Theoretical Neuroscience

Computational and Mathematical Modeling of Neural Systems
Overview

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.

The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

Table of Contents

  1. Series Foreword
  2. Preface
  3. I. Neural Encoding and Decoding
  4. 1. Neural Encoding I: Firing Rates and Spike Statistics
  5. 2. Neural Encoding II: Reverse Correlation and Visual Receptive Fields
  6. 3. Neural Decoding
  7. 4. Information Theory
  8. II. Neurons and Neural Circuits
  9. 5. Model Neurons I: Neuroelectronics
  10. 6. Model Neurons II: Conductances and Morphology
  11. 7. Network Models
  12. III. Adaptation and Learning
  13. 8. Plasticity and Learning
  14. 9. Classical Conditioning and Reinforcement Learning
  15. 10. Representational Learning
  16. Mathematical Appendix
  17. References
  18. Index