Artificial neurons are the basic building blocks of artificial neural networks, which are designed to mimic the way human brains process information. Each artificial neuron receives input data, processes it using a mathematical function, and produces an output. This output can then be passed to other neurons, allowing the network to learn and make decisions based on the data it receives. These artificial neurons are often organized in layers, with input layers receiving data, hidden layers processing it, and output layers delivering results. They are used in various applications, including machine learning, image recognition, and natural language processing, enabling computers to perform complex tasks.