Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened understanding of the brain, such as the inspiration of neocognitron, which is the pioneering work of convolutional neural networks. Per the motives of the emerging field: NeuroAI, a great amount of neuroscience knowledge can help catalyze the next generation of AI by endowing a network with more powerful capabilities. As we know, the human brain has numerous morphologically and functionally different neurons, while artificial neural networks are almost exclusively built on a single neuron type. In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. Since an artificial network is a miniature of the human brain, introducing neuronal diversity should be valuable in terms of addressing those essential problems of artificial networks such as efficiency, interpretability, and memory. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron. Then, we review studies of designing new neurons for artificial networks. Next, we discuss what gains can neuronal diversity bring into artificial networks and exemplary applications in several important fields. Lastly, we discuss the challenges and future directions of neuronal diversity to explore the potential of NeuroAI.

翻译：摘要：纵观历史，人工智能的发展，尤其是人工神经网络，始终向对大脑日益深入的理解开放并不断受其启发，例如作为卷积神经网络开创性工作的“新认知机”便是一例。依据新兴领域——神经人工智能的动机，大量神经科学知识可通过赋予网络更强大的能力，助力催生下一代人工智能。众所周知，人类大脑拥有大量形态和功能各异的神经元，而人工神经网络几乎完全基于单一神经元类型构建。在大脑中，神经元多样性是各类生物智能行为的关键促成因素。由于人工网络是大脑的缩影，引入神经元多样性在解决效率、可解释性和记忆等人工网络核心问题方面应具有重要价值。在本入门指南中，我们首先讨论生物神经元多样性的基础知识，以及生物神经元中信息传递和处理的特征。接着，我们回顾了为人工网络设计新神经元的相关研究。随后，我们探讨神经元多样性可为人工网络带来哪些增益，以及在若干重要领域的典型应用案例。最后，我们讨论了神经元多样性面临的挑战与未来方向，以探索神经人工智能的潜力。