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Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems

ISBN-10: 0262041995

ISBN-13: 9780262041997

Edition: 2001

Authors: Peter Dayan, L. F. Abbott

List price: $55.00
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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.
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Book details

List price: $55.00
Copyright year: 2001
Publisher: MIT Press
Publication date: 12/1/2001
Binding: Hardcover
Pages: 476
Size: 8.00" wide x 10.00" long x 1.25" tall
Weight: 2.948
Language: English

Preface
Neural Encoding and Decoding
Neural Encoding I: Firing Rates and Spike Statistics
Introduction
Spike Trains and Firing Rates
What Makes a Neuron Fire?
Spike-Train Statistics
The Neural Code
Chapter Summary
Appendices
Annotated Bibliography
Neural Encoding II: Reverse Correlation and Visual Receptive Fields
Introduction
Estimating Firing Rates
Introduction to the Early Visual System
Reverse-Correlation Methods: Simple Cells
Static Nonlinearities: Complex Cells
Receptive Fields in the Retina and LGN
Constructing V1 Receptive Fields
Chapter Summary
Appendices
Annotated Bibliography
Neural Decoding
Encoding and Decoding
Discrimination
Population Decoding
Spike-Train Decoding
Chapter Summary
Appendices
Annotated Bibliography
Information Theory
Entropy and Mutual Information
Information and Entropy Maximization
Entropy and Information for Spike Trains
Chapter Summary
Appendix
Annotated Bibliography
Neurons and Neural Circuits
Model Neurons I: Neuroelectronics
Introduction
Electrical Properties of Neurons
Single-Compartment Models
Integrate-and-Fire Models
Voltage-Dependent Conductances
The Hodgkin-Huxley Model
Modeling Channels
Synaptic Conductances
Synapses on Integrate-and-Fire Neurons
Chapter Summary
Appendices
Annotated Bibliography
Model Neurons II: Conductances and Morphology
Levels of Neuron Modeling
Conductance-Based Models
The Cable Equation
Multi-compartment Models
Chapter Summary
Appendices
Annotated Bibliography
Network Models
Introduction
Firing-Rate Models
Feedforward Networks
Recurrent Networks
Excitatory-Inhibitory Networks
Stochastic Networks
Chapter Summary
Appendix
Annotated Bibliography
Adaptation and Learning
Plasticity and Learning
Introduction
Synaptic Plasticity Rules
Unsupervised Learning
Supervised Learning
Chapter Summary
Appendix
Annotated Bibliography
Classical Conditioning and Reinforcement Learning
Introduction
Classical Conditioning
Static Action Choice
Sequential Action Choice
Chapter Summary
Appendix
Annotated Bibliography
Representational Learning
Introduction
Density Estimation
Causal Models for Density Estimation
Discussion
Chapter Summary
Appendix
Annotated Bibliography
Mathematical Appendix
Linear Algebra
Finding Extrema and Lagrange Multipliers
Differential Equations
Electrical Circuits
Probability Theory
Annotated Bibliography
References