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by Daniel M Rice
Calculus of Thought
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Title page
Table of Contents
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Preface
A Personal Perspective
Chapter 1. Calculus Ratiocinator
Abstract
1 A Fundamental Problem with the Widely Used Methods
2 Ensemble Models and Cognitive Processing in Playing Jeopardy
3 The Brain's Explicit and Implicit Learning
4 Two Distinct Modeling Cultures and Machine Intelligence
5 Logistic Regression and the Calculus Ratiocinator Problem
Chapter 2. Most Likely Inference
Abstract
1 The Jaynes Maximum Entropy Principle
2 Maximum Entropy and Standard Maximum Likelihood Logistic Regression
3 Discrete Choice, Logit Error, and Correlated Observations
4 RELR and the Logit Error
5 RELR and the Jaynes Principle
Chapter 3. Probability Learning and Memory
Abstract
1 Bayesian Online Learning and Memory
2 Most Probable Features
3 Implicit RELR
4 Explicit RELR
Chapter 4. Causal Reasoning
Abstract
1 Propensity Score Matching
2 RELR's Outcome Score Matching
3 An Example of RELR's Causal Reasoning
4 Comparison to Other Bayesian and Causal Methods
Chapter 5. Neural Calculus
Abstract
1 RELR as a Neural Computational Model
2 RELR and Neural Dynamics
3 Small Samples in Neural Learning
4 What about Artificial Neural Networks?
Chapter 6. Oscillating Neural Synchrony
Abstract
1 The EEG and Neural Synchrony
2 Neural Synchrony, Parsimony, and Grandmother Cells
3 Gestalt Pragnanz and Oscillating Neural Synchrony
4 RELR and Spike-Timing-Dependent Plasticity
5 Attention and Neural Synchrony
6 Metrical Rhythm in Oscillating Neural Synchrony
7 Higher Frequency Gamma Oscillations
Chapter 7. Alzheimer's and Mind–Brain Problems
Abstract
1 Neuroplasticity Selection in Development and Aging
2 Brain and Cognitive Changes in Very Early Alzheimer's Disease
3 A RELR Model of Recent Episodic and Semantic Memory
4 What Causes the Medial Temporal Lobe Disturbance in Early Alzheimer's?
5 The Mind–Brain Problem
Chapter 8. Let Us Calculate
Abstract
1 Human Decision Bias and the Calculus Ratiocinator
2 When the Experts are Wrong
3 When Predictive Models Crash
4 The Promise of Cognitive Machines
Appendix
A1 RELR Maximum Entropy Formulation
A2 Derivation of RELR Logit from Errors-in-Variables Considerations
A3 Methodology for Pew 2004 Election Weekend Model Study
A4 Derivation of Posterior Probabilities in RELR's Sequential Online Learning
A5 Chain Rule Derivation of Explicit RELR Feature Importance
A6 Further Details on the Explicit RELR Low Birth Weight Model in Chapter 3
A7 Zero Intercepts in Perfectly Balanced Stratified Samples
A8 Detailed Steps in RELR's Causal Machine Learning Method
Notes and References
Index
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Table of Contents
Calculus of Thought
Neuromorphic Logistic Regression in Cognitive Machines
Daniel M. Rice
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