Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Peter Dayan and L. F. Abbott is a comprehensive textbook that fills a critical gap in the field of computational neuroscience by providing a unified treatment of the basic methods and models. The book is designed to serve as a primary resource for students and researchers, offering a thorough introduction to computational neuroscience. It spans a wide range of topics, from cellular-level models such as ion channel kinetics to cognitive-level models like reinforcement learning. The book also covers state-of-the-art techniques in neural coding and recent advances in unsupervised learning models. The book is divided into two main camps of computational neuroscience. The first six chapters focus on descriptive models that use mathematical and computational techniques to characterize neural function. These include methods for analyzing spike statistics, reverse correlation techniques, and information-theoretic approaches. The last four chapters present more abstract models that extrapolate beyond available data, discussing recurrent network models, learning and adaptation, and probabilistic models of representation. The authors have made the book accessible to a wide audience, with minimal mathematical prerequisites. While the book contains many equations, it explains them clearly and strives to maintain mathematical rigor without sacrificing clarity. The appendices provide additional background on topics such as linear algebra, statistics, and optimization. This book is suitable for graduate students in neuroscience and psychology, as well as advanced undergraduates. It has been successfully used in a graduate-level computational neuroscience course and is likely to become a standard text for many years to come.Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Peter Dayan and L. F. Abbott is a comprehensive textbook that fills a critical gap in the field of computational neuroscience by providing a unified treatment of the basic methods and models. The book is designed to serve as a primary resource for students and researchers, offering a thorough introduction to computational neuroscience. It spans a wide range of topics, from cellular-level models such as ion channel kinetics to cognitive-level models like reinforcement learning. The book also covers state-of-the-art techniques in neural coding and recent advances in unsupervised learning models. The book is divided into two main camps of computational neuroscience. The first six chapters focus on descriptive models that use mathematical and computational techniques to characterize neural function. These include methods for analyzing spike statistics, reverse correlation techniques, and information-theoretic approaches. The last four chapters present more abstract models that extrapolate beyond available data, discussing recurrent network models, learning and adaptation, and probabilistic models of representation. The authors have made the book accessible to a wide audience, with minimal mathematical prerequisites. While the book contains many equations, it explains them clearly and strives to maintain mathematical rigor without sacrificing clarity. The appendices provide additional background on topics such as linear algebra, statistics, and optimization. This book is suitable for graduate students in neuroscience and psychology, as well as advanced undergraduates. It has been successfully used in a graduate-level computational neuroscience course and is likely to become a standard text for many years to come.