Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems

Name: Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems
Price: 29.01 USD
Availability: InStock
ISBN: 9780262541855

ISBN-10: 0262541858

ISBN-13: 9780262541855

Edition: 2005

Authors: Peter Dayan, Laurence F. Abbott, Tomaso A. Poggio, Terrence J. Sejnowski

List price: $55.00

This item qualifies for FREE shipping.

30 day, 100% satisfaction guarantee!

Sell

Get cash fast!

Buy new: $53.79

Marketplace

5 new & used from $29.01

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

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…

Book details

List price: $55.00
Copyright year: 2005
Publisher: MIT Press
Publication date: 8/12/2005
Binding: Paperback
Pages: 480
Size: 8.00" wide x 10.00" long x 0.85" tall
Weight: 2.134

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



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