This book chapter explores the dynamics of continual learning, a process where models incrementally learn from a non-stationary stream of data. While humans excel at this, artificial neural networks struggle due to a phenomenon known as catastrophic forgetting, where new learning overwrites previous knowledge. The chapter reviews the challenges and recent advancements in addressing this issue, highlighting the practical implications and potential applications of successful continual learning methods. It discusses key features of effective continual learning, such as rapid adaptation, task similarity exploitation, task agnosticism, noise tolerance, and resource efficiency. The chapter also delves into different types of continual learning scenarios, including task-based and task-free approaches, and evaluates various computational strategies like replay, parameter regularization, functional regularization, optimization-based approaches, and context-dependent processing. Each strategy is analyzed in terms of its effectiveness and trade-offs, providing insights into the ongoing research and future directions in the field.This book chapter explores the dynamics of continual learning, a process where models incrementally learn from a non-stationary stream of data. While humans excel at this, artificial neural networks struggle due to a phenomenon known as catastrophic forgetting, where new learning overwrites previous knowledge. The chapter reviews the challenges and recent advancements in addressing this issue, highlighting the practical implications and potential applications of successful continual learning methods. It discusses key features of effective continual learning, such as rapid adaptation, task similarity exploitation, task agnosticism, noise tolerance, and resource efficiency. The chapter also delves into different types of continual learning scenarios, including task-based and task-free approaches, and evaluates various computational strategies like replay, parameter regularization, functional regularization, optimization-based approaches, and context-dependent processing. Each strategy is analyzed in terms of its effectiveness and trade-offs, providing insights into the ongoing research and future directions in the field.