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" Biological neural networks : "
Konstantin V. Baev.
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BL
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| Record Number
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726657
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b546389
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| Main Entry
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Konstantin V. Baev.
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| Title & Author
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Biological neural networks : : hierarchical concept of brain\ Konstantin V. Baev.
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| Publication Statement
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Boston: Birkhauser, 1998
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| Page. NO
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XXXVII, [1], 273 s. : il. ; 24 cm
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| ISBN
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1461241006
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: 1461286522
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: 9781461241003
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: 9781461286523
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| Contents
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1 Limitations of Analytical Mechanistic Approaches to Biological Neural Networks.- 1.1 Inborn Automatic Behaviors From the Point of View of Classical Theory.- 1.1.1 A Brief Review of the Locomotor Behavior Evolution in Animals.- 1.1.2 Initiation of Inborn Automatic Behaviors.- 1.1.3 The Problem of Organization of Central Pattern Generators for Inborn Automatic Behaviors.- 1.1.4 Afferent Correction of Central Pattern Generators.- 1.1.5 Ontogenesis of Locomotor Function.- 1.1.6 Is the Formulation of the Generator Problem Correct?.- 1.2 Learning from the Point of View of Classical Theory.- 1.2.1 Classical Conditioning of the Eyelid Closure Response.- 2 The Control Theory Approach to Biological Neural Networks.- 2.1 A Brief Historical Review of the Development of Automatic Control Theory.- 2.2 Basic Concepts of Control Theory.- 2.3 Computational Abilities of Biological Neural Networks.- 2.4 Broadening the Concept of Automatism: Inborn Automatisms and Acquired Habits.- 2.5 How Can Control Theory Concepts Be Applied to Biological Neural Networks?.- 3 A Central Pattern Generator Includes A Model of Controlled Object: An Experimental Proof.- 4 The Spinal Motor Optimal Control System.- 4.1 Sensory Information Processing in the Spinal Motor Control System.- 4.2 The Essence of the Internal Model of the Controlled Object.- 4.3 The Internal Representation of the Controlled Object Phase State.- 4.4 The Principal Features of the Neural Organization of Internal Representations of the Controlled Object State and Its Model.- 4.5 Neural Mechanisms for Calculating the Most Probable Current State of the Controlled Object.- 5 Generalizing the Concept of a Neural Optimal Control System: A Generic Neural Optimal Control System.- 6 Learning in Artificial and Biological Neural Networks.- 6.1 The Problem of Learning in Neurocomputing.- 6.2 Basic Principles of Learning in Biological Neural Networks.- 6.2.1 An Analogy: Brownian Motion of Particles in the Presence of a Temperature Gradient.- 6.2.2 Change of Neuronal Transfer Function Due to the Influence of Initiating Signals.- 6.2.3 Change of a Function Calculated by a Neural Network.- 6.2.4 Basic Principles of Classical Conditioning.- 7 The Hierarchy of Neural Control Systems.- 8 Application of the Concept of Optimal Control Systems to Inborn Motor Automatisms in Various Animal Species.- 8.1 The Principle of Motor Automatism Initiation.- 8.2 Invertebrate Central Pattern Generators from the Perspective of the Optimal Control System.- 8.3 Vertebrate Central Pattern Generators from the Perspective of the Optimal Control System.- 8.4 The Phenomenon of Entrainment of Central Rhythms.- 8.5 A Generator is a Learning System!.- 9 The Stretch-Reflex System.- 10 The Cerebellum.- 10.1 The Semantics of Cerebellar Inputs.- 10.2 How the Cerebellum Learns to Coordinate Movements.- 11 The Skeletomotor Cortico-Basal Ganglia-Thalamocortical Circuit.- 11.1 An Anatomical Survey of Cortico-Basal Ganglia-Thalamocortical Circuits.- 11.2 What is Modeled by the Skeletomotor Basal Ganglia-Thalamocortical Circuit?.- 11.3 An Error Distribution System.- 11.4 Clinical Applications of the Theory.- 11.4.1 Parkinson's Disease.- 12 The Limbic System.- 12.1 Associated Automatisms.- 12.1.1 Automatisms Subordinated to the Hypothalamus.- 12.1.2 Initiating Signals of the Hypothalamus.- 12.1.3 Cortical Automatisms Used by the Limbic System.- 12.2 Specificity of Control Tasks: General Considerations.- 12.2.1 The Coordination Problem.- 12.2.2 Long-Range Space and Time Orientation.- 12.2.3 From Conditioned Reflex to Operant Learning.- 12.3 Functions of Different Limbic Structures.- 12.3.1 The Hypothalamus.- 12.3.2 The Hippocampus.- 12.3.3 The Cingulate Gyrus and its Cortico-Basal Ganglia-Thalamocortical Loop.- 13 The Prefrontal Cortex.- 13.1 Means for Further Evolutionary Improvements.- 13.2 Prefrontal Cortico-Basal Ganglia-Thalamocortical Loops.- 14 Conclusion.- 14.1 The Variety of Memory Mechanisms in the Brain.- 14.2 Non-Neuronal Network Cellular and Molecular Systems.- 14.2.1 The Immune System.- 14.2.2 Intracellular Systems.- 14.3 Evolution and Learning Processes.- 14.4 Self-Applicability of the Theory and Its Application to Other Sciences.- 14.5 Future of Neurobiology for Physicists.- 14.6 Artificial Intelligence and Future Neurocomputers.- References.- 1 The Main Properties of Sensory Information Sources and Channels.- 2 Functioning of the Internal Model of the Controlled Object.- 3 The Spinal Optimal Motor Control System as a Neural Network.- Abbreviations.
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| LC Classification
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QP363.3K667 1998
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Birkhäuser Verlag.
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Konstantin V Baev
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