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.

该论文探讨了记忆增强神经网络（MANNs），研究它们如何将类人记忆过程融入人工智能中，涵盖了感知记忆、短期记忆和长期记忆等不同类型的记忆，并将心理理论与人工智能应用联系起来。研究调查了包括霍普菲尔德网络、神经图灵机、相关矩阵记忆、记忆形态神经网络和神经注意力记忆在内的先进架构，解释了它们的工作原理和优势所在。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等现实世界应用中的使用情况，展示了记忆增强器如何提高人工智能任务的准确性、效率和可靠性。总体而言，本综述全面介绍了 MANNs，为基于内存的人工智能系统的未来研究提供了深入见解。