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 Scène 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: |