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Neural Systems: Analysis and Modeling

  • Frank H. Eeckman

Table of contents

  1. Front Matter
    Pages i-ix
  2. Analysis and Modeling Tools and Techniques

    1. Front Matter
      Pages 1-1
    2. Analysis

    3. Modeling

      1. Upinder S. Bhalla, James M. Bower
        Pages 95-102
      2. Edward K. Blum, Peyvand M. Khademi, Kevin Chau, Patrick Leung, Xin Wang
        Pages 103-111
      3. Frank H. Eeckman, Frédéric E. Theunissen, John P. Miller
        Pages 121-126
  3. Sensory Systems

    1. Front Matter
      Pages 137-137
    2. Visual System

    3. Auditory System

    4. Other Sensory Systems

  4. Motor Systems

    1. Front Matter
      Pages 373-373
    2. Central Pattern Generators (CPG’s)

      1. Eve Marder, James M. Weimann, Thomas B. Kepler, L. F. Abbott
        Pages 377-390
    3. Cortex, Cerebellum and Spinal Cord

  5. Back Matter
    Pages 463-465

About this book

Introduction

In recent years there has been tremendous activity in computational neuroscience resulting from two parallel developments. On the one hand, our knowledge of real nervous systems has increased dramatically over the years; on the other, there is now enough computing power available to perform realistic simulations of actual neural circuits. This is leading to a revolution in quantitative neuroscience, which is attracting a growing number of scientists from non-biological disciplines. These scientists bring with them expertise in signal processing, information theory, and dynamical systems theory that has helped transform our ways of approaching neural systems. New developments in experimental techniques have enabled biologists to gather the data necessary to test these new theories. While we do not yet understand how the brain sees, hears or smells, we do have testable models of specific components of visual, auditory, and olfactory processing. Some of these models have been applied to help construct artificial vision and hearing systems. Similarly, our understanding of motor control has grown to the point where it has become a useful guide in the development of artificial robots. Many neuroscientists believe that we have only scratched the surface, and that a more complete understanding of biological information processing is likely to lead to technologies whose impact will propel another industrial revolution.
Neural Systems: Analysis and Modeling contains the collected papers of the 1991 Conference on Analysis and Modeling of Neural Systems (AMNS), and the papers presented at the satellite symposium on compartmental modeling, held July 23-26, 1992, in San Francisco, California. The papers included, present an update of the most recent developments in quantitative analysis and modeling techniques for the study of neural systems.

Keywords

Motor Sensor information theory integrated circuit metal-oxide-semiconductor transistor robot systems theory

Editors and affiliations

  • Frank H. Eeckman
    • 1
  1. 1.Lawrence Livermore National LaboratoryUSA

Bibliographic information