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Introduction to Neural and Cognitive Modeling / Edition 2

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Overview

This thoroughly, thoughtfully revised edition of a very successful textbook makes the principles and the details of neural network modeling accessible to cognitive scientists of all varieties as well as to others interested in these models. Research since the publication of the first edition has been systematically incorporated into a framework of proven pedagogical value.

Features of the second edition include:

• A new section on spatiotemporal pattern processing

• Coverage of ARTMAP networks (the supervised version of adaptive resonance networks) and recurrent back-propagation networks

• A vastly expanded section on models of specific brain areas, such as the cerebellum, hippocampus, basal ganglia, and visual and motor cortex

• Up-to-date coverage of applications of neural networks in areas such as combinatorial optimization and knowledge representation

As in the first edition, the text includes extensive introductions to neuroscience and to differential and difference equations as appendices for students without the requisite background in these areas. As graphically revealed in the flowchart in the front of the book, the text begins with simpler processes and builds up to more complex multilevel functional systems.

For more information visit the author's personal Web site at www.uta.edu/psychology/faculty/levine/

Incl. basic science, associative learning & synap- tic plasticity, memory, attention, categorization, more.

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Product Details

  • ISBN-13: 9780805820058
  • Publisher: Taylor & Francis
  • Publication date: 2/1/2000
  • Edition description: Revised
  • Edition number: 2
  • Pages: 512
  • Lexile: 1420L (what's this?)
  • Product dimensions: 6.14 (w) x 9.21 (h) x 1.13 (d)

Table of Contents

Contents: Preface. Preface to the Second Edition. Brain and Machine: The Same Principles? Historical Outline. Associative Learning and Synaptic Plasticity. Competition, Lateral Inhibition, and Short-Term Memory. Conditioning, Attention, and Reinforcement. Coding and Categorization. Optimization, Control, Decision, and Knowledge Representation. A Few Recent Technical Advances. Appendices: Basic Facts of Neurobiology. Difference and Differential Equations in Neural Networks.

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