Theoretical neuroscience : computational and mathematical modeling of neural systems

Başlık:

Yazar:

Dayan, Peter.

ISBN:

9780262041997

Ek Yazar:

Dayan, Peter.

Yayım Bilgisi:

Cambridge, Mass. : Massachusetts Institute of Technology Press, c2001.

Fiziksel Tanım:

xv, 460 p. : ill. ; 25 cm.

Series:

Computational neuroscience

Series Title:

Computational neuroscience

Contents:

Neural encoding I: firing rates and spike statistics -- Neural encoding II: reverse correlation and visual receptive fields -- Neural decoding -- Information theory -- Model neurons I: neuroelectronics -- Model neurons II: conductances and morphology -- Network models -- Plasticity and learning -- Classical conditioning and reinforcement learning -- Representational learning -- Mathematical appendix.

Corporate Subject:

Tıp Fakültesi Kütüphanesi

Konu Terimleri:

Neural networks (Neurobiology) -- Computer simulation.

Sinir ağları (Nörobiyoloji) -- Bilgisayar simülasyonu.

Human information processing -- Computer simulation.

İnsan bilgisi işleme -- Bilgisayar simülasyonu.

Computational neuroscience.

Hesaplanabilir nörolojik bilimler.

Neural Networks (Computer)

Computer Simulation.

Models, Neurological.

Neurosciences informatiques.

Râeseaux neuronaux (Neurobiologie) -- Simulation par ordinateur.

Information, Traitement de l', chez l'homme -- Simulation par ordinateur.

Added Author:

Abbott, L. F.

Electronic Access:

Table of contents http://www.loc.gov/catdir/toc/fy031/2001044005.html

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Library	Materyal Türü	Barkod	Yer Numarası	Durum
Searching... Pamukkale Tıp Fakültesi Kütüphanesi	Kitap	0024639	WL 26.5D275T 2001	Searching... Unknown

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Özet

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.

Author Notes

Peter Dayan is on the faculty of the Gatsby Computational Neuroscience Unit at University College London.

Preface	p. xiii
I Neural Encoding and Decoding	p. 1
1 Neural Encoding I: Firing Rates and Spike Statistics	p. 3
1.1 Introduction	p. 3
1.2 Spike Trains and Firing Rates	p. 8
1.3 What Makes a Neuron Fire?	p. 17
1.4 Spike-Train Statistics	p. 24
1.5 The Neural Code	p. 34
1.6 Chapter Summary	p. 39
1.7 Appendices	p. 40
1.8 Annotated Bibliography	p. 43
2 Neural Encoding II: Reverse Correlation and Visual Receptive Fields	p. 45
2.1 Introduction	p. 45
2.2 Estimating Firing Rates	p. 45
2.3 Introduction to the Early Visual System	p. 51
2.4 Reverse-Correlation Methods: Simple Cells	p. 60
2.5 Static Nonlinearities: Complex Cells	p. 74
2.6 Receptive Fields in the Retina and LGN	p. 77
2.7 Constructing V1 Receptive Fields	p. 79
2.8 Chapter Summary	p. 81
2.9 Appendices	p. 81
2.10 Annotated Bibliography	p. 84
3 Neural Decoding	p. 87
3.1 Encoding and Decoding	p. 87
3.2 Discrimination	p. 89
3.3 Population Decoding	p. 97
3.4 Spike-Train Decoding	p. 113
3.5 Chapter Summary	p. 118
3.6 Appendices	p. 119
3.7 Annotated Bibliography	p. 122
4 Information Theory	p. 123
4.1 Entropy and Mutual Information	p. 123
4.2 Information and Entropy Maximization	p. 130
4.3 Entropy and Information for Spike Trains	p. 145
4.4 Chapter Summary	p. 149
4.5 Appendix	p. 150
4.6 Annotated Bibliography	p. 150
II Neurons and Neural Circuits	p. 151
5 Model Neurons I: Neuroelectronics	p. 153
5.1 Introduction	p. 153
5.2 Electrical Properties of Neurons	p. 153
5.3 Single-Compartment Models	p. 161
5.4 Integrate-and-Fire Models	p. 162
5.5 Voltage-Dependent Conductances	p. 166
5.6 The Hodgkin-Huxley Model	p. 173
5.7 Modeling Channels	p. 175
5.8 Synaptic Conductances	p. 178
5.9 Synapses on Integrate-and-Fire Neurons	p. 188
5.10 Chapter Summary	p. 191
5.11 Appendices	p. 191
5.12 Annotated Bibliography	p. 193
6 Model Neurons II: Conductances and Morphology	p. 195
6.1 Levels of Neuron Modeling	p. 195
6.2 Conductance-Based Models	p. 195
6.3 The Cable Equation	p. 203
6.4 Multi-compartment Models	p. 217
6.5 Chapter Summary	p. 224
6.6 Appendices	p. 224
6.7 Annotated Bibliography	p. 228
7 Network Models	p. 229
7.1 Introduction	p. 229
7.2 Firing-Rate Models	p. 231
7.3 Feedforward Networks	p. 241
7.4 Recurrent Networks	p. 244
7.5 Excitatory-Inhibitory Networks	p. 265
7.6 Stochastic Networks	p. 273
7.7 Chapter Summary	p. 276
7.8 Appendix	p. 276
7.9 Annotated Bibliography	p. 277
III Adaptation and Learning	p. 279
8 Plasticity and Learning	p. 281
8.1 Introduction	p. 281
8.2 Synaptic Plasticity Rules	p. 284
8.3 Unsupervised Learning	p. 293
8.4 Supervised Learning	p. 313
8.5 Chapter Summary	p. 326
8.6 Appendix	p. 327
8.7 Annotated Bibliography	p. 328
9 Classical Conditioning and Reinforcement Learning	p. 331
9.1 Introduction	p. 331
9.2 Classical Conditioning	p. 332
9.3 Static Action Choice	p. 340
9.4 Sequential Action Choice	p. 346
9.5 Chapter Summary	p. 354
9.6 Appendix	p. 355
9.7 Annotated Bibliography	p. 357
10 Representational Learning	p. 359
10.1 Introduction	p. 359
10.2 Density Estimation	p. 368
10.3 Causal Models for Density Estimation	p. 373
10.4 Discussion	p. 389
10.5 Chapter Summary	p. 394
10.6 Appendix	p. 395
10.7 Annotated Bibliography	p. 396
Mathematical Appendix	p. 399
A.1 Linear Algebra	p. 399
A.2 Finding Extrema and Lagrange Multipliers	p. 408
A.3 Differential Equations	p. 410
A.4 Electrical Circuits	p. 413
A.5 Probability Theory	p. 415
A.6 Annotated Bibliography	p. 418
References	p. 419

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Author Notes

Table of Contents