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Book Categories |
Ch. 1 | Introduction | |
1.1 | Neuroinformatics | 2 |
1.2 | Information-Theoretic Framework of Neural Networks | 15 |
1.3 | Entropy, Thermodynamics and Information Theory | 20 |
1.4 | Information-Theoretics and Neural Network Training | 22 |
1.5 | Dynamics of Neural Learning in the Information- Theoretic Plane | 38 |
1.6 | Neural Nonlinear Activity in the Information- Theoretic Plane | 40 |
1.7 | Degree of Neural Complexity and Maximum Entropy | 41 |
Ch. 2 | Neural Complex: A Nonlinear C[superscript 3]I System? | |
2.1 | Introduction | 71 |
2.2 | Neural Information Processing: C[superscript 3]I Protocols | 72 |
2.3 | Nonlinear Neural Activity | 76 |
2.4 | Bernoulli-Riccati Equations | 80 |
2.5 | Nonlinear Neural Activity: Practical Considerations | 83 |
2.6 | Entropy/Information Across a Neural Nonlinear Process | 88 |
2.7 | Nonsigmoidal Activation Functions | 88 |
2.8 | Definitions and Lemmas on Certain Classes of Nonlinear Functions | 89 |
Ch. 3 | Nonlinear and Informatic Aspects of Fuzzy Neural Activity | |
3.1 | Introduction | 113 |
3.2 | What is Fuzzy Activity? | 113 |
3.3 | Crisp Set versus Fuzzy Sets | 115 |
3.4 | Membership Attributes to a Fuzzy Set | 116 |
3.5 | Fuzzy Neural Activity | 119 |
3.6 | Fuzzy Differential Equations | 120 |
3.7 | Membership Attributions to Fuzzy Sets via Activation Function | 126 |
3.8 | Neural Architecture with a Fuzzy Sigmoid | 130 |
3.9 | Fuzzy Considerations, Uncertainty and Information | 134 |
3.10 | Information-Theoretics of Crisp and Fuzzy Sets | 135 |
3.11 | Fuzzy Neuroinformatics | 141 |
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