Information is an important resource. Storing and retrieving information faithfully are huge challenges and many methods have been developed to understand the principles behind robust information processing. In this review, we focus on information storage and retrieval from the perspective of energetics, dynamics, and statistical mechanics. We first review the Hopfield model of associative memory, the classic energy-based model of memory. We then discuss generalizations and physical realizations of the Hopfield model. Finally, we highlight connections to energy-based neural networks used in deep learning. We hope this review inspires new directions along the lines of information storage and retrieval in physical systems.