Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems / Edition 1

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems / Edition 1

by Peter Dayan, Laurence F. Abbott
     
 

ISBN-10: 0262541858

ISBN-13: 9780262541855

Pub. Date: 09/01/2005

Publisher: MIT Press

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

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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.

The MIT Press

Product Details

ISBN-13:
9780262541855
Publisher:
MIT Press
Publication date:
09/01/2005
Series:
Computational Neuroscience
Edition description:
New Edition
Pages:
480
Sales rank:
474,705
Product dimensions:
8.00(w) x 10.00(h) x 1.00(d)
Age Range:
18 Years

Table of Contents

1Neural encoding I : firing rates and spike statistics3
2Neural encoding II : reverse correlation and visual receptive fields45
3Neural decoding87
4Information theory123
5Model neurons I : neuroelectronics153
6Model neurons II : conductances and morphology195
7Network models229
8Plasticity and learning281
9Classical conditioning and reinforcement learning331
10Representational learning359
App. 1Linear algebra399
App. 2Finding extrema and Lagrange multipliers408
App. 3Differential equations410
App. 4Electrical circuits413
App. 5Probability theory415

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