新概念阅读：人脑也有“复制”的可能

新概念阅读：
The 86 or so billion neurons in the human brain and the hundreds of trillions of connections between them allow us to think, walk, talk and interact with one another. It is no exaggeration to say all human nature lies within. The more we understand how it works, the better we can diagnose and treat neurological disorders from autism to Alzheimer’s.

人脑中的约860亿个神经元以及这些神经元之间几百万亿的连接，使我们能够思考、行走、讲话、与他人互动。毫不夸张地说，人性皆在大脑中。我们对它的工作原理了解越深刻，就越能更有效地诊断和治疗孤独症和老年痴呆症之类的神经疾病。

The 10-year €1.19bn project to simulate the entire human brain, announced on Monday by the European Commission is, at about a sixth of the cost of the Large Hadron Collider, the biggest neuroscience project undertaken. It is an important, but flawed, step to a better understanding of the organ’s workings.

欧盟委员会(European Commission)日前公布了为期10年、耗资11.9亿欧元的研究项目，旨在模拟完整的人脑结构。该项目的成本约为大型强子对撞机(Large Hadron Collider)项目的六分之一，是规模最大的神经学研究项目。它是人类朝着加深对大脑工作原理的了解迈出的重要一步，但也存在着缺陷。

The flaw lies in the unrealistic goal. In the words of the science journal Nature, The Human Brain Project’s goal of a complete simulation is “a breathtaking ambition that has been met with some scepticism”. Although it would be valuable – enabling researchers, for example, to test the effects of mental-health drugs – the complexity of the organ is far too intricate to be modelled accurately with today’s computers. By most estimates, this is likely to be out of reach for decades.

缺陷在于它的目标不现实。用科学期刊《自然》(Nature)的话说，人脑研究项目(Human Brain Project)提出了完全模拟人脑的“惊人目标，但也招致了一些怀疑”。尽管该项目具有宝贵的价值——例如，帮助研究人员测试精神疾病药物的药效——但人脑结构太过复杂，难以通过目前的计算机精确建模。根据多数人的估计，这一目标很可能在几十年内都无法实现。

As neuroscientist Matteo Carandini recently observed, more than two decades of attempts to build simulations have yielded little, partly because complex systems are hard to model with sufficient precision (think about how hard it is to predict the weather two weeks hence). In the words of a classic 1972 essay by physicist P.W. Anderson: “The ability to reduce everything to simple fundamental laws does not imply the ability to start from those laws and reconstruct the universe . . . At each level of complexity entirely new properties appear.” Large-scale models are possible but the more complex they are, the greater the computational demands, and the greater the risk of error. Even if computer speed continues to double every 18-24 months, it is likely to take significantly more than a decade to reach the point at which an accurate, complete simulation is genuinely feasible.

神经学家马特奥 卡兰迪尼(Matteo Carandini)近期观察发现，20多年来的人脑模拟试验成果寥寥，部分原因在于对复杂系统的建模难以达到足够的精度(试想预测两周后天气的难度有多大)。物理学家P W 安德森(P.W. Anderson)在发表于1972年一篇的经典文章中写道：“人类能将一切事物简化到基本定律，不代表人类能从这些定律出发、重构出宇宙……事物的复杂度每变化一级，都会呈现出全新的性质。”大模型是可能实现的，但模型越复杂，对计算能力的要求就越高，出现误差的可能性也就越大。即便计算机处理能力继续以每18至24个月翻一番的速度发展，真正实现精确、完全的人脑模拟需要的时间可能也远不止十年。

And even if we had sufficient computing power, we do not know enough about how individual neurons work, either alone or in co-ordination with other neurons.

此外，即使计算能力足够强，我们对神经元单独工作和互相协作的原理也缺乏足够的认识。

We still lack basic knowledge, such as how memories are encoded in the brain, and it is hard to simulate what we do not understand.

我们仍然欠缺基础性的认识(如大脑如何对记忆编码)。我们很难模拟出自己不了解的事物。

Even so, it could foster a great deal of useful science. The crucial question is how the money will be spent. Much of the infrastructure developed will serve a vast number of projects, and the funding will support more than 250 scientists from more than 80 institutions, each with his or her own research agenda. A great many, such as Yadin Dudai (who specialises in memory), Seth Grant (who studies the genetics and evolution of neural function) and Stanislas Dehaene (who works on the brain basis of mathematics and consciousness), are stellar.

即便如此，人脑研究项目仍能够促进诸多有用科学的发展。关键的问题是：资金如何使用?建立起的基础研究架构将服务于数目繁多的科研项目，资金将支持80多家机构的250多名科学家，他们各有自己的研究计划。包括亚丁 杜达伊(Yadin Dudai，专攻记忆)、塞斯 格兰特(Seth Grant，研究神经功能的遗传和进化)和斯坦尼斯拉斯 德阿纳(Stanislas Dehaene，研究数学和意识的大脑意识)在内的许多人，都是非常杰出的科学家。

Still, by focusing on the newsworthy but unlikely goal of cataloguing all the brain’s individual parts, the project may squander some of its budget. By way of analogy, imagine a laptop fell to earth 500 years ago, and the world’s best scientists tried to discover how it worked. One strategy would be to dissect it, noting how the wires and transistors connect, developing tools such as microscopes and logic probes to try to fathom its complexity. Another would be to use the software to try to get a handle on what it did. One would hope to connect the two levels of understanding – one functional (what the laptop does), the other physical (how the circuits work). It is doubtful one could recreate the laptop by taking measurements.

分门别类地对大脑各个部位编制目录，虽有新闻价值，却难以实现。人脑研究项目致力于实现这一目标，可能会浪费一部分预算经费。让我们做一个类比：设想500年前有一台笔记本电脑掉落在地球上，世界上最优秀的科学家试图揭秘它的工作原理。一种方法是“解剖”这台电脑，记录线路与晶体管的连接方式，并研制出显微镜和逻辑探头等工具，以彻底理解它的复杂结构。另一种方法则是运用软件探究它的功能。人们会希望结合功能(笔记本电脑可以做什么)和物理结构(电路的工作原理)这两个层面来理解它。单凭测量零件的尺寸，很难重新造出一台功能齐全的笔记本电脑。

Contemporary neuroscience is filled with talk of axons, dendrites, neurotransmitters, and technical machinery such as calcium channels (which allow neurons to do their work). But too little is known about how those elements co-ordinate to mediate ideas, emotions and actions. Even basic phenomena such as short-term memory remain poorly understood. At present, the Human Brain Project seems too tilted towards physical understanding, with too little weight given to functional understanding. Truly understanding the brain will require bridging between the two.

现代神经学充斥着对轴突、树突和神经传递素，以及钙离子通道(它使神经元发挥作用)等技术机制的讨论。但至于这些要素如何相互协调，传递思想、表情和动作，人们知之甚少。对于短暂记忆等基本现象的认识也仍然十分匮乏。目前，人脑研究项目似乎过于重视实现物理上的理解，而轻视了功能上的理解。要想真正了解大脑，需要将二者融为一体。