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American researchers have combined lab-grown human brain tissue with computer hardware to create a working biocomputer.
美国研究人员将实验室培养的人脑组织与计算机硬件相结合，创造了一台可以工作的生物计算机。
The scientists say brain cells used in the experiment were able to recognize speech and complete simple math problems.
科学家表示，实验中使用的脑细胞能够识别言语并完成简单的数学问题。
The team made brain-like tissue that took the form of what they called a "brain organoid."
该团队制造出了类似大脑的组织，他们称之为“类大脑器官”。
Harvard University's Stem Cell Institute explains that an organoid is a collection of individualized, complex cells that can be grown from stem cells in a lab.
哈佛大学干细胞研究所解释说，类器官是一组个性化的复杂细胞，可以在实验室中由干细胞培养出来。
Under the right laboratory conditions, organoids can be made to look and even work similarly to real human tissue and organs.
在适当的实验室条件下，可以培养出与真正的人体组织和器官外观和功能相似的类器官。
In this process, stem cells "can follow their own genetic instructions to self-organize," the Stem Cell Institute says.
该干细胞研究所表示，在这个过程中，干细胞“可以遵循自己的遗传指令进行自我组织”。
So far, scientists have been able to produce organoids that look like, or resemble, some organs.
到目前为止，科学家已经能够制造出外观与某些器官相似的类器官。
These organs include the brain, kidney, lung, stomach and liver.
其中包括脑、肾、肺、胃和肝。
Such lab-created organoids are generally used to study how organs work without needing to experiment on actual organs.
这种实验室制造的类器官通常用于研究器官如何工作，从而无需在真正的器官上进行实验。
In the biocomputer experiment, the team said stem cells were able to form neurons similar to those found in the human brain.
在生物计算机实验中，该团队表示，干细胞能够形成类似于在人类大脑中发现的神经元。
Neurons are electrically charged cells that transport signals to the brain and other parts of the body.
神经元是一种带电细胞，负责向大脑和身体其他部位传递信号。
Feng Guo led the experiment.
冯国领导了这项实验。
He is a bioengineer and professor of Intelligent System Engineering at Indiana University Bloomington.
他是印第安纳大学布伯明顿分校的生物工程师和智能系统工程教授。
His team recently published their research results in a study in Nature Electronics.
他的团队最近在《自然·电子学》期刊上发表了他们的研究成果。
The researchers attached the brain organoid to a set of traditional electronic computing circuits.
研究人员将大脑类器官连接到一组传统的电子计算电路上。
The researchers call this system Brainoware.
研究人员称这个系统为Brainoware。
The system was used to establish communication between the organoid and electronic circuits.
该系统被用于在类器官和电子电路之间建立通信。
An artificial intelligence (AI) tool was used to help read the neural activity of the organoid.
人工智能工具被用来帮助读取类器官的神经活动。
The scientists aim to build "a bridge between AI and organoids," Guo explained to Nature.
冯国向《自然》期刊解释说，科学家的目标是在“人工智能和类器官之间架起一座桥梁。”
Guo believes that combining organoids and computer circuits could provide additional speed and energy to improve the performance of AI computing systems.
冯国认为，将类器官和计算机电路相结合可以提供额外的速度和能量来提高人工智能计算系统的性能。
The study notes that adding human brain power might be able to help machines with the things they do not do as well as people.
这项研究指出，增加人类的脑力或许能够帮助机器完成一些它们做得不如人类好的事情。
For example, the researchers said humans generally have a faster learning ability and use less energy thinking than computers do.
例如，研究人员表示，人类的学习能力通常比计算机快，但思考时使用的能量比计算机少。
During one part of the experiment, the team tested the Brainoware system's voice recognition ability.
在实验的一个阶段，该团队测试了Brainoware系统的语音识别能力。
The team trained the system on 240 recordings of eight different voices.
该团队用8种不同声音的240段录音对该系统进行了训练。
The researchers said the organoid produced different neural signals in reaction to the different voices.
研究人员表示，这种类器官会对不同的声音产生不同的神经信号。
The accuracy level of the system reached 78 percent, Guo said.
冯国说，该系统的准确率达到了78%。
"This is the first demonstration of using brain organoids [for computing]," Guo told MIT Technology Review.
冯国告诉《麻省理工科技评论》，“这是使用大脑类器官进行计算的首次演示。”
He added, "It's exciting to see the possibilities of organoids for biocomputing in the future."
他补充说：“看到类器官在未来用于生物计算的可能性是令人兴奋的。”
Guo said these results persuaded his team that a brain-computer system can work to improve computing performance, especially for some AI jobs.
冯国说，这些结果说服了他的团队，脑机系统可以提高计算性能，尤其是对于一些人工智能工作。
But he noted the best accuracy rates recorded by the Brainoware system were still below the accuracy rates of traditional AI networks.
但他指出，Brainoware系统记录的最佳准确率仍低于传统人工智能网络的准确率。
Guo said this is one of the things his team plans to try to improve.
冯国说，这是他的团队计划努力改进的事情之一。
Lena Smirnova is a developmental neuroscientist at Johns Hopkins University in Baltimore, Maryland.
莉娜·斯米尔诺娃是马里兰州巴尔的摩市约翰斯·霍普金斯大学的发育神经科学家。
She told Nature that more research will be needed to improve such systems.
她告诉《自然》杂志，需要更多的研究来改进这种系统。
But she said, "The study confirms some key theoretical ideas that could eventually make a biological computer possible."
但她说：“这项研究证实了一些关键的理论观点，这些观点最终可能会使生物计算机成为可能。”
Smirnova noted that in earlier experiments, researchers have used other kinds of neuron cells to perform similar computational activities.
斯米尔诺娃指出，在早期的实验中，研究人员已经使用其他类型的神经元细胞来执行类似的计算活动。
But the latest study, she said, was the first to demonstrate this kind of performance in a brain organoid.
但她说，最新的研究首次在大脑类器官中证明了这种性能。
I'm Bryan Lynn.
布莱恩·林恩为您播报。
译文为可可英语翻译，未经授权请勿转载！
