For a long time, biology and neuroscience fields have been a great source of
inspiration for computer scientists, towards the development of Artificial
Intelligence (AI) technologies. This survey aims at providing a comprehensive
review of recent biologically-inspired approaches for AI. After introducing the
main principles of computation and synaptic plasticity in biological neurons,
we provide a thorough presentation of Spiking Neural Network (SNN) models, and
we highlight the main challenges related to SNN training, where traditional
backprop-based optimization is not directly applicable. Therefore, we discuss
recent bio-inspired training methods, which pose themselves as alternatives to
backprop, both for traditional and spiking networks. Bio-Inspired Deep Learning
(BIDL) approaches towards advancing the computational capabilities and
biological plausibility of current models.

该论文综述了近期生物学启发的人工智能方法，重点介绍了脉冲神经网络模型及其训练中的挑战，以及用于传统网络和脉冲网络的生物启发式训练方法，以推进当前模型的计算能力和生物合理性。