Introduction to Neural Networks
List price: $75.00
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Description: 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.
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
List price: $75.00
Copyright year: 1995
Publisher: MIT Press
Publication date: 3/16/1995
Size: 7.50" wide x 9.25" long x 1.75" tall
James A. Anderson is Professor in the Department of Cognitive and Linguistic Sciences at Brown University.
|Properties of Single Neurons|
|Synaptic Integration and Neuron Models|
|Essential Vector Operations|
|Lateral Inhibition and Sensory Processing|
|Simple Matrix Operations|
|The Linear Associator: Background and Foundations|
|The Kinear Associator: Simulations|
|Early Network Models: The Perceptron|
|Gradient Descent Algorithms|
|Representation of Information|
|Applications of Simple Associators: Concept Formation and Object Motion|
|Energy and Neural Networks: Hopfield Networks and Boltzmann Machines|
|Nearest Neighbor Models|
|The BSB Model: A Simple Nonlinear Autoassociative Neural Network|
|Teaching Arithmetic to a Neural Network|