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Series Foreword | |
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Preface | |
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Introduction | |
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A Probability Primer | |
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What Is Probability? | |
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Bayes Theorem | |
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Measuring Information | |
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Making an Inference | |
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Learning from Data | |
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Graphical Models and Other Bayesian Algorithms | |
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Reading Neural Codes | |
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Spike Coding | |
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Spikes: What Kind of Code? | |
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Encoding and Decoding | |
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Adaptive Spike Coding | |
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Summary | |
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Recommended Reading | |
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Likelihood-Based Approaches to Modeling the Neural Code | |
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The Neural Coding Problem | |
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Model Fitting with Maximum Likelihood | |
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Model Validation | |
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Summary | |
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Combining Order Statistics with Bayes Theorem for Millisecond-by-Millisecond Decoding of Spike Trains | |
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Introduction | |
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An Approach to Decoding | |
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Simplifying the Order Statistic Model | |
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Discussion | |
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Bayesian Treatments of Neuroimaging Data | |
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Introduction | |
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Attention to Visual Motion | |
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The General Linear Model | |
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Parameter Estimation | |
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Posterior Probability Mapping | |
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Dynamic Causal Modeling | |
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Discussion | |
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Making Sense of the World | |
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Population Codes | |
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Introduction | |
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Coding and Decoding | |
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Representing Uncertainty with Population Codes | |
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Conclusion | |
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Computing with Population Codes | |
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Computing, Invariance, and Throwing Away Information | |
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Computing Functions with Networks of Neurons: A General Algorithm | |
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Efficient Computing; Qualitative Analysis | |
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Efficient Computing; Quantitative Analysis | |
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Summary | |
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Efficient Coding of Visual Scenes by Grouping and Segmentation | |
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Introduction | |
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Computational Theories for Sc�ne Segmentation | |
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A Computational Algorithm for the Weak-Membrane Model | |
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Generalizations of the Weak-Membrane Model | |
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Biological Evidence | |
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Summary and Discussion | |
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Bayesian Models of Sensory Cue Integration | |
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Introduction | |
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Psychophysical Tests of Bayesian Cue Integration | |
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Psychophysical Tests of Bayesian Priors | |
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Mixture models. Priors, and Cue Integration | |
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Conclusion | |
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Making Decisions and Movements | |
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The Speed and Accuracy of a Simple Perceptual Decision: A Mathematical Primer | |
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Introduction | |
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The Diffusion-to-Bound Framework | |
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Derivation of Choice and Reaction Time Functions | |
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Implementation of Diffusion-to-Bound Framework in the Brain | |
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Conclusions | |
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Neural Models of Bayesian Belief Propagation | |
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Introduction | |
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Bayesian Inference through Belief Propagation | |
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Neural Implementations of Belief Propagation | |
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Results | |
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Discussion | |
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Optimal Control Theory | |
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Discrete Control: Bellman Equations | |
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Continuous Control: Hamilton-Jacobi-Bellman Equations | |
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Deterministic Control: Pontryagin's Maximum Principle | |
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Linear-Quadratic-Gaussian Control: Riccati Equations | |
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Optimal Estimation: Kalman Filter | |
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Duality of Optimal Control and Optimal Estimation | |
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Optimal Control as a Theory of Biological Movement | |
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Bayesian Statistics and Utility Functions in Sensorimotor Control | |
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Introduction | |
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Motor Decisions | |
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Utility: The Cost of Using our Muscles | |
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Neurobiology | |
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Discussion | |
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Contributors | |
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Index | |