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.

Product Details

ISBN-13:
9780262541855
Publisher:
MIT Press
Publication date:
09/01/2005
Series:
Computational Neuroscience Series
Edition description:
New Edition
Pages:
480
Sales rank:
546,508
Product dimensions:
8.00(w) x 10.00(h) x 1.00(d)
Age Range:
18 Years

Table of Contents

Prefacexiii
INeural Encoding and Decoding1
1Neural Encoding I: Firing Rates and Spike Statistics3
1.1Introduction3
1.2Spike Trains and Firing Rates8
1.3What Makes a Neuron Fire?17
1.4Spike-Train Statistics24
1.5The Neural Code34
1.6Chapter Summary39
1.7Appendices40
1.8Annotated Bibliography43
2Neural Encoding II: Reverse Correlation and Visual Receptive Fields45
2.1Introduction45
2.2Estimating Firing Rates45
2.3Introduction to the Early Visual System51
2.4Reverse-Correlation Methods: Simple Cells60
2.5Static Nonlinearities: Complex Cells74
2.6Receptive Fields in the Retina and LGN77
2.7Constructing V1 Receptive Fields79
2.8Chapter Summary81
2.9Appendices81
2.10Annotated Bibliography84
3Neural Decoding87
3.1Encoding and Decoding87
3.2Discrimination89
3.3Population Decoding97
3.4Spike-Train Decoding113
3.5Chapter Summary118
3.6Appendices119
3.7Annotated Bibliography122
4Information Theory123
4.1Entropy and Mutual Information123
4.2Information and Entropy Maximization130
4.3Entropy and Information for Spike Trains145
4.4Chapter Summary149
4.5Appendix150
4.6Annotated Bibliography150
IINeurons and Neural Circuits151
5Model Neurons I: Neuroelectronics153
5.1Introduction153
5.2Electrical Properties of Neurons153
5.3Single-Compartment Models161
5.4Integrate-and-Fire Models162
5.5Voltage-Dependent Conductances166
5.6The Hodgkin-Huxley Model173
5.7Modeling Channels175
5.8Synaptic Conductances178
5.9Synapses on Integrate-and-Fire Neurons188
5.10Chapter Summary191
5.11Appendices191
5.12Annotated Bibliography193
6Model Neurons II: Conductances and Morphology195
6.1Levels of Neuron Modeling195
6.2Conductance-Based Models195
6.3The Cable Equation203
6.4Multi-compartment Models217
6.5Chapter Summary224
6.6Appendices224
6.7Annotated Bibliography228
7Network Models229
7.1Introduction229
7.2Firing-Rate Models231
7.3Feedforward Networks241
7.4Recurrent Networks244
7.5Excitatory-Inhibitory Networks265
7.6Stochastic Networks273
7.7Chapter Summary276
7.8Appendix276
7.9Annotated Bibliography277
IIIAdaptation and Learning279
8Plasticity and Learning281
8.1Introduction281
8.2Synaptic Plasticity Rules284
8.3Unsupervised Learning293
8.4Supervised Learning313
8.5Chapter Summary326
8.6Appendix327
8.7Annotated Bibliography328
9Classical Conditioning and Reinforcement Learning331
9.1Introduction331
9.2Classical Conditioning332
9.3Static Action Choice340
9.4Sequential Action Choice346
9.5Chapter Summary354
9.6Appendix355
9.7Annotated Bibliography357
10Representational Learning359
10.1Introduction359
10.2Density Estimation368
10.3Causal Models for Density Estimation373
10.4Discussion389
10.5Chapter Summary394
10.6Appendix395
10.7Annotated Bibliography396
Mathematical Appendix399
A.1Linear Algebra399
A.2Finding Extrema and Lagrange Multipliers408
A.3Differential Equations410
A.4Electrical Circuits413
A.5Probability Theory415
A.6Annotated Bibliography418
References419
Index439
Exercises

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