This paper explores Memory-Augmented Neural Networks (MANNs), delving into how they blend human-like memory processes into AI. It covers different memory types, like sensory, short-term, and long-term memory, linking psychological theories with AI applications. The study investigates advanced architectures such as Hopfield Networks, Neural Turing Machines, Correlation Matrix Memories, Memformer, and Neural Attention Memory, explaining how they work and where they excel. It dives into real-world uses of MANNs across Natural Language Processing, Computer Vision, Multimodal Learning, and Retrieval Models, showing how memory boosters enhance accuracy, efficiency, and reliability in AI tasks. Overall, this survey provides a comprehensive view of MANNs, offering insights for future research in memory-based AI systems.

翻译：本文探讨了记忆增强神经网络（MANNs），深入研究了它们如何将类人记忆过程融入人工智能。论文涵盖了不同类型的记忆，如感觉记忆、短期记忆和长期记忆，并将心理学理论与人工智能应用联系起来。研究调查了诸如霍普菲尔德网络、神经图灵机、相关矩阵记忆、Memformer和神经注意力记忆等先进架构，解释了它们的工作原理及其擅长领域。论文深入探讨了MANNs在自然语言处理、计算机视觉、多模态学习和检索模型中的实际应用，展示了记忆增强如何提升人工智能任务的准确性、效率和可靠性。总体而言，本综述提供了对MANNs的全面视角，为未来基于记忆的人工智能系统研究提供了见解。