An Introduction to Natural Computation

Overview

It is now clear that the brain is unlikely to be understood without recourse to computational theories. The theme of An Introduction to Natural Computation is that ideas from diverse areas such as neuroscience, information theory, and optimization theory have recently been extended in ways that make them useful for describing the brains programs. This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models. It stresses the broad spectrum of learning models—ranging from neural network learning through reinforcement learning to genetic learning—and situates the various models in their appropriate neural context.

To write about models of the brain before the brain is fully understood is a delicate matter. Very detailed models of the neural circuitry risk losing track of the task the brain is trying to solve. At the other extreme, models that represent cognitive constructs can be so abstract that they lose all relationship to neurobiology. An Introduction to Natural Computation takes the middle ground and stresses the computational task while staying near the neurobiology.

Table of Contents

  1. Figures
  2. Tables
  3. Preface
  4. Natural Computation
  5. I. Core Concepts
  6. Fitness
  7. Programs
  8. Data
  9. Dynamics
  10. Optimization
  11. II. Memories
  12. Content-Addressable Memory
  13. Supervised Learning
  14. Unsupervised Learning
  15. III. Programs
  16. Markov Models
  17. Reinforcement Learning
  18. IV. Systems
  19. Genetic Algorithms
  20. Genetic Programming
  21. Summary
  22. Index