欢迎访问贝贝英语网！
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结合起来，创建了一个工作的生物计算机。
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语音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.” 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.
团队制造了一种被称为“脑器官体”的类似大脑的组织。哈佛大学干细胞研究所解释说，器官体是一组个性化的复杂细胞，可以在实验室中从干细胞中培养出来。
Under the right laboratory conditions, organoids can be made to look and even work similarly to real human tissue and organs. In this process, stem cells “can follow their own genetic instructions to self-organize,” the Stem Cell Institute says.
在适当的实验室条件下，器官体可以被制成看起来甚至工作方式都类似于真实人体组织和器官。在这个过程中，干细胞“可以遵循它们自己的遗传指令进行自我组织”，干细胞研究所表示。
So far, scientists have been able to produce organoids that look like, or resemble, some organs. These organs include the brain, kidney, lung, stomach and liver. Such lab-created organoids are generally used to study how organs work without needing to experiment on actual organs.
到目前为止，科学家已经能够制造出看起来或类似于某些器官的器官体，包括大脑、肾脏、肺、胃和肝脏等。这些实验室制造的器官体通常用于研究器官的工作方式，而无需对真实器官进行实验。
In the biocomputer experiment, the team said stem cells were able to form neurons similar to those found in the human brain. Neurons are electrically charged cells that transport signals to the brain and other parts of the body.
在生物计算机实验中，团队表示，干细胞能够形成类似于人脑中的神经元。神经元是带电细胞，传递信号到大脑和身体的其他部位。
Feng Guo led the experiment. He is a bioengineer and professor of Intelligent System Engineering at Indiana University Bloomington. His team recently published their research results in a study in Nature Electronics.
郭峰领导了这个实验。他是印第安纳大学布卢明顿分校的生物工程师和智能系统工程学教授。他的团队最近在《自然电子学》杂志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. The researchers call this system Brainoware. The system was used to establish communication between the organoid and electronic circuits. An artificial intelligence (AI) tool was used to help read the neural activity of the organoid.
研究人员将大脑器官体连接到一组传统的电子计算电路上。研究人员将这个系统称为Brainoware。该系统用于在器官体和电子电路之间建立通信。人工智能工具被用来帮助读取器官体的神经活动。
The scientists aim to build “a bridge between AI and organoids,” Guo explained to Nature. Guo believes that combining organoids and computer circuits could provide additional speed and energy to improve the performance of AI computing systems.
科学家们的目标是建立“人工智能和器官体之间的桥梁”，郭峰向《自然》解释说。郭峰认为，结合器官体和计算机电路可以提供额外的速度和能量，以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. For example, the researchers said humans generally have a faster learning ability and use less energy thinking than computers do.
研究指出，增加人类大脑的力量可能有助于机器在某些方面的表现不如人类。例如，研究人员表示，人类通常具有比计算机更快的学习能力，并且在思考时使用的能量较少。
During one part of the experiment, the team tested the Brainoware system’s voice recognition ability. The team trained the system on 240 recordings of eight different voices. The researchers said the organoid produced different neural signals in reaction to the different voices. The accuracy level of the system reached 78 percent, Guo said.
在实验的一个部分，团队测试了Brainoware系统的语音识别能力。团队对该系统进行了240个不同声音的八个不同录音的训练。研究人员表示，器官体对不同的声音产生了不同的神经信号。郭峰说，系统的准确性达到了78%。
“This is the first demonstration of using brain organoids [for computing],” Guo told MIT Technology Review. He added, “It’s exciting to see the possibilities of organoids for biocomputing in the future.”
“这是首次演示使用大脑器官体进行计算，”郭峰告诉麻省理工科技评论。他补充说：“看到器官体在生物计算中的未来可能性是令人
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. But he noted the best accuracy rates recorded by the Brainoware system were still below the accuracy rates of traditional AI networks. Guo said this is one of the things his team plans to try to improve.
郭峰表示，这些结果使他的团队相信，大脑计算机系统可以提高计算性能，特别是对于一些人工智能工作。但他注意到，Brainoware系统记录的最佳准确率仍然低于传统人工智能网络的准确率。郭峰表示，这是他的团队计划尝试改进的事项之一。
Lena Smirnova is a developmental neuroscientist at Johns Hopkins University in Baltimore, Maryland. She told Nature that more research will be needed to improve such systems. But she said, “The study confirms some key theoretical ideas that could eventually make a biological computer possible.”
莉娜·斯米尔诺娃是马里兰州巴尔的摩约翰斯·霍普金斯大学的发育神经科学家。她告诉《自然》杂志，需要进行更多的研究以改善这样的系统。但她说：“这项研究证实了一些关键的理论观念，最终可能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. But the latest study, she said, was the first to demonstrate this kind of performance in a brain organoid.
斯米尔诺娃指出，在早期的实验中，研究人员曾使用其他类型的神经元细胞执行类似的计算活动。但她表示，最新的研究是第一个在大脑器官体中展示这种性能的研究。
_____________________________________________
Words in This Story
instruction – n. a set of steps for how to do something
circuit – v. a complete circle an electric current travels around
accurate – adj. correct or exact
theoretical – adj. based on the ideas that relate to a subject, not the practical uses of that subject
