Understanding the representations of artificial or biological neural networks is crucial in discovering the neural information processing mechanisms of the brain. Representational Similarity Analysis (RSA), is an analytical framework in computational and cognitive neuroscience, comparing models and brains in terms of their representational geometries. Representational Similarity Analysis: Unlocking the Neural Representations of Brains and Machines is the first book on representational similarity analysis, surveying the advances in computational neuroscience. This book is organized into five distinct sections. The first, introduces the reader to representation patterns and relation to neuroscience and psychology. The second section explores how to understand the data including data modalities in both modern neuroscience and AI research. The third section, reviews Representational similarity analysis (RSA) in depth, covering all aspects from metrics, interpretation and modeling. Next, section offers tutorials of RSA computations including setup, case studies and practical considerations. The last section summaries the possible future frontiers of representational studies. Provides a timely and comprehensive review of representational similarity analysis Proposes a new unified formulation of statistical inference methods for comparing brain and model representations Covers a vast array of special topics and applications including both vision and language to help illustrate the wide use in understanding neural information processing Presents convincing case studies and hands-on tutorials for a broad audience of scientists including neuroscience, psychology, and computer science