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Computational Modeling of Neural Activities for Statistical Inference 1st ed. 2016 [Kõva köide]

  • Formaat: Hardback, 127 pages, kõrgus x laius: 235x155 mm, kaal: 3554 g, 20 Illustrations, color; 22 Illustrations, black and white; XXIV, 127 p. 42 illus., 20 illus. in color., 1 Hardback
  • Ilmumisaeg: 23-May-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319322842
  • ISBN-13: 9783319322841
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Computational Modeling of Neural Activities for Statistical Inference 1st ed. 2016Suurem pilt
  • Formaat: Hardback, 127 pages, kõrgus x laius: 235x155 mm, kaal: 3554 g, 20 Illustrations, color; 22 Illustrations, black and white; XXIV, 127 p. 42 illus., 20 illus. in color., 1 Hardback
  • Ilmumisaeg: 23-May-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319322842
  • ISBN-13: 9783319322841
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319322841.html
Märksõnad:
This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field.

Basic Principles of ERP Research, Surprise, and Probability Estimation.- Introduction to Model Estimation and Selection Methods.- A New Theory of Trial-by-Trial P300 Amplitude Fluctuations.- Bayesian Inference and the Urn-Ball Task.- Summary and Outlook.
1 Basic Principles of ERP Research, Surprise, and Probability Estimation
1(14)
1.1 Data Acquisition and Initial Analysis
1(5)
1.2 Signal-to--Noise Ratio Estimation for Event-Related Potentials
6(2)
1.3 Circularity in Data Analyses
8(1)
1.4 Probabilities and Surprise
9(3)
1.4.1 Bayesian Updating
10(1)
1.4.2 Predictive Surprise
11(1)
1.5 Probability Weighting Functions
12(3)
2 Introduction to Model Estimation and Selection Methods
15(26)
2.1 An Example Study
15(1)
2.2 Classical Single-Level Models
16(3)
2.2.1 The Null and Informative Hypotheses
16(1)
2.2.2 The General Linear Model
17(2)
2.3 Hierarchical Multiple-Level Models
19(3)
2.3.1 The First Level
19(1)
2.3.2 The Second Level
20(1)
2.3.3 The Third Level
20(2)
2.4 Model Estimation and Selection
22(7)
2.4.1 Collapsing and Augmenting the Hierarchical Model
23(2)
2.4.2 Model Parameter Optimization and Likelihood Calculation
25(2)
2.4.3 Model Selection Using Bayes Factors and Posterior Model Probabilities
27(1)
2.4.4 Group Studies
28(1)
2.5 A Transfer Example Experiment---Setup
29(6)
2.5.1 Signal-to-Noise Ratio Simulation
31(1)
2.5.2 Synthetic Data and Experimental Conditions
32(2)
2.5.3 The Model Space
34(1)
2.6 A Transfer Example Experiment---Results
35(5)
2.6.1 A Single Subject
36(2)
2.6.2 Multiple Subjects
38(2)
2.7 Evaluation Summary
40(1)
3 A New Theory of Trial-by-Trial P300 Amplitude Fluctuations
41(30)
3.1 Overview
41(3)
3.2 Participants, Experimental Design, Data Acquisition, and Data Analysis
44(1)
3.3 State-of-the-Art Observer Models and Surprise
45(5)
3.3.1 Approach by Squires et al. (SQU)
45(2)
3.3.2 Approach by Mars et al. (MAR)
47(1)
3.3.3 Approach by Ostwald et al. (OST)
48(1)
3.3.4 Surprise Based on the SQU, MAR, and OST Models
49(1)
3.4 The Digital Filtering Model (DIF)
50(6)
3.4.1 Short-Term Memory
51(1)
3.4.2 Long-Term Memory
52(2)
3.4.3 Alternation Expectation
54(1)
3.4.4 Explanatory Notes
54(1)
3.4.5 Surprise Based on the DIF Model
55(1)
3.4.6 DIF Model Parameter Training
55(1)
3.5 Specification of the Design Matrices for Model Estimation and Selection in the Oddball Task
56(2)
3.6 Results
58(8)
3.6.1 Conventional ERP Analyses
58(3)
3.6.2 Model-Based Trial-by-Trial Analyses
61(5)
3.7 Summary and Discussion
66(5)
4 Bayesian Inference and the Urn-Ball Task
71(40)
4.1 Overview
71(2)
4.2 Participants, Experimental Design, Data Acquisition, and Data Analysis
73(4)
4.3 The Bayesian Observer Model
77(6)
4.3.1 Bayes' Theorem and the Urn-Ball Task
77(2)
4.3.2 The Belief Distribution (BEL)
79(1)
4.3.3 The Prediction Distribution (PRE)
80(1)
4.3.4 Surprise Based on the Bayesian Observer Model
80(1)
4.3.5 Summary and Visualization of Bayesian Inference
81(2)
4.4 Incorporating Probability Weighting Functions into the Bayesian Observer Model
83(4)
4.4.1 Probability Weighting of the Inference Input (BELSI and PRESI)
83(1)
4.4.2 Probability Weighting of the Inference Output (BELSO and PRESO)
84(1)
4.4.3 Weighting Parameter Optimization
85(2)
4.5 The DIF Model in the Urn-Ball Task
87(2)
4.5.1 The Objective Initial Prior (DIFOP)
88(1)
4.5.2 The Initial Prior Using Weighting Functions (DIFSP)
88(1)
4.6 Specification of the Design Matrices for Model Estimation and Selection in the Urn-Ball Task
89(1)
4.7 Results
90(16)
4.7.1 Conventional ERP Analyses
90(4)
4.7.2 Model-Based Trial-by-Trial Analyses
94(12)
4.8 Summary and Discussion
106(5)
5 Summary and Outlook
111(4)
Appendix 115(4)
Bibliography 119