- This title is eligible for Amazon Fall Textbook promotions. Get unlimited free Two-Day Shipping for three months with a free trial of Amazon Prime. Add $100 worth of eligible textbooks to your cart to qualify. Sign up at checkout. New members only. Here's how (restrictions apply)
Join Amazon Prime and ship Two-Day for free and Overnight for $3.99. Already a member? Sign in.
|
|||||||||
Special Offers and Product Promotions
Better Together
Customers Who Bought This Item Also Bought
Neural Networks: A Comprehensive Foundation (2nd Edition) by Simon Haykin  (21)
|
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Peter Dayan (6)
$29.39
|
Understanding 99% of Artificial Neural Networks: Introduction & Tricks by Marcelo Bosque  (28)
$12.95
|
Artificial Intelligence: A Modern Approach (2nd Edition) (Prentice Hall Series in Artificial Intelligence) by Stuart Russell (80)
$83.16
|
Introduction to Connectionist Modelling of Cognitive Processes by Peter McLeod  (1)
$89.50
|
Editorial Reviews
Product Description
Choice Outstanding Academic Title, 1996. An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas.
Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject.
The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.
About the Author
James A. Anderson is Professor in the Department of Cognitive and Linguistic Sciences at Brown University.
Choice Outstanding Academic Title, 1996. An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas.
Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject.
The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.
About the Author
James A. Anderson is Professor in the Department of Cognitive and Linguistic Sciences at Brown University.
Product Details
|