  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.
