In this survey, we examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology, unifying these various processes under one possible taxonomy. Our proposed taxonomy is constructed based on how a learning algorithm answers a central question underpinning the mechanisms of synaptic plasticity in complex adaptive neuronal systems: where do the signals that drive the learning in individual elements of a network come from and how are they produced? In this unified treatment, we organize the ever-growing set of brain-inspired learning processes into six general families and consider these in the context of backpropagation of errors and its known criticisms. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes, wherein lies the opportunity to build a strong bridge between machine learning, computational neuroscience, and cognitive science.

翻译：本综述系统考察了受神经生物学启发或驱动的人工神经网络信用分配算法，并将这些多样化过程统一纳入一种可能的分类体系。我们提出的分类框架基于一个核心问题构建——学习算法如何回答驱动复杂自适应神经元系统中突触可塑性机制的根本性问题：网络中单个元素学习信号的来源及其产生机制？通过这种统一视角，我们将日益增长的脑启发学习过程归纳为六大类，并结合误差反向传播及其已知缺陷进行探讨。本综述旨在促进神经拟态系统及其学习过程的未来发展，为机器学习、计算神经科学与认知科学之间架设坚实桥梁提供契机。