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E-raamat: Information-Theoretic Aspects of Neural Networks

(Florida Atlantic University, Boca Raton, Florida, USA)
  • Formaat: 416 pages
  • Ilmumisaeg: 23-Sep-2020
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781000102758
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Information-Theoretic Aspects of Neural NetworksSuurem pilt
  • Formaat: 416 pages
  • Ilmumisaeg: 23-Sep-2020
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781000102758
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Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information.
Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as:
  • Shannon information and information dynamics
  • neural complexity as an information processing system
  • memory and information storage in the interconnected neural web
  • extremum (maximum and minimum) information entropy
  • neural network training
  • non-conventional, statistical distance-measures for neural network optimizations
  • symmetric and asymmetric characteristics of information-theoretic error-metrics
  • algorithmic complexity based representation of neural information-theoretic parameters
  • genetic algorithms versus neural information
  • dynamics of neurocybernetics viewed in the information-theoretic plane
  • nonlinear, information-theoretic transfer function of the neural cellular units
  • statistical mechanics, neural networks, and information theory
  • semiotic framework of neural information processing and neural information flow
  • fuzzy information and neural networks
  • neural dynamics conceived through fuzzy information parameters
  • neural information flow dynamics
  • informatics of neural stochastic resonance
    Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.
    • Introduction
    Neuroinformatics
    		2(13)
    Information-Theoretic Framework of Neural Networks
    		15(5)
    Entropy, Thermodynamics and Information Theory
    		20(2)
    Information-Theoretics and Neural Network Training
    		22(16)
    Dynamics of Neural Learning in the Information-Theoretic Plane
    		38(2)
    Neural Nonlinear Activity in the Information-Theoretic Plane
    		40(1)
    Degree of Neural Complexity and Maximum Entropy
    		41(2)
    Concluding Remarks
    		43(28)
    Bibliography
    		45(8)
    Appendix 1.1
    		53(8)
    Appendix 1.2
    		61(8)
    Appendix 1.3
    		69(2)
    Neural Complex: A Nonlinear C3I System
    Introduction
    		71(1)
    Neural Information Processing: C3I Protocols
    		72(4)
    Nonlinear Neural Activity
    		76(4)
    Bernoulli-Riccati Equations
    		80(3)
    Nonlinear Neural Activity: Practical Considerations
    		83(5)
    Entropy/Information Across a Neural Nonlinear Process
    		88(1)
    Nonsigmoidal Activation Functions
    		88(1)
    Definitions and Lemmas on Certain Classes of Nonlinear Functions
    		89(2)
    Concluding remarks
    		91(22)
    Bibliography
    		95(4)
    Appendix 2.1
    		99(6)
    Appendix 2.2
    		105(8)
    Nonlinear and Informatic Aspects of Fuzzy Neural Activity
    Introduction
    		113(1)
    What is Fuzzy Activity
    		113(2)
    Crisp Set versus Fuzzy Sets
    		115(1)
    Membership Attributes to a Fuzzy Set
    		116(3)
    Fuzzy Neural Activity
    		119(1)
    Fuzzy Differential Equations
    		120(6)
    Membership Attributions to Fuzzy Sets via Activation Function
    		126(4)
    Neural Architecture with a Fuzzy Sigmoid
    		130(4)
    Fuzzy Considerations, Uncertainty and Information
    		134(1)
    Information-Theoretics of Crisp and Fuzzy Sets
    		135(6)
    Fuzzy Neuroinformatics
    		141(6)
    Concluding Remarks
    		147(6)
    Csiszar's Information-Theoretic Error-Measures for Neural Network Optimizations
    Introduction
    		153(1)
    Disorganization and Entropy Considerations in Neural Networks
    		154(4)
    Information-Theoretic Error Measures
    		158(12)
    Neutral Nonlinear Response versus Optimization Algorithms
    		170(3)
    A Multilayer Perceptron Training with Information-Theoretic Cost-Functions
    		173(3)
    Results on Neural Network Training with Csiszar's Error-Measures
    		176(36)
    Generalized Csiszar's Symmetrized Error-Measures
    		212(10)
    Concluding Remarks
    		222(11)
    Bibliography
    		229(4)
    Dynamics of Neural Learning in the Information-Theoretic Plane
    Introduction
    		233(4)
    Stochastical Neural Dynamics
    		237(1)
    Stochastical Dynamics of the Error-Measures (ε)
    		238(1)
    Random-Walk Paradigm of ε(t) Dynamics
    		239(4)
    Evolution of ε(t): Representation via the Fokker-Planck Equation
    		243(2)
    Logistic Growth Model of ε(t)
    		245(4)
    Convergence Considerations
    		249(8)
    Further Considerations on the Dynamics of ε(t)
    		257(6)
    Dynamics of Fuzzy Uncertainty
    		263(4)
    Concluding Remarks
    		267(4)
    Bibliography
    		269(2)
    Informatic Perspectives of Complexity Measure in Neural Networks
    Introduction
    		271(1)
    Neural Complexity
    		271(8)
    Complexity Measure
    		279(5)
    Neural Networks: Simple and Complex
    		284(2)
    Neural Complexity versus Neural Entropy
    		286(3)
    Neural Network Training via Complexity Parameter
    		289(2)
    Calculation of μTi and σTi
    		291(2)
    Perceptron Training: Simulated Results
    		293(4)
    Concluding Remarks
    		297(8)
    Bibliography
    		299(2)
    Appendix 6.1
    		301(4)
    Information-Theoretic Aspects of Neural Stochastic Resonance
    Introduction
    		305(8)
    Inter-Event Histograms (IIH) and Stochastic Resonance
    		313(4)
    A Neural Network under SR-Based Learning
    		317(4)
    Simulation Results
    		321(3)
    Concluding Remarks
    		324(7)
    Bibliography
    		327(4)
    Neural Informatics and Genetic Algorithms
    Entropy, Thermobiodynamics and Bioinformatics
    		331(11)
    Genetic Code
    		342(2)
    Search Algorithms
    		344(14)
    Simple Genetic Algorith (SGA)
    		358(5)
    Genetic Algorithms and Neural Networks
    		363(7)
    Information-Theoretics of Genetic Selection Algorithm
    		370(4)
    A Test ANN Architecture Deploying GA and IT Concepts
    		374(5)
    Description of the Algorithm
    		379(5)
    Experimental Simulations
    		384(9)
    Concluding Remarks
    		393(4)
    Bibliography
    		395(2)
    Subject Index 397
    P. S. Neelakanta
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