neural

简明释义

英[ˈnjʊərəl]美[ˈnʊrəl]

adj. 神经的，神经系统的

【名】 （Neural）（捷）诺伊拉尔（人名）

英英释义

单词用法

同义词

反义词

例句

1.This metabolic cost may arise from the muscular and neural activity involved in singing or from behaviors associated with singing.

这种代谢消耗可能是由于唱歌或与唱歌相关的行为中涉及的肌肉和神经活动所产生的。

2.And the neural real estate devoted to vision increases.

神经系统从此便专注于视觉的提高。

3.The neural activity of the brain will become intensified.

大脑的神经活动将会增强。

4.Instead it will lead to what you might call neural Buddhism.

相反，它会导致另一种形式，我们可试称为神经佛教。

5.By manipulating the amount of money on offer in each situation, Cohen and his collaborators could watch this neural tug of war unfold.

通过操纵每种情况下提供的资金数量，科恩和他的合作者可以观察到这种神经拉锯战的展开。

6.Repetitive neural responses may enhance recall of faces and words.

重复的神经反应可能会加深人们对面孔与词语的回忆。

7.Learning requires the brain to create new neural networks.

学习需要大脑去创造神经网络。

8.The research was published in the Journal of Neural Engineering.

该研究成果发表在《神经工程学》杂志上。

9.Scientists are exploring how neural 神经的 networks can mimic human thought processes.

科学家们正在探索如何让神经的网络模拟人类思维过程。

10.Advancements in neural 神经的 engineering are paving the way for new treatments.

在神经的工程方面的进步为新的治疗方法铺平了道路。

11.The brain's neural 神经的 pathways are crucial for learning and memory.

大脑的神经的通路对学习和记忆至关重要。

12.The neural 神经的 network of the human body is incredibly complex.

人体的神经的网络极其复杂。

13.The study of neural 神经的 connections helps us understand various neurological disorders.

研究神经的连接有助于我们理解各种神经系统疾病。

作文

In recent years, the field of artificial intelligence has made tremendous strides, particularly in the development of neural networks. These systems are inspired by the human brain and are designed to recognize patterns and make decisions based on data. The term neural refers to anything related to the nerves or the nervous system, which is crucial for processing information in living organisms. Just as our brains utilize a complex network of neurons to transmit signals, neural networks in AI use layers of interconnected nodes to process inputs and generate outputs.The architecture of a typical neural network consists of an input layer, one or more hidden layers, and an output layer. Each node in these layers acts similarly to a neuron, receiving input, applying a mathematical function, and passing the output to the next layer. This hierarchical structure allows neural networks to learn from vast amounts of data, adjusting the connections between nodes based on the strength of the signals they receive. This process, known as training, enables the network to improve its accuracy over time.One of the most significant advantages of using neural networks is their ability to handle unstructured data, such as images, audio, and text. Traditional algorithms often struggle with this type of data, but neural networks excel due to their flexibility. For instance, in image recognition tasks, a neural network can identify objects by learning features at different levels of abstraction—from simple shapes in the early layers to complex patterns in the deeper layers.Moreover, neural networks have been instrumental in advancements in various fields, including healthcare, finance, and autonomous vehicles. In healthcare, they assist in diagnosing diseases by analyzing medical images, while in finance, they help detect fraudulent transactions by recognizing unusual patterns in data. The application of neural networks in self-driving cars allows these vehicles to interpret their surroundings and make real-time decisions, showcasing their potential to revolutionize industries.However, the rise of neural networks also raises important ethical considerations. As these systems become more integrated into our daily lives, issues such as bias in training data and the transparency of decision-making processes come to the forefront. If a neural network is trained on biased data, it may perpetuate those biases in its predictions, leading to unfair outcomes. Therefore, it is crucial for researchers and practitioners to ensure that neural networks are trained on diverse datasets and that their operations are transparent and explainable.In conclusion, the concept of neural networks represents a remarkable intersection of biology and technology. By mimicking the way our brains work, these systems have opened up new possibilities for machine learning and artificial intelligence. As we continue to explore the capabilities of neural networks, it is essential to address the ethical implications of their use, ensuring that they serve humanity positively and equitably. The future of technology is undoubtedly intertwined with the advancements in neural systems, making it an exciting area for ongoing research and development.

近年来，人工智能领域取得了巨大的进步，特别是在神经网络的发展方面。这些系统受到人脑的启发，旨在识别模式并根据数据做出决策。术语神经指与神经或神经系统相关的任何事物，这对于处理生物体中的信息至关重要。就像我们的脑利用复杂的神经元网络传递信号一样，人工智能中的神经网络也使用层次相互连接的节点来处理输入并生成输出。典型的神经网络的架构由输入层、一个或多个隐藏层和输出层组成。这些层中的每个节点都类似于神经元，接收输入，应用数学函数，并将输出传递给下一层。这种分层结构使得神经网络能够从大量数据中学习，根据接收到的信号强度调整节点之间的连接。这个过程被称为训练，使网络能够随着时间的推移提高其准确性。使用神经网络的一个显著优势是它们能够处理非结构化数据，如图像、音频和文本。传统算法通常在处理这类数据时遇到困难，但神经网络由于其灵活性而表现出色。例如，在图像识别任务中，神经网络可以通过学习不同抽象层次的特征来识别对象——从早期层中的简单形状到深层中的复杂模式。此外，神经网络在医疗、金融和自动驾驶汽车等多个领域的进展中发挥了重要作用。在医疗保健中，它们通过分析医学图像来帮助诊断疾病，而在金融中，它们通过识别数据中的异常模式来帮助检测欺诈交易。神经网络在自动驾驶汽车中的应用使这些车辆能够解释周围环境并做出实时决策，展示了它们变革行业的潜力。然而，神经网络的崛起也引发了重要的伦理考虑。随着这些系统越来越多地融入我们的日常生活，训练数据中的偏见以及决策过程的透明性等问题开始浮现。如果神经网络是在有偏见的数据上训练的，它可能会在预测中延续这些偏见，导致不公平的结果。因此，研究人员和从业者必须确保神经网络在多样化的数据集上进行训练，并且它们的操作是透明和可解释的。总之，神经网络的概念代表了生物学和技术的显著交汇点。通过模仿我们大脑的工作方式，这些系统为机器学习和人工智能开辟了新的可能性。随着我们继续探索神经网络的能力，解决其使用的伦理影响至关重要，确保它们积极、公平地服务于人类。技术的未来无疑与神经系统的进步密切相关，使其成为持续研究和发展的激动人心的领域。