Bayesian brain: probabilistic approaches to neural coding
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Cambridge, Mass. [u.a.]
MIT Press
2007
|
| Series: | Computational neuroscience
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturangaben |
| Physical Description: | XIII, 326 S. graph. Darst. |
| ISBN: | 026204238X 9780262042383 |
Staff View
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Record in the Search Index
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| adam_text | Contents
Series Foreword ix
Preface xi
I Introduction 1
1 A Probability Primer
Kenji Doya and Shin Ishii 3
1.1 What Is Probability? 3
1.2 Bayes Theorem 6
1.3 Measuring Information 6
1.4 Making an Inference 8
1.5 Learning from Data 10
1.6 Graphical Models and Other Bayesian Algorithms 13
II Reading Neural Codes 15
2 Spike Coding
Adrienne Fairhall 17
2.1 Spikes: What Kind of Code? 17
2.2 Encoding and Decoding 25
2.3 Adaptive Spike Coding 42
2.4 Summary 47
2.5 Recommended Reading 47
3 Likelihood-Based Approaches to Modeling the Neural Code
Jonathan Pillow 53
3.1 The Neural Coding Problem 53
3.2 Model Fitting with Maximum Likelihood 55
3.3 Model Validation 64
3.4 Summary 68
vi Contents
4 Combining Order Statistics with Bayes Theorem for
Millisecond-by-Millisecond Decoding of Spike Trains
Barry ]. Richmond and Matthew C. Wiener 71
4.1 Introduction 71
4.2 An Approach to Decoding 72
4.3 Simplifying the Order Statistic Model 82
4.4 Discussion 84
5 Bayesian Treatments of Neuroimaging Data
Will Penny and Karl Friston 93
5.1 Introduction 93
5.2 Attention to Visual Motion 94
5.3 The General Linear Model 95
5.4 Parameter Estimation 98
5.5 Posterior Probability Mapping 101
5.6 Dynamic Causal Modeling 104
5.7 Discussion 109
III Making Sense of the World 113
6 Population Codes
Alexandre Pouget and Richard S. Zemel 115
6.1 Introduction 115
6.2 Coding and Decoding 116
6.3 Representing Uncertainty with Population Codes 120
6.4 Conclusion 127
7 Computing with Population Codes
Peter Latham and Alexandre Pouget 131
7.1 Computing, Invariance, and Throwing Away Information 131
7.2 Computing Functions with Networks of Neurons: A General
Algorithm 132
7.3 Efficient Computing; Qualitative Analysis 136
7.4 Efficient Computing; Quantitative Analysis 137
7.5 Summary 142
8 Efficient Coding of Visual Scenes by Grouping and Segmentation
Tai Sing Lee and Alan L. Yuille 145
8.1 Introduction 145
8.2 Computational Theories for Scene Segmentation 148
8.3 A Computational Algorithm for the Weak-Membrane
Model 152
8.4 Generalizations of the Weak-Membrane Model 156
8.5 Biological Evidence 161
8.6 Summary and Discussion 180
Contents vii
9 Bayesian Models of Sensory Cue Integration
David C. Knill 189
9.1 Introduction 189
9.2 Psychophysical Tests of Bayesian Cue Integration 191
9.3 Psychophysical Tests of Bayesian Priors 195
9.4 Mixture models, Priors, and Cue Integration 199
9.5 Conclusion 204
IV Making Decisions and Movements 207
10 The Speed and Accuracy of a Simple Perceptual Decision: A Mathematical
Primer
Michael N. Shadlen, Timothy D. Hanks, Anne K. Churchland,
Roozbeh Kiani, and Tianming Yang 209
10.1 Introduction 209
10.2 The Diffusion-to-Bound Framework 210
10.3 Derivation of Choice and Reaction Time Functions 213
10.4 Implementation of Diffusion-to-Bound Framework in the
Brain 226
10.5 Conclusions 233
11 Neural Models of Bayesian Belief Propagation
Rajesh P. N. Rao 239
11.1 Introduction 239
11.2 Bayesian Inference through Belief Propagation 240
11.3 Neural Implementations of Belief Propagation 244
11.4 Results 248
11.5 Discussion 258
12 Optimal Control Theory
Emanuel Todorov 269
12.1 Discrete Control: Bellman Equations 270
12.2 Continuous Control: Hamilton-Jacobi-Bellman Equations 273
12.3 Deterministic Control: Pontryagin s Maximum Principle 277
12.4 Linear-Quadratic-Gaussian Control: Riccati Equations 283
12.5 Optimal Estimation: Kalman Filter 287
12.6 Duality of Optimal Control and Optimal Estimation 290
12.7 Optimal Control as a Theory of Biological Movement 294
viii Contents
13 Bayesian Statistics and Utility Functions in Sensorimotor Control
Konrad P. Kording and Daniel M. Wolpert 299
13.1 Introduction 299
13.2 Motor Decisions 301
13.3 Utility: The Cost of Using our Muscles 308
13.4 Neurobiology 314
13.5 Discussion 316
Contributors 321
Index 324
|
| adam_txt |
Contents
Series Foreword ix
Preface xi
I Introduction 1
1 A Probability Primer
Kenji Doya and Shin Ishii 3
1.1 What Is Probability? 3
1.2 Bayes Theorem 6
1.3 Measuring Information 6
1.4 Making an Inference 8
1.5 Learning from Data 10
1.6 Graphical Models and Other Bayesian Algorithms 13
II Reading Neural Codes 15
2 Spike Coding
Adrienne Fairhall 17
2.1 Spikes: What Kind of Code? 17
2.2 Encoding and Decoding 25
2.3 Adaptive Spike Coding 42
2.4 Summary 47
2.5 Recommended Reading 47
3 Likelihood-Based Approaches to Modeling the Neural Code
Jonathan Pillow 53
3.1 The Neural Coding Problem 53
3.2 Model Fitting with Maximum Likelihood 55
3.3 Model Validation 64
3.4 Summary 68
vi Contents
4 Combining Order Statistics with Bayes Theorem for
Millisecond-by-Millisecond Decoding of Spike Trains
Barry ]. Richmond and Matthew C. Wiener 71
4.1 Introduction 71
4.2 An Approach to Decoding 72
4.3 Simplifying the Order Statistic Model 82
4.4 Discussion 84
5 Bayesian Treatments of Neuroimaging Data
Will Penny and Karl Friston 93
5.1 Introduction 93
5.2 Attention to Visual Motion 94
5.3 The General Linear Model 95
5.4 Parameter Estimation 98
5.5 Posterior Probability Mapping 101
5.6 Dynamic Causal Modeling 104
5.7 Discussion 109
III Making Sense of the World 113
6 Population Codes
Alexandre Pouget and Richard S. Zemel 115
6.1 Introduction 115
6.2 Coding and Decoding 116
6.3 Representing Uncertainty with Population Codes 120
6.4 Conclusion 127
7 Computing with Population Codes
Peter Latham and Alexandre Pouget 131
7.1 Computing, Invariance, and Throwing Away Information 131
7.2 Computing Functions with Networks of Neurons: A General
Algorithm 132
7.3 Efficient Computing; Qualitative Analysis 136
7.4 Efficient Computing; Quantitative Analysis 137
7.5 Summary 142
8 Efficient Coding of Visual Scenes by Grouping and Segmentation
Tai Sing Lee and Alan L. Yuille 145
8.1 Introduction 145
8.2 Computational Theories for Scene Segmentation 148
8.3 A Computational Algorithm for the Weak-Membrane
Model 152
8.4 Generalizations of the Weak-Membrane Model 156
8.5 Biological Evidence 161
8.6 Summary and Discussion 180
Contents vii
9 Bayesian Models of Sensory Cue Integration
David C. Knill 189
9.1 Introduction 189
9.2 Psychophysical Tests of Bayesian Cue Integration 191
9.3 Psychophysical Tests of Bayesian Priors 195
9.4 Mixture models, Priors, and Cue Integration 199
9.5 Conclusion 204
IV Making Decisions and Movements 207
10 The Speed and Accuracy of a Simple Perceptual Decision: A Mathematical
Primer
Michael N. Shadlen, Timothy D. Hanks, Anne K. Churchland,
Roozbeh Kiani, and Tianming Yang 209
10.1 Introduction 209
10.2 The Diffusion-to-Bound Framework 210
10.3 Derivation of Choice and Reaction Time Functions 213
10.4 Implementation of Diffusion-to-Bound Framework in the
Brain 226
10.5 Conclusions 233
11 Neural Models of Bayesian Belief Propagation
Rajesh P. N. Rao 239
11.1 Introduction 239
11.2 Bayesian Inference through Belief Propagation 240
11.3 Neural Implementations of Belief Propagation 244
11.4 Results 248
11.5 Discussion 258
12 Optimal Control Theory
Emanuel Todorov 269
12.1 Discrete Control: Bellman Equations 270
12.2 Continuous Control: Hamilton-Jacobi-Bellman Equations 273
12.3 Deterministic Control: Pontryagin's Maximum Principle 277
12.4 Linear-Quadratic-Gaussian Control: Riccati Equations 283
12.5 Optimal Estimation: Kalman Filter 287
12.6 Duality of Optimal Control and Optimal Estimation 290
12.7 Optimal Control as a Theory of Biological Movement 294
viii Contents
13 Bayesian Statistics and Utility Functions in Sensorimotor Control
Konrad P. Kording and Daniel M. Wolpert 299
13.1 Introduction 299
13.2 Motor Decisions 301
13.3 Utility: The Cost of Using our Muscles 308
13.4 Neurobiology 314
13.5 Discussion 316
Contributors 321
Index 324 |
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| spelling | Bayesian brain probabilistic approaches to neural coding Kenji Doya ... Cambridge, Mass. [u.a.] MIT Press 2007 XIII, 326 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Computational neuroscience Literaturangaben Brain Neurons Bayesian statistical decision theory Neuropsychologie (DE-588)4135740-1 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Neuropsychologie (DE-588)4135740-1 s Bayes-Verfahren (DE-588)4204326-8 s b DE-604 Doya, Kenji Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016666542&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Bayesian brain probabilistic approaches to neural coding |
| title_auth | Bayesian brain probabilistic approaches to neural coding |
| title_exact_search | Bayesian brain probabilistic approaches to neural coding |
| title_exact_search_txtP | Bayesian brain probabilistic approaches to neural coding |
| title_full | Bayesian brain probabilistic approaches to neural coding Kenji Doya ... |
| title_fullStr | Bayesian brain probabilistic approaches to neural coding Kenji Doya ... |
| title_full_unstemmed | Bayesian brain probabilistic approaches to neural coding Kenji Doya ... |
| title_short | Bayesian brain |
| title_sort | bayesian brain probabilistic approaches to neural coding |
| title_sub | probabilistic approaches to neural coding |
| topic | Brain Neurons Bayesian statistical decision theory Neuropsychologie (DE-588)4135740-1 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| topic_facet | Brain Neurons Bayesian statistical decision theory Neuropsychologie Bayes-Verfahren Aufsatzsammlung |
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