells, you don’t know where the cells are in three-dimensional space. If you’re studying that brain circuit and here is how information is flowing from sensation into memory regions towards motor areas, you’ve lost all the three-dimensionality of the circuit. You just have ground up the brain into a soup, right? 为什么这项技术如此重要?因为如果你只是对基因组进行测序,或者在破坏了细胞结构之后再将这些基因表达的模式进行测序,你就无法知道这些细胞在三维空间中的位置。如果你正在研究某个大脑神经电路,而恰好感觉中的信息正是经由这些细胞流入记忆区进而流向运动区,那么你就丢失了这一神经电路中的所有三维信息,因为你已经把大脑搅成了一锅粥。 Or for a tumor, we know that there are cells that are by the blood vessels, there are stem cells, there are metastasizing cells; if you just grind up the tumor and sequence the nucleic acids, you again have lost the three-dimensional picture. A couple years ago, George’s group published a paper where they could take cells in a dish and sequence the expressed genes. 或者举个肿瘤的例子,我们知道有些肿瘤细胞分布在血管附近,有些是肿瘤干细胞,还有些肿瘤细胞能够转移,如果你将肿瘤打碎并且将其中的核酸进行排序,你同样会丢失它的三维图像。几年前,George的研究团队发表了一篇论文表明他们能够在保持细胞完整性的同时对已经表达的基因进行测序。 That is, you have DNA in the nucleus, that expresses in terms of RNA, which is the recipe of that cell, and the RNA then drives all the downstream production of proteins and other biomolecules. The RNA is sort of in-between the genome and the mature phenotype of the cell. It's kind of the recipe. George’s group was sequencing the RNA. I thought that was amazing: you could read out the recipe of a cell. 也就是说,细胞核中有DNA,DNA通过转录会生成RNA,而RNA则是细胞的配方,在之后它会驱动下游的蛋白质和其它生物分子的生产过程。RNA可以看作某种基因组和细胞的成熟表现型之间的中间产物,它也是一种配方。当George的团队对RNA进行测序时,我觉得这有些不可思议,因为现在我们居然已经能够读懂细胞的配方了! Now, there was a tricky part: it didn’t work well in large 3D structures like brain circuits or tumors. Our group had been developing a way of taking brain circuits and tumors and other complex tissues and physically expanding them to make them bigger. What we do to make the brain or a tumor bigger is we take a piece of brain tissue and we chemically synthesize throughout the cells, in-between the molecules, around the molecules, in that piece of brain, a web of a polymer that’s very similar to the stuff in baby diapers. And then, when we add water, the polymer swells and pushes all the molecules apart, so it becomes big enough that you can see it even using cheap optics. 现在棘手的问题来了:这种技术在大脑神经电路和肿瘤这样的大型3D结构上的表现并不好。我们团队已经在研发了一种能在物理上将大脑神经电路,肿瘤和其它此类复杂组织进行放大的方法。我们用来放大大脑神经电路(或是肿瘤)的方法是获取其中的一小块脑组织,然后通过化学方法在这块脑组织的细胞内部分子之间和分子外部进行合成,最终得到一块像婴儿的纸尿裤一样的网状聚合物。然后我们在其中加入水,这块聚合物会膨胀,并将所有的分子推散,这样它就变得足够大了,即使用一些便宜的光学设备也能看清楚其中的结构。 One of my dreams is you could take a bacterium or a virus and expand it until you can take a picture on a cell phone. Imagine how that could help with diagnostics, right? You could find out what infection somebody has just by making it bigger, take a picture and you’re done. 我的梦想之一就是有一天我们可以将一个细菌或是病毒放大到你能够用手机给它拍照的程度。想想这能在多大程度上帮助人们进行诊断吧,在判断某个病人到底是被什么感染了这个问题时,你只需要将感染物不断地放大,然后给它拍张照片就搞定了。 We started talking with George: what if we can take our sample and expand it and then run their in situ sequencing method—because sequencing, of course, is really complicated. You need room around the molecules to sequence them. This is very exciting to me, if we can take stuff and expand it and then use George’s technology to read out the recipes of the cells, we could map the structure of life in a way. 于是我们去和George谈了这件事情:如果我们能够将我们所采集的样本放大,然后再对放大后的样本使用“就地测序”的方法——测序这项工作本身真的非常复杂,因为分子之间要有足够的空间。这对我来说是件令人兴奋的事情,如果我们能够将这些组织进行采样,然后将它们放大,再使用George发明的技术去读取这些细胞的“配方”,那么我们就能以某种方式绘制出生命的结构。 We can see how all the cells look in a complex brain circuit, or in a tumor, or in an organ that’s undergoing autoimmune attack like in type 1 diabetes. That’s one of the things that excites me most is this in situ sequencing concept. If we can apply it to large 3D structures and tissues, we might be able to map the fundamental building blocks of life. 我们可以看看在一个复杂的大脑神经电路中所有细胞到底是什么样子的,对于一个肿瘤或是一个正在遭受类似I型糖尿病这类自免疫攻击的器官,我们也可以做到同样的事情。这就是“就地测序”这一概念所能做到的最令我激动的事情之一。如果我们能够将这项技术应用到大型的3D结构和组织中,也许就能绘制出生命基本单元的样子。 Our current collaboration with George’s group has been focused very much on small pieces of tissue that we have: mouse brains probably, other model organisms in use in neuroscience. But we know that if they work in those systems, they’ll probably work in human tissues as well. 在目前与George的团队的合作当中,我们的关注点还主要集中在一些比较小的组织切片上:例如老鼠的大脑和其它一些在神经科学中常用的模式生物。但我们知道如果他们的技术在这样的系统中是有效的,那么这些技术大概在人体组织中也同样能发挥作用。 Imagine we get a cancer biopsy from somebody, we use our group’s technology to expand it physically, making everything big enough to see, and then, we can go in and use George’s in situ sequencing technology to read out the molecular composition. 想像一下,假如我们从某位患者身上获得了一块活体癌症组织,然后使用我们小组开发的技术将它在物理上进行放大,让其中所有东西都大到能够被观测到,那么我们就可以进入组织内部,使用George的“原地测序”技术读取其中的分子构成。 When we first published the idea of expanding something, a lot of people were very skeptical about it. It’s a very unconventional way of doing things. To convince people that it works, we went down [the following] line of reasoning: a design method. 当我们首次公开发表这项在物理上将某个活体组织进行放大的技术思路时,很多人都对此深表怀疑。因为这是一种非常不合传统的做法。为了让人们相信这种技术是可行的,我们采用了如下的论证路线,它是一种设计方法。 When we synthesized the baby diaper-like polymers inside the cells, we would anchor through molecular bonds specific molecules to the polymer, and then we would wipe up all the rest. We can use enzymes and so forth to chop up the rest. 当我们在细胞内部合成出那些像婴儿纸尿裤一样的网状聚合物时,我们会将整个分子键结构中的一些特定分子保留在聚合物的网状结构上,而去除掉其它的分子。我们可以使用一些酶和类似的化合物将其它的分子切掉。 That way, when we expand the polymer, our molecules that we care about are anchored and move apart, but the rest of the structure has been destroyed or chopped up so that it does not impede the expansion. That’s a key design element. 通过这种方式,当我们在放大网状聚合物时,我们所关心的那些分子都被原封不动地单独保留了下来,但剩下的那些结构则会被销毁或切除,这样它们就不会妨碍放大的过程。这就是其中关键的设计元素之一。 One way to think of this is—chemistry is a way of doing fabrication massively in parallel. So suppose that I want to see two things that are close together, like my two hands here. But of course, lenses cannot see very, very small things, right, thanks to diffraction. So what if we took my two hands and anchored them to these expandable polymers and then destroyed everything else? There might be a lot of junk here we don’t care about. 可以这样看——化学技术是一种并行地进行大规模制造的方法。假设我想要看清两件紧紧贴在一起的东西,就像我的两只放在一起的手。当然,由于衍射现象的存在,普通的镜头是无法看清非常非常小的东西的。但如果我把两只手都固定在这些可放大的网状聚合物上,然后将其它所有的东西都毁掉呢?因为其中可能包含了一大堆我们完全不关心的垃圾。 We add water and the polymer swells, moving my hands along with it until they’re far apart enough that we can see the gap between them. That’s the core idea of what we call expansion microscopy where we take the molecules in a cell or the molecules in a tissue, a brain circuit or a tumor, and we anchor those molecules to a swellable polymer. When we add water, the molecules we care about, the ones we’ve anchored—that we’ve nailed to the polymer, as it were, have moved apart until they’re far apart enough that we can see them using cheap, scalable, and easily deployed optics like you could find on an inexpensive microscope or even a webcam. 我们向网状聚合物中加水,然后它会膨胀,我的两只手也会随着它的膨胀发生移动,一直到它们的距离远到我们能够看清其中的缝隙。这就是被这项我们称为“放大显微术”的核心思路,我们从一个细胞或者一块组织——比如大脑的神经电路或者肿瘤——中选定一些分子,然后将它们固定在一块可膨胀的网状聚合物上,当我们向其中加水,那些我们所关心的被固定在聚合物上的原本贴在一起的分子就会互相分离,直到我们可以通过使用一些廉价的,可扩展并且容易部署的光学仪器——比如低端的显微镜,甚至是网络摄像头——将它们看清。 After we published our paper on expanding tissues, a lot of people started to apply them. For example, suppose you wanted to figure out how the cells are configured in a cancer biopsy. You can take the sample and if you look at it under a microscope, you can’t see the fine structures, but if you blow it up and make it bigger, maybe you could see the shape of the genome; maybe you could see that one cell is extending a tiny tendril, too tiny to see through other means, and maybe that’s the beginning of metastasis. 在我们发表了关于这项放大生物组织技术的论文之后,有许多人都开始将这项技术投入应用。举个例子,假如你想知道在一块活体癌症组织中细胞是如何构成的,你可以取下一块样本,如果你用一架显微镜去观察它,你根本无法看清其中的精细结构,但如果你能够将它放得更大,也许你就能看清其中基因组的形状了,也许你还能看见某个细胞在扩张一个细小的卷须状结构,但这个结构实在太小了,通过其它的任何方法你都无法看清它,而那可能正是一次癌细胞转移过程的开始。 A lot of people are trying to use our technology now for seeing things that you just can’t see any other way, and we’re finding a lot of interest not just from brain scientists because now you have a way of mapping brain circuits with nanoscale precision in 3D, but also from other brain-like problems: tumors and organs and development and so forth where you want to look at a 3D structure but with nanoscale precision. 现在有很多人在尝试使用我们的技术来看清那些他们无法通过其它方式看清的结构,而我们发现不仅仅只有脑科学家对它感兴趣——这项技术为脑科学家们提供了一种在纳米级精度上绘制3D大脑神经电路图的方法,而其它一些研究与大脑问题具有共性的课题的科学家们也对此感兴趣:在肿瘤和某些器官的发展过程和其它一些类似的课题中,人们也希望能够在纳米级的精度上看清3D结构。 We’ve spun out a small company to try to make kits and maybe provide this as a service so that people can use this widely. Of course, we’ve also put all the recipes on the Internet so people can download them, and hundreds and hundreds of groups have already started to play with these kinds of tools. 我们成立了一家小公司来尝试为这项技术制作一些工具套件,甚至是将它作为一项服务提供给需要的人以让这项技术能够被广泛地使用。当然,我们同样也在因特网上公开了这项技术的所有“配方”,人们可以下载它们。已经有数以百计的研究小组开始在他们的工作中使用这些工具。 We want to make the invisible visible, and it’s hard to see a 3D structure like a circuit that might store a memory or a circuit in the brain that might be processing an emotion, with the nanoscale resolution that you need to see neural connections and the molecules that make neurons do what they do. 我们希望能让那些原来看不到的结构被看清,要清晰地看到大脑中某个可能存储了记忆或是正在处理某种感情的神经电路的3D结构是非常困难的,你需要在纳米级的分辨率下才能看到神经元之间的连接和那些促使神经元发挥作用的分子结构。 The fundamental limit on how fine we can see things is related to a technical parameter called the mesh size; that is basically the spacing between the polymer chains. We think that the spacing between the polymer chains is about a couple nanometers; that is, around the same size as a biomolecule. If we can push all the molecules away from each other very evenly, it’s like drawing a picture on a balloon and blowing it up: you might be able to see all the individual particles and building blocks of life, but you know what? 决定我们能够在多高的清晰度下看清东西的基础限制是与一项被称为“网格尺寸”的技术参数相关的,这个参数的含义其实就是网状聚合物构成的链式结构之间的孔隙大小。我们认为这个空隙的大小大约是几纳米,也就是说,这和一个生物分子的大小差不多。如果我们能够将所有的分子按照非常接近的比例彼此推开,这就有点像在一个气球上画了一幅画,然后再将气球吹大,之后你就有可能看清所有的那些颗粒和组成生命的基础成分了。 We have to validate the technology down to that level of resolution. So far, we have validated it down to about a factor of ten bigger than that, in order of magnitude. But if we can get down to single molecule resolution, you could try to map the building blocks of living systems. We haven’t gotten there yet. 但你需要知道的是,我们还需要在生物分子级的分辨率上对这项技术进行验证,到目前为止,我们已经在比这高一个数量级的分辨率上成功地验证了这项技术。如果我们能够在单个分子的分辨率上验证这项技术,我们就能够绘制出活系统中的那些基础成分了,但目前我们还没能做到这一点。 I’ve been amazed at how fast neurotechnology has started to move. Ten years ago, we had relatively few tools for looking at and controlling the brain, and now, ten years later, we have our optogenetic tools for controlling brain circuits, this expansion method for mapping the fine circuitry, and also, we have developed 3D imagining methods that basically work the way that our eyes work to reconstruct 3D images of brain high speed electrical dynamics. 我对近来神经技术的发展速度感到吃惊。十年前,我们只有相对很有限的工具来控制大脑,而十年后的今天,我们已经拥有了像“光基因”这样的工具来控制大脑的神经电路,还有这种通过放大技术来绘制精细的神经电路的方法。此外,我们还开发了3D成像的方法来观测大脑内部的高速电子动态,其工作原理和我们的眼睛重建3D图像的方法是相同的。 In the coming fifteen years, two things are going to happen and a third thing, might happen. One thing that will happen is that our ability to map the fine details of neural circuits and see high speed dynamics and control it will probably be perfected; that might happen as soon as five years from now but definitely within fifteen years, I would predict that. 在接下来的十五年中,我认为会发生两件重大的事情,另外还有第三件事情也可能会发生。第一件事情是,我们绘制神经电路的精确细节,观测其中的高速动态,以及对它进行控制的能力将会得到完善,这些也许最快在今后的五年中就会发生,并且在十五年内几乎一定会发生,我可以肯定地这样预测。 The second thing is that we’re going to have some detailed-enough maps of small neural circuits that maybe we could even make computational models of their operation. For example, there is a small worm called C. elegans that has 302 neurons; maybe we can map all of them and their molecules and their dynamics and perhaps we can make a computational model of that worm. Or maybe a slightly larger brain: the larval zebrafish has 100,000 neurons, mice have 100 million—ballpark—and humans have 100 billion. You can see there are some multistage logarithmic jumps there that we have to make. 第二件事情是我们将绘制出一些细节足够丰富的小型神经电路图像,也许我们甚至可以据此开发出一些有关它们工作方式的计算模型。例如,有一种叫做秀丽隐杆线虫的蠕虫拥有302个神经元,也许我们能够绘制出它的所有神经电路图,以及其中的分子结构和电子动态,那么我们也许可以建立这种蠕虫的计算模型。如果扩展到大一点的大脑,斑马鱼拥有大约十万个神经元,而老鼠则拥有大约1亿个神经元,人类的神经元数目大约是一千亿。你可以从这里看出,在大脑规模从小到大的过程中,我们需要做很多次多级的对数跳跃。 The speculative thing is that we might have some tools that might let us look at human brain functions much, much more accurately. Right now, we have so few tools for looking at the human brain, there is functional MRI which lets you look at blood flow that is downstream of brain activity, but it’s very indirect and it’s very crude. The time resolution is thousands of times slower than in brain activity, and the spatial resolution, each little block that you see in these brain scans contains tens to hundreds of thousands of neurons, and we know that even nearby neurons can be doing completely different things. 而那件不太确定的事情则是我们也许会拥有一些能够让我们以远高于当前的精确程度观察人类大脑功能的工具。现在,能用来观察人类大脑的工具实在太少了,我们有一些功能性的核磁共振(MRI)设备能让我们观察某种大脑活动所引发的血液流动,但这种方式太间接了,同时也太不精确。这种工具的时间分辨率比大脑活动要慢上数千倍,从空间分辨率上说,你从MRI的扫描图像上看到的每个小方格都包含了数以百万计的神经元,而我们知道,即使是相邻的神经元也可能正在做着完全不同的事情。 What we most need right now, I would say, is a method for imaging and controlling human brain circuits with single cell, single electrical pulse precision, and the jury is out on how that could happen. There’s lots of brainstorming. I haven’t seen any technology generated so far that can probably do it although there’s lots of interesting speculation. That’s something I would love to see happen and we have started to work on some ideas that might allow you to do it. 我想说,我们当前最需要的,是一种能够在单个细胞,单个电子脉冲的精度上对大脑电路进行控制和成像的方法,而不确定的是这将会如何发生。我们已经进行过了很多次头脑风暴,但至今为止,虽然有许多有趣的可能性,我却并没有看出任何一种现有的技术能有很大的可能性做到这一点。我希望能够看到这件事情在不远的将来发生,而且我们已经开始将一些有前景的想法付诸实践了。 There’s a lot of speculation about whether there are quantum effects that are necessary for brain computations. At body temperature, it’s very likely that quantum effects, if any, are going to be very, very short-lived, maybe much shorter than the kinds of computations that are happening in the brain. It’s quite possible that if such effects are important, we would need far more powerful tools to see them, or perhaps you can explain all of the biophysics of neurons known to date, for the most part, with completely classical models. 关于大脑的计算过程中是否会用到量子效应这一问题有很多猜测。在人的体温之下,似乎量子效应即便存在也会非常非常短暂,其存续时间相对于发生在大脑之内的计算过程要短得多。如果此类效应的确是重要的,那么我们很可能就需要比当前强大得多的工具来观测它们。但实际上我们也可能完全能够通过一些经典模型来解释目前我们所知的绝大部分关于神经元的生物物理现象。 The thing that I loved about working on the quantum computation project, this was with Neil Gershenfeld back in the day, was this greater philosophy of how information and physics are linked. There are many theories of fundamental physical principles of computation; there is even the phrase, “it from bit,” where people talk about the fundamental thermodynamic limits of how information processing occurs in physical systems. 我之所以愿意投入时间在量子计算研究项目上,主要是早先在与Neil Gershenfeld一起工作的时候,受到了关于信息和物理学是如何紧密联系在一起的这一伟大哲学思想的影响。在计算的基础物理原则方面已经有了很多理论。当人们会谈论在物理学系统中发生的信息处理过程所受到的基础热力学限制时甚至有这样的谚语:“万物皆比特(it from bit)”。 For example, there are so many bits associated with a black hole, there is, based upon temperature, a fundamental amount of information that might be encoded in a specific transition. The brain for the most part is operating, because it’s at body temperature and all that, far above those physical fundamental limits in terms of information processing. 例如,与一个黑洞相关的比特数非常之多,在给定的温度下,一个基础量的信息可能会被编码到某个特定的转换过程中。而大脑在大多数情况下都在工作,因为它处于人体体温的环境下,而在这种温度下的信息处理则远远超过了那些物理上的基础限制。 On one level, the most parsimonious models of the brain are analogue because we know that there are different amounts of transmitters being released at synapses, we know that the electrical pulses that neurons compute can vary in their height and in their duration. 从某个层面上说,那些关于大脑的最简化模型都是模拟的(而非数字的),因为我们知道,大脑中的各突触所释放出的传导物质是不同的,我们也知道神经元所计算的不同电脉冲的强度和持续时间区别很大。 Of course, if you dig deep enough, you could say, well, you could just count the neurotransmitters, you could count the ions, and it becomes digital again, but that’s a much more detailed level of description that might not be the most parsimonious level because you had to count and localize every single sodium ion and potassium ion and chloride ion. Hopefully, we don’t have to go that far. But if we need to, we would probably have to build new technologies to do that. 当然,如果你功课做得够深,那你可以去数一下那些神经递质的数目,还有电脉冲中离子的数目,那么这个问题就又成为了数字的(而非模拟的)了,但那是一个细致得多的描述水平,而并不处在最简化层次上,因为你需要计数和定位每一个钠离子,钾离子和氯离子。希望我们不需要走得那么远,但如果真的有必要,我们还是很可能去创造一些新的技术来做到这些事情。 My co-inventor, Karl Deisseroth, and I both won Breakthrough Prizes in Life Sciences for our work together on optogenetics, this technology where we put molecules that are light sensitive into neurons and then we can make them activatable or silence-able with pulses of light. 我和我的合作者Karl Deisseroth由于我们在“光基因”技术上的合作成果共同获得了《生命科学》杂志所颁发的突破奖,在这项技术中,我们将一些光敏分子植入神经元中,然后我们就可以通过光脉冲来让它们在可激活状态和静息状态之间切换。 Our groups have sent these molecules out to literally thousands of basic as well as clinically interested neuroscientists, and people are studying very basic science questions like how is a smell represented in the brain? But they’re also trying to answer clinically relevant questions like where should you deactivate brain cells to shut down an epileptic seizure? I’ll give you an example of the latter since there is a lot of disease interest. 我们的小组已经将这样的分子提供给数千名基础神经科学家和临床神经科学家,其中有些科学家研究的是非常基础的科学问题,例如气味是如何在大脑中被表达出来的。还有一些科学家则试图回答一些临床相关的问题,例如应该在什么地方让大脑细胞停止活动以停止一次癫痫病的发作。下面我会给你举一个后者的例子,因为有很多人都对我们的技术在疾病研究方面的应用感兴趣。 People have been trying to shut down the over excitable cells during seizures for literally decades, but it’s so difficult because which part of the brain and which cells and which projections? It’s such a big mess, right, the brain? So a group at UC Irvine has been using our technologies to try to turn off different brain cells or even to turn on different brain cells, and what they’re finding is that some cells, if you activate them, can shut down a seizure in a mouse model. But still, who would have thought that activating a certain kind of cell would be enough to terminate a seizure? There is no other way to test that, right, because how do you turn on just one kind of cell? 人们在过去数十年中一直在尝试去关闭那些在癫痫病发作时过度活跃的细胞,这非常困难,因为很难弄清大脑中究竟是哪个部分的哪些细胞的哪些投射过于活跃了。要知道大脑看起来就是一团乱麻。一个来自加州大学尔湾分校的研究小组使用我们的技术试图关闭和激活大脑内部的不同细胞,而他们的研究成果表明的确存在某些细胞,通过激活它们可以在一个鼠脑模型中停止癫痫的发作。 What they did was there are certain classes of cell called interneurons, and they tend to shut down other cell types in the brain. What this group did is they took a molecule that we had first put into neurons about a decade ago, a molecule that, kind of like a solar panel, when you shine light on it, will drive electricity into the neuron. They delivered the gene for this molecule so that it would only be on in those interneurons, none of the other cells nearby, just the interneurons. And then, when they shine light, these interneurons will shut down their neighboring cells, and they showed you could terminate a seizure in a mouse model of epilepsy. 某些类型的细胞被称为中间神经元,它们所做的事情是尝试去关闭大脑内部其它类型的细胞。这个小组采用了我们十年前第一次植入神经元时所使用的一种分子,这种分子有点像一块太阳能电池板,当你将它置于光照下,它就会驱动神经元内部的电信号。他们将这种分子的基因植入了那些中间神经元,并保证除了这些中间神经元之外,附近的其它细胞内部都不存在这种分子。然后当他们点亮光线,这些中间神经元就会关闭它们相邻的细胞,他们的工作成果表明你能够在一只老鼠癫痫病发作时通过这种方法停止病症的发作。 That’s interesting because now, if you could build a drug that would drive those cells, maybe that would be a new way of treating seizures, or you could try to directly use light to activate those cells and build a sort of prosthetic that would be implanted in the brain and activate those cells near a seizure focus, for example. 这一结果十分有趣,因为现在你可以制造一种药物来驱动那些细胞,也许这会成为一种治疗癫痫病的新途径,或者你也可以尝试直接使用光来激活那些细胞,并制造出某种能够被植入大脑的假体,并通过它来激活癫痫病发作的核心区附近的那些细胞。 People are exploring both ideas. Could you use our optogenetic tools to turn on and off different cell types in the brain to find better targets, but then, treat those targets with drugs? Or could you use light to activate cells and directly sculpt their activity in real-time in a human patient? The latter, of course, is much higher risk, but it’s fun to think about for sure. And there are a couple companies that are trying to do that now. 以上两种思路都正处于人们的探索之中。人们是否能够使用我们的“光基因”工具来打开和关闭大脑内部不同类型的细胞功能以更好地发现目标,然后再使用药物对这些目标有的放矢呢?或者是否能够使用光来激活病人大脑中的某些细胞并且直接实时控制它们的行为呢?第二种做法无疑会带来很高的风险,但考虑这种可能性确实非常有趣。目前的确有一些公司在尝试这么做。 When we were talking about the Breakthrough Prize, I thought about the little speech I gave—they give you thirty seconds, but I thought about it for several weeks because I feel like there is such a push to cure things, a push to find treatments, but in some ways, by forcing it to go too fast, we might miss the serendipitous insights that are much more powerful. 谈到《生命科学》杂志的突破奖,我想到了我在获奖时所发表的一段简短讲话——他们只给了我三十秒,但我却考虑了几个星期,因为我觉得人们太过于急着去治疗疾病,找到好的疗法,但在某种情况下,如果我们强行地迅速推进这些事情,就很可能错过一些实际上要强大得多的只有通过机缘巧合才能发现的深刻洞见。 I’ll give you an example: in 1927, the Nobel Prize in Medicine was given to this guy who came up with a treatment for dementia. What this person did is, he would take people with dementia and he would deliberately give them malaria. Remember this is the greatest idea of its time, right? 让我来举个例子:1927年的诺贝尔医学奖颁发给了一位发现了一种痴呆症疗法的科学家。而他所做的事情则是故意让那些患有痴呆症的病人感染疟疾。记住,这可是那个时代最伟大的点子。 Now, why did it work? Well, malaria causes a very high fever. At that time, dementia was often caused by syphilis, and so, the high fever of malaria would kill the parasite that causes syphilis. Now, in 1928, one year later, antibiotics started to come online, and of course, antibiotics have been a huge hit and syphilis-related dementia is almost unheard of nowadays. 那么问题来了,为什么这种做法能够起效?其实是因为疟疾会导致非常严重的发热。而在当时,痴呆症则通常是由梅毒引起的,通过这种方法,疟疾所带来的高烧就能够杀死那些引起梅毒的寄生虫。而在一年后的1928年,抗生素开始得到普及,当然,抗生素所带来的影响的确非常深远,由梅毒所引起的痴呆症在最近几乎完全销声匿迹了。 The rush to get a short-term treatment, I worry, can sometimes cause people to misdirect their attention from getting down to the ground truth mechanisms of knowing what’s going on. It’s almost like people often talk about we’re doing all this incremental stuff, we should do more moon shots, right? I worry that medicine does too many moon shots. Almost everything we do in medicine is a moon shot because we don’t know for sure if it’s going to work. 我所担心的是,人们急于在短期内去寻求某种疗法的风潮有时会错误地将人们的注意力从脚踏实地去研究基础事实并弄清其中的机制上转移开。这就像人们所经常谈论的,我们总是做着这些循序渐进的事情,我们难道不应该把更多的精力花在探月这样的事情上吗?我担心医学界会关注太多这类“探月”式的大目标。我们目前在医学上所做的所有事情都是一次“探月”,因为我们并不能肯定我们所做的事情能够见效。 People forget. When they landed on the moon, they already had several hundred years of calculus so they have the math; physics, so they know Newton’s Laws; aerodynamics, you know how to fly; rocketry, people were launching rockets for many decades before the moon landing. When Kennedy gave the moon landing speech, he wasn’t saying, let’s do this impossible task; he was saying, look, we can do it. We’ve launched rockets; if we don’t do this, somebody else will get there first. 人们都是健忘的。当人类第一次踏上月球时,微积分已经发明了几百年了,所以他们拥有足够好的数学工具;而在物理上,人类也已经知道了牛顿定律;在空气动力学上,人们已经知道了如何飞行;在火箭技术上,登月前人们已经积累了数十年的发射火箭的经验。当肯尼迪发表登月演说时,他并不是在说,让我们来完成这项不可能完成的任务吧,他所说的是,看吧,我们能做到这件事情。美国人发射了登月火箭,如果我们不做这件事情,将会有别人捷足先登。 Moon shot has gone almost into the opposite parlance; rather than saying here is something big we can do and we know how to do it, it’s here is some crazy thing, let’s throw a lot of resources at it and let’s hope for the best. I worry that that’s not how “moon shot” should be used. I think we should do anti-moon shots! “探月”这个字眼现在的意思已经和当初完全颠倒过来了,现在它的意思已不再是“这是件大事,而且我们知道如何将它完成”,而变成了“这件事情很疯狂,让我们多投入些资源然后祈祷吧”。我所担心的正是对探月精神的这种误用,我觉得我们现在应该做的是反对这种“探月精神”,脚踏实地做更多的基础研究! (编辑:辉格@whigzhou) *注:本译文未经原作者授权,本站对原文不持有也不主张任何权利,如果你恰好对原文拥有权益并希望我们移除相关内容,请私信联系,我们会立即作出响应。