Recently emerged technologies based on Deep Learning (DL) achieved outstanding results on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter several challenges related to robustness to adversarial inputs, ecological impact, and the necessity of huge amounts of training data. In response, researchers are focusing more and more interest on biologically grounded mechanisms, which are appealing due to the impressive capabilities exhibited by biological brains. This survey explores a range of these biologically inspired models of synaptic plasticity, their application in DL scenarios, and the connections with models of plasticity in Spiking Neural Networks (SNNs). Overall, Bio-Inspired Deep Learning (BIDL) represents an exciting research direction, aiming at advancing not only our current technologies but also our understanding of intelligence.

翻译：近期基于深度学习的技术在人工智能领域的多种任务上取得了卓越成果。然而，这些技术仍面临若干挑战，包括对对抗性输入的鲁棒性、生态影响以及对海量训练数据的依赖。为此，研究人员日益聚焦于生物启发的机制，这些机制因生物大脑展现出的惊人能力而备受关注。本综述系统探讨了一系列生物启发的突触可塑性模型、其在深度学习场景中的应用，以及它们与脉冲神经网络中可塑性模型的关联。总体而言，生物启发式深度学习不仅有望推动现有技术的进步，更能深化我们对智能本质的理解，是一个极具前景的研究方向。