英語短文：人腦也有“復制”的可能
英語短文：
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.
人腦中的約860億個神經元以及這些神經元之間幾百萬億的連接，使我們能夠思考、行走、講話、與他人互動。毫不夸張地說，人性皆在大腦中。我們對它的工作原理了解越深刻，就越能更有效地診斷和治療孤獨癥和老年癡呆癥之類的神經疾病。
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.
歐盟委員會(European Commission)日前公布了為期10年、耗資11.9億歐元的研究項目，旨在模擬完整的人腦結構。該項目的成本約為大型強子對撞機(Large Hadron Collider)項目的六分之一，是規模最大的神經學研究項目。它是人類朝著加深對大腦工作原理的了解邁出的重要一步，但也存在著缺陷。
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.
缺陷在于它的目標不現實。用科學期刊《自然》(Nature)的話說，人腦研究項目(Human Brain Project)提出了完全模擬人腦的“驚人目標，但也招致了一些懷疑”。盡管該項目具有寶貴的價值——例如，幫助研究人員測試精神疾病藥物的藥效——但人腦結構太過復雜，難以通過目前的計算機精確建模。根據多數人的估計，這一目標很可能在幾十年內都無法實現。
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.
神經學家馬特奧 卡蘭迪尼(Matteo Carandini)近期觀察發現，20多年來的人腦模擬試驗成果寥寥，部分原因在于對復雜系統的建模難以達到足夠的精度(試想預測兩周后天氣的難度有多大)。物理學家P W 安德森(P.W. Anderson)在發表于1972年一篇的經典文章中寫道：“人類能將一切事物簡化到基本定律，不代表人類能從這些定律出發、重構出宇宙……事物的復雜度每變化一級，都會呈現出全新的性質。”大模型是可能實現的，但模型越復雜，對計算能力的要求就越高，出現誤差的可能性也就越大。即便計算機處理能力繼續以每18至24個月翻一番的速度發展，真正實現精確、完全的人腦模擬需要的時間可能也遠不止十年。
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，即使計算能力足夠強，我們對神經元單獨工作和互相協作的原理也缺乏足夠的認識。
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.
我們仍然欠缺基礎性的認識(如大腦如何對記憶編碼)。我們很難模擬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，人腦研究項目仍能夠促進諸多有用科學的發展。關鍵的問題是：資金如何使用?建立起的基礎研究架構將服務于數目繁多的科研項目，資金將支持80多家機構的250多名科學家，他們各有自己的研究計劃。包括亞丁 杜達伊(Yadin Dudai，專攻記憶)、塞斯 格蘭特(Seth Grant，研究神經功能的遺傳和進化)和斯坦尼斯拉斯 德阿納(Stanislas Dehaene，研究數學和意識的大腦意識)在內的許多人，都是非常杰出的科學家。
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.
分門別類地對大腦各個部位編制目錄，雖有新聞價值，卻難以實現。人腦研究項目致力于實現這一目標，可能會浪費一部分預算經費。讓我們做一個類比：設想500年前有一臺筆記本電腦掉落在地球上，世界上最優秀的科學家試圖揭秘它的工作原理。一種方法是“解剖”這臺電腦，記錄線路與晶體管的連接方式，并研制出顯微鏡和邏輯探頭等工具，以徹底理解它的復雜結構。另一種方法則是運用軟件探究它的功能。人們會希望結合功能(筆記本電腦可以做什么)和物理結構(電路的工作原理)這兩個層面來理解它。單憑測量零件的尺寸，很難重新造出一臺功能齊全的筆記本電腦。
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.
現代神經學充斥著對軸突、樹突和神經傳遞素，以及鈣離子通道(它使神經元發揮作用)等技術機制的討論。但至于這些要素如何相互協調，傳遞思想、表情和動作，人們知之甚少。對于短暫記憶等基本現象的認識也仍然十分匱乏。目前，人腦研究項目似乎過于重視實現物理上的理解，而輕視了功能上的理解。要想真正了解大腦，需要將二者融為一體。
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