The book *Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems* by Peter Dayan and L. F. Abbott is a significant contribution to the field of computational neuroscience. It fills a gap by providing a comprehensive resource that integrates the basic methods and models of computational neuroscience, spanning from cellular-level models to cognitive-level models. Unlike other texts that focus on either engineering, math, or physics perspectives, this book offers a deep and thorough exploration of neural coding techniques and recent advances in unsupervised learning models. The first six chapters focus on descriptive models and computational techniques to characterize neural function, including methods for spike statistics, reverse correlation, information theory, and detailed electrical models of neurons. The last four chapters delve into more abstract models, such as recurrent network dynamics, learning and adaptation, and probabilistic theories of representation, providing a theoretical framework for interpreting data and motivating future experiments. The book is accessible to a wide audience, requiring only first-year calculus and simple linear algebra. It effectively balances mathematical rigor with clear explanations, making it suitable for graduate students in neuroscience and psychology, as well as advanced undergraduates. The reviewer, Bruno A. Olshausen from the University of California–Davis, predicts that this book will become a staple for courses on computational neuroscience in the coming years.The book *Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems* by Peter Dayan and L. F. Abbott is a significant contribution to the field of computational neuroscience. It fills a gap by providing a comprehensive resource that integrates the basic methods and models of computational neuroscience, spanning from cellular-level models to cognitive-level models. Unlike other texts that focus on either engineering, math, or physics perspectives, this book offers a deep and thorough exploration of neural coding techniques and recent advances in unsupervised learning models. The first six chapters focus on descriptive models and computational techniques to characterize neural function, including methods for spike statistics, reverse correlation, information theory, and detailed electrical models of neurons. The last four chapters delve into more abstract models, such as recurrent network dynamics, learning and adaptation, and probabilistic theories of representation, providing a theoretical framework for interpreting data and motivating future experiments. The book is accessible to a wide audience, requiring only first-year calculus and simple linear algebra. It effectively balances mathematical rigor with clear explanations, making it suitable for graduate students in neuroscience and psychology, as well as advanced undergraduates. The reviewer, Bruno A. Olshausen from the University of California–Davis, predicts that this book will become a staple for courses on computational neuroscience in the coming years.