An accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons.
Understanding the brain is a major frontier of modern science. Given the complexity of neural circuits, advancing that understanding requires mathematical and computational approaches. This accessible undergraduate textbook in computational neuroscience provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Starting with the biophysics of single neurons, Robert Rosenbaum incrementally builds to explanations of neural coding, learning, and the relationship between biological and artificial neural networks. Examples with real neural data demonstrate how computational models can be used to understand phenomena observed in neural recordings. Based on years of classroom experience, the material has been carefully streamlined to provide all the content needed to build a foundation for modeling neural circuits in a one-semester course.
• Proven in the classroom • Example-rich, student-friendly approach • Includes Python code and a mathematical appendix reviewing the requisite background in calculus, linear algebra, and probability • Ideal for engineering, science, and mathematics majors and for self-study
Robert Rosenbaum is Associate Professor of Applied and Computational Mathematics and Statistics at the University of Notre Dame. His research in computational neuroscience is focused on using computational models of neural circuits to help understand the dynamics and statistics of neural activity underlying sensory processing and learning.
Well written and accessible, this book takes the reader from single neurons and synapses to models of whole networks—the latter typically (and unfortunately) missing from most textbooks. Suitable for advanced undergraduates but also a must-read for just about every neuroscientist on the planet.
Peter Latham, Professor, Gatsby Computational Neuroscience Unit, University College London