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.

翻译：长期以来，生物学和神经科学领域一直是计算机科学家开发人工智能技术的重要灵感来源。本综述旨在全面回顾近期受生物学启发的人工智能方法。在介绍生物神经元中计算与突触可塑性的主要原理后，我们详细阐述了脉冲神经网络模型，并重点介绍了与SNN训练相关的主要挑战——传统基于反向传播的优化方法在此不直接适用。因此，我们讨论了近期作为反向传播替代方案的受生物学启发的训练方法，这些方法既适用于传统网络也适用于脉冲网络。受生物学启发的深度学习方法致力于提升当前模型的计算能力和生物合理性。