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

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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

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Editorial Reviews

From the Publisher
"It will not be surprising if this book becomes the standard text for students and researchers entering theoretical neuroscience for years to come." M. Brandon Westover PhilosophicalPsychology

"Not only does the book set a high standard for theoretical neuroscience, it defines the field." Dmitri Chklovskii Neuron

Philosophical Psychology - M. Brandon Westover

It will not be surprising if this book becomes the standard text for students and researchers entering theoretical neuroscience for years to come.

Neuron - Dmitri Chklovskii

Not only does the book set a high standard for theoretical neuroscience, it defines the field.

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Product Details

  • ISBN-13: 9780262541855
  • Publisher: MIT Press
  • Publication date: 9/1/2005
  • Series: Computational Neuroscience
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 480
  • Sales rank: 288,724
  • Product dimensions: 8.00 (w) x 10.00 (h) x 1.00 (d)

Meet the Author

Peter Dayan is Professor and Director of the Gatsby Computational Neuroscience Unit at
University College London.

Larry Abbott is Professor of Neuroscience and Co-Director of the Center for Theoretical
Neuroscience at Columbia University.

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Table of Contents

1 Neural encoding I : firing rates and spike statistics 3
2 Neural encoding II : reverse correlation and visual receptive fields 45
3 Neural decoding 87
4 Information theory 123
5 Model neurons I : neuroelectronics 153
6 Model neurons II : conductances and morphology 195
7 Network models 229
8 Plasticity and learning 281
9 Classical conditioning and reinforcement learning 331
10 Representational learning 359
App. 1 Linear algebra 399
App. 2 Finding extrema and Lagrange multipliers 408
App. 3 Differential equations 410
App. 4 Electrical circuits 413
App. 5 Probability theory 415
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