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Modeling Neural Circuits Made Simple with Python
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Product Details
Author:
Robert Rosenbaum
Format:
Paperback
Pages:
168
Publisher:
MIT Press (March 19, 2024)
Language:
English
ISBN-13:
9780262548083
ISBN-10:
0262548089
Weight:
12.6oz
Dimensions:
7" x 10" x 0.41"
File:
RandomHouse-PRH_Book_Company_PRH_PRT_Onix_full_active_D20260405T165752_155746804-20260405.xml
Folder:
RandomHouse
List Price:
$45.00
Series:
Computational Neuroscience Series
Case Pack:
32
As low as:
$34.65
Publisher Identifier:
P-RH
Discount Code:
A
QuickShip:
Yes
Audience:
General/trade
Country of Origin:
United States
Pub Discount:
65
Imprint:
The MIT Press
Overview
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.
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
