Название: Modeling Neural Circuits Made Simple with Python
Автор: Robert Rosenbaum
Издательство: The MIT Press
Год: 2024
Страниц: 168
Язык: английский
Формат: epub (true)
Размер: 10.1 MB

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.

This book assumes a basic background in calculus (derivatives and integrals), linear algebra (matrix products and eigenvalues), and probability or statistics (expectations and variance). All other mathematical background is covered in appendix A.

Figures in the book are accompanied by Python notebooks containing the code needed to reproduce the figure. See the book’s website for links to the Python notebooks. The file name of each Python notebook is referenced in the associated figure caption. If you are not familiar with Python or NumPy, or if you just need a review, see the notebook PythonIntro.ipynb for a brief introduction to Python programming.

The main text of this book was whittled down to a minimal thread of material needed to build a backbone for modeling neural circuits. This approach assures that the book can be covered in a one-semester course without rushing, and it also makes the book amenable to self-learning. Some important topics and models that were omitted from the main text are covered in appendix B.

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




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