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1. Biological learning and control : how the brain builds representations, predicts events, and makes decisions ((ebook) :)

Reza Shadmehr and Sandro Mussa-Ivaldi

出版情報:	Cambridge, Mass. : MIT Press, c2012 1 online resource (385 p.)
シリーズ名:	Computational neuroscience ;
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Series Foreword

Introduction

Space in the Mammalian Brain / 1：

Building a Space Map / 2：

The Space Inside / 3：

Sensorimotor Integration and State Estimation / 4：

Bayesian Estimation and Inference / 5：

Learning to Make Accurate Predictions / 6：

Learning Faster / 7：

The Multiple Timescales of Memory / 8：

Building Generative Models: Structural Learning and Identification of the Learner / 9：

Costs and Rewards of Motor Commands / 10：

Cost of Time in Motor Control / 11：

Optimal Feedback Control / 12：

Appendix

Notes

References

Index

Series Foreword

Introduction

Space in the Mammalian Brain / 1：

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2. Bayesian brain : probabilistic approaches to neural coding ((ebook) :)

Kenji Doya ... [et al.]

出版情報:	Cambridge, Mass. ; London : MIT, c2007 1 online resource (326 p.)
シリーズ名:	Computational neuroscience ;
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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：

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3. Language, music, and the brain : : a mysterious relationship ((ebook) :)

edited by Michael A. Arbib

出版情報:	1 online resource (xiii, 662 pages).
シリーズ名:	Strüngmann Forum reports ;
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4. The brain : a very short introduction ((ebook) :)

Michael O'Shea

出版情報:	Oxford : Oxford University Press, 2005 1 online resource (xii, 136 p.)
シリーズ名:	Very short introductions ;
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Acknowledgements

List of illustrations

Thinking about the brain / 1：

From humours to cells: components of mind / 2：

Signalling in the brain: getting connected / 3：

From the Big Bang to the big brain / 4：

Sensing, perceiving, and acting / 5：

Memories are made of this / 6：

Broken brain: invention and intervention / 7：

Epilogue / 8：

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