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