In this survey, we examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. These processes are unified under one possible taxonomy, which 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 schemes 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 an important opportunity to build a strong bridge between machine learning, computational neuroscience, and cognitive science.

翻译：本综述考察了受神经生物学启发或驱动的人工神经网络中用于进行信用分配的算法。我们基于学习算法如何回答一个核心问题——该问题支撑复杂自适应神经元系统中突触可塑性的机制——即驱动网络中单个元素学习的信号来自何处以及如何产生，将这些过程统一在一个可能的分类体系下。在这一统一处理框架中，我们将不断增长的脑启发学习方案组织成六大类，并结合误差反向传播及其已知批评对其展开考量。本综述的结果旨在促进神经模拟系统及其构成学习过程的未来发展，这为在机器学习、计算神经科学与认知科学之间搭建强有力桥梁提供了重要机遇。