Towards NeuroAI: introducing neuronal diversity into artificial neural networks

Abstract Throughout history, the development of artificial intelligence, especially artificial neural networks, has been continuously influenced by a deeper understanding of the brain. This influence includes the development of the neocognitron, considered a precursor to convolutional neural networks. The emerging field of NeuroAI posits that leveraging neuroscience knowledge could significantly advance AI by imbuing networks with enhanced capabilities. Unlike the human brain, which features a variety of morphologically and functionally distinct neurons, artificial neural networks typically rely on a homogeneous neuron model. In the human brain, the diversity of neurons facilitates a wide range of intelligent behaviors. Given that artificial networks aim to mimic the human brain, incorporating a diversity of neuron models could address key challenges in artificial intelligence, such as efficiency, interpretability, and memory capacity. This perspective begins by examining the basics of biological neuronal diversity and how biological neurons transmit and process information. We then explore research efforts to design novel neuron models for artificial networks and discuss the potential benefits of neuronal diversity, including applications in several critical areas. Finally, we address the challenges and future directions for integrating neuronal diversity into artificial networks, highlighting its potential to enrich NeuroAI.