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Preface
Acknowledgments

Chapter 1—Introduction
1.1 General
1.2 Stochastical Aspects and Physics of Neural Activity
1.3 Neurocybernetic Concepts
1.4 Statistical Mechanics-Cybernetics-Neural Complex
1.5 Concluding Remarks

Chapter 2—Neural and Brain Complex
2.1 Introduction
2.2 Gross Features of the Brain and the Nervous System
2.3 Neurons and Their Characteristics
2.4 Biochemical and Electrical Activities in Neurons
2.5 Mode(s) of Communication among Neurons
2.6 Collective Response of Neurons
2.7 Neural Net: A Self-Organizing Finite Automaton
2.8 Concluding Remarks

Chapter 3—Concepts of Mathematical Neurobiology
3.1 Mathematical Neurobiology: Past and Present
3.2 Mathematics of Neural Activities
3.2.1 General considerations
3.2.2 Random sequence of neural potential spikes
3.2.3 Neural field theory
3.3 Models of Memory in Neural Networks
3.4 Net Function and Neuron Function
3.5 Concluding Remarks

Chapter 4—Pseudo-Thermodynamics of Neural Activity
4.1 Introduction
4.2 Machine Representation of Neural Network
4.3 Neural Network versus Machine Concepts
4.3.1 Boltzmann Machine
4.3.2 McCulloch-Pitts Machine
4.3.3 Hopfield Machine
4.3.4 Gaussian Machine
4.4 Simulated Annealing and Energy Function
4.5 Cooling Schedules
4.6 Reverse-Cross and Cross Entropy Concepts
4.7 Activation Rule
4.8 Entropy at Equilibrium
4.9 Boltzmann Machine as a Connectionist Model
4.10 Pseudo-Thermodynamic Perspectives of Learning Process
4.11 Learning from Examples Generated by a Perceptron
4.12 Learning at Zero Temperature
4.13 Concluding Remarks

Chapter 5—The Physics of Neural Activity: A Statistical Mechanics Perspective
5.1 Introduction
5.2 Cragg and Temperley Model
5.3 Concerns of Griffith
5.4 Little’s Model
5.5 Thompson and Gibson Model
5.6 Hopfield’s Model
5.7 Peretto’s Model
5.8 Little’s Model versus Hopfield’s Model
5.9 Ising Spin System versus Interacting Neurons
5.10 Liquid-Crystal Model
5.11 Free-Point Molecular Dipole Interactions
5.12 Stochastical Response of Neurons under Activation
5.13 Hamiltonian of Neural Spatial Long-Range Order
5.14 Spatial Persistence in the Nematic Phase
5.15 Langevin Machine
5.16 Langevin Machine versus Boltzmann Machine
5.17 Concluding Remarks

Chapter 6—Stochastical Dynamics of the Neural Complex
6.1 Introduction
6.2 Stochastical Dynamics of the Neural Assembly
6.3 Correlation of Neuronal State Disturbances
6.4 Fokker-Planck Equation of Neural Dynamics
6.5 Stochastical Instability in Neural Networks
6.6 Stochastical Bounds and Estimates of Neuronal Activity
6.7 Stable States Search via Modified Bias Parameter
6.8 Noise-Induced Effects on Saturated Neural Population
6.9 Concluding Remarks

Chapter 7—Neural Field Theory: Quasiparticle Dynamics and Wave Mechanics Analogies of Neural Networks
7.1 Introduction
7.2 “Momentum-Flow” Model of Neural Dynamics
7.3 Neural “Particle” Dynamics
7.4 Wave Mechanics Representation of Neural Activity
7.5 Characteristics of Neuronal Wave Function
7.6 Concepts of Wave Mechanics versus Neural Dynamics
7.7 Lattice Gas System Analogy of Neural Assembly
7.8 The Average Rate of Neuronal Transmission Flow
7.9 Models of Peretto and Little versus Neuronal Wave
7.10 Wave Functional Representation of Hopfield’s Network
7.11 Concluding Remarks

Chapter 8—Informatic Aspects of Neurocybernetics
8.1 Introduction
8.2 Information-Theoretics of Neural Networks
8.3 Information Base of Neurocybernetics
8.4 Informatics of Neurocybernetic Processes
8.5 Disorganization in the Neural System
8.6 Entropy of Neurocybernetic Self-Regulation
8.7 Subjective Neural Disorganization
8.8 Continuous Neural Entropy
8.9 Differential Disorganization in the Neural Complex
8.10 Dynamic Characteristics of Neural Informatics
8.11 Jensen-Shannon Divergence Measure
8.12 Semiotic Framework of Neuroinformatics
8.13 Informational Flow in the Neural Control Process
8.14 Dynamic State of Neural Organization
8.15 Concluding Remarks
Bibliography
Appendix A
Appendix B
Appendix C
Index
Copyright © CRC Press LLC

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