Bayesian brain : probabilistic approaches to neural coding

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Bayesian brain : probabilistic approaches to neural coding / Kenji Doya ... [et al.]

資料種別:

図書

出版情報:

Cambridge, Mass. : MIT Press, c2007

形態:

xiii, 326 p., [4] leaves of plates ; 24 cm

シリーズ名:

Computational neuroscience <BA07230443>

著者名:

ISBN:

9780262042383 [026204238X]

書誌ID:

BA80142746

子書誌情報

フルテキスト

所蔵情報

他の版・巻

書誌詳細

注記:

Other editors: Shin Ishii, Alexandre Pouget and Rajesh P.N. Rao
Includes bibliographical references and index

著者名ヨミ:

ドウヤ, ケンジ
イシイ, シン

主題:

Bayesian statistical decision theory; Brain; Neurons

言語:

英語

目次情報:

Series Foreword

Preface

Introduction / I：

A Probability Primer / Kenji Doya ; Shin Ishii1：

What Is Probability? / 1.1：

Bayes Theorem / 1.2：

Measuring Information / 1.3：

Making an Inference / 1.4：

Learning from Data / 1.5：

Graphical Models and Other Bayesian Algorithms / 1.6：

Reading Neural Codes / II：

Spike Coding / Adrienne Fairhall2：

Spikes: What Kind of Code? / 2.1：

Encoding and Decoding / 2.2：

Adaptive Spike Coding / 2.3：

Summary / 2.4：

Recommended Reading / 2.5：

Likelihood-Based Approaches to Modeling the Neural Code / Jonathan Pillow3：

The Neural Coding Problem / 3.1：

Model Fitting with Maximum Likelihood / 3.2：

Model Validation / 3.3：

Combining Order Statistics with Bayes Theorem for Millisecond-by-Millisecond Decoding of Spike Trains / Barry J. Richmond ; Matthew C. Wiener3.4：

An Approach to Decoding / 4.1：

Simplifying the Order Statistic Model / 4.3：

Discussion / 4.4：

Bayesian Treatments of Neuroimaging Data / Will Penny ; Karl Friston5：

Attention to Visual Motion / 5.1：

The General Linear Model / 5.3：

Parameter Estimation / 5.4：

Posterior Probability Mapping / 5.5：

Dynamic Causal Modeling / 5.6：

Making Sense of the World / 5.7：

Population Codes / Alexandre Pouget ; Richard S. Zemel6：

Coding and Decoding / 6.1：

Representing Uncertainty with Population Codes / 6.3：

Conclusion / 6.4：

Computing with Population Codes / Peter Latham7：

Computing, Invariance, and Throwing Away Information / 7.1：

Computing Functions with Networks of Neurons: A General Algorithm / 7.2：

Efficient Computing; Qualitative Analysis / 7.3：

Efficient Computing; Quantitative Analysis / 7.4：

Efficient Coding of Visual Scenes by Grouping and Segmentation / Tai Sing Lee ; Alan L. Yuille7.5：

Computational Theories for ScÃ¨ne Segmentation / 8.1：

A Computational Algorithm for the Weak-Membrane Model / 8.3：

Generalizations of the Weak-Membrane Model / 8.4：

Biological Evidence / 8.5：

Summary and Discussion / 8.6：

Bayesian Models of Sensory Cue Integration / David C. Knul9：

Psychophysical Tests of Bayesian Cue Integration / 9.1：

Psychophysical Tests of Bayesian Priors / 9.3：

Mixture models. Priors, and Cue Integration / 9.4：

Making Decisions and Movements / 9.5：

The Speed and Accuracy of a Simple Perceptual Decision: A Mathematical Primer / Michael N. Shadlen ; Timothy D. Hanks ; Anne K. Churchland ; Roozbeh Kiani ; Tianming Yang10：

The Diffusion-to-Bound Framework / 10.1：

Derivation of Choice and Reaction Time Functions / 10.3：

Implementation of Diffusion-to-Bound Framework in the Brain / 10.4：

Conclusions / 10.5：

Neural Models of Bayesian Belief Propagation / Rajesh P.N. Rao11：

Bayesian Inference through Belief Propagation / 11.1：

Neural Implementations of Belief Propagation / 11.3：

Results / 11.4：

Optimal Control Theory / Emanuel Todorov11.5：

Discrete Control: Bellman Equations / 12.1：

Continuous Control: Hamilton-Jacobi-Bellman Equations / 12.2：

Deterministic Control: Pontryagin's Maximum Principle / 12.3：

Linear-Quadratic-Gaussian Control: Riccati Equations / 12.4：

Optimal Estimation: Kalman Filter / 12.5：

Duality of Optimal Control and Optimal Estimation / 12.6：

Optimal Control as a Theory of Biological Movement / 12.7：

Bayesian Statistics and Utility Functions in Sensorimotor Control / Konrad P. Kording ; Daniel M. Wolpert13：

Motor Decisions / 13.1：

Utility: The Cost of Using our Muscles / 13.3：

Neurobiology / 13.4：

Contributors / 13.5：

Index

Series Foreword

Preface

Introduction / I：

A Probability Primer / Kenji Doya ; Shin Ishii1：

What Is Probability? / 1.1：

Bayes Theorem / 1.2：

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