Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs' operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to enhance these networks' capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. Ultimately, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence.

翻译：人工神经网络已成为机器学习中的核心工具，在图像与语音生成、博弈对弈及机器人技术等多元领域取得了显著成功。然而，人工神经网络与生物大脑的工作机制存在根本性差异，尤其体现在学习过程中。本文系统综述了当前人工神经网络中脑启发式学习表征的研究进展。我们探究了整合更具生物合理性的机制（如突触可塑性）以增强网络能力的实现路径，并深入剖析了该方法的潜在优势与挑战。最后，我们指出了这一快速演进领域中具有前景的研究方向，这些探索将有望推动我们更接近智能本质的理解。