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Theoretical Neuroscience : Computational and Mathematical Modeling of Neural Systems - Peter Dayan

Theoretical Neuroscience

Computational and Mathematical Modeling of Neural Systems

Paperback Published: 1st September 2005
ISBN: 9780262541855
Number Of Pages: 480
For Ages: 18+ years old

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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.

Industry Reviews

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

-Dmitri Chklovskii, Neuron
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, Philosophical Psychology * Reviews *
Not only does the book set a high standard for theoretical neuroscience, it defines the field.

-Dmitri Chklovskii, Neuron * Reviews *

Prefacep. xiii
Neural Encoding and Decodingp. 1
Neural Encoding I: Firing Rates and Spike Statisticsp. 3
Introductionp. 3
Spike Trains and Firing Ratesp. 8
What Makes a Neuron Fire?p. 17
Spike-Train Statisticsp. 24
The Neural Codep. 34
Chapter Summaryp. 39
Appendicesp. 40
Annotated Bibliographyp. 43
Neural Encoding II: Reverse Correlation and Visual Receptive Fieldsp. 45
Introductionp. 45
Estimating Firing Ratesp. 45
Introduction to the Early Visual Systemp. 51
Reverse-Correlation Methods: Simple Cellsp. 60
Static Nonlinearities: Complex Cellsp. 74
Receptive Fields in the Retina and LGNp. 77
Constructing V1 Receptive Fieldsp. 79
Chapter Summaryp. 81
Appendicesp. 81
Annotated Bibliographyp. 84
Neural Decodingp. 87
Encoding and Decodingp. 87
Discriminationp. 89
Population Decodingp. 97
Spike-Train Decodingp. 113
Chapter Summaryp. 118
Appendicesp. 119
Annotated Bibliographyp. 122
Information Theoryp. 123
Entropy and Mutual Informationp. 123
Information and Entropy Maximizationp. 130
Entropy and Information for Spike Trainsp. 145
Chapter Summaryp. 149
Appendixp. 150
Annotated Bibliographyp. 150
Neurons and Neural Circuitsp. 151
Model Neurons I: Neuroelectronicsp. 153
Introductionp. 153
Electrical Properties of Neuronsp. 153
Single-Compartment Modelsp. 161
Integrate-and-Fire Modelsp. 162
Voltage-Dependent Conductancesp. 166
The Hodgkin-Huxley Modelp. 173
Modeling Channelsp. 175
Synaptic Conductancesp. 178
Synapses on Integrate-and-Fire Neuronsp. 188
Chapter Summaryp. 191
Appendicesp. 191
Annotated Bibliographyp. 193
Model Neurons II: Conductances and Morphologyp. 195
Levels of Neuron Modelingp. 195
Conductance-Based Modelsp. 195
The Cable Equationp. 203
Multi-compartment Modelsp. 217
Chapter Summaryp. 224
Appendicesp. 224
Annotated Bibliographyp. 228
Network Modelsp. 229
Introductionp. 229
Firing-Rate Modelsp. 231
Feedforward Networksp. 241
Recurrent Networksp. 244
Excitatory-Inhibitory Networksp. 265
Stochastic Networksp. 273
Chapter Summaryp. 276
Appendixp. 276
Annotated Bibliographyp. 277
Adaptation and Learningp. 279
Plasticity and Learningp. 281
Introductionp. 281
Synaptic Plasticity Rulesp. 284
Unsupervised Learningp. 293
Supervised Learningp. 313
Chapter Summaryp. 326
Appendixp. 327
Annotated Bibliographyp. 328
Classical Conditioning and Reinforcement Learningp. 331
Introductionp. 331
Classical Conditioningp. 332
Static Action Choicep. 340
Sequential Action Choicep. 346
Chapter Summaryp. 354
Appendixp. 355
Annotated Bibliographyp. 357
Representational Learningp. 359
Introductionp. 359
Density Estimationp. 368
Causal Models for Density Estimationp. 373
Discussionp. 389
Chapter Summaryp. 394
Appendixp. 395
Annotated Bibliographyp. 396
Mathematical Appendixp. 399
Linear Algebrap. 399
Finding Extrema and Lagrange Multipliersp. 408
Differential Equationsp. 410
Electrical Circuitsp. 413
Probability Theoryp. 415
Annotated Bibliographyp. 418
Referencesp. 419
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9780262541855
ISBN-10: 0262541858
Series: Computational Neuroscience Series
Audience: Professional
For Ages: 18+ years old
Format: Paperback
Language: English
Number Of Pages: 480
Published: 1st September 2005
Publisher: MIT Press Ltd
Country of Publication: US
Dimensions (cm): 25.3 x 20.3  x 2.2
Weight (kg): 0.94