Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Paperback)

Description:

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.

Peter Dayan is on the faculty of the Gatsby Computational Neuroscience Unit at University College London.

L. F. Abbott is the Nancy Lurie Marks Professor of Neuroscience and Director of the Volen Center for Complex Systems at Brandeis University. He is the coeditor of Neural Codes and Distributed Representations (MIT Press, 1999).

 

Table of Contents:

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