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

Table of Contents

1 Modeling Single Neurons

2 Measuring and Modeling Neural Variability

3 Modeling Networks of Neurons

4 Modeling Plasticity and Learning

About the Author

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.