Biological Neural Networks: Hierarchical Concept of Brain Function / Edition 1

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Overview

Biological Neural Networks presents a novel conceptual framework for neurobiology achieved by the application of control theory. This new paradigm provides unifying principles for understanding the functional construction of the nervous system. Konstantin Baev argues forcefully that all hierarchical levels of the nervous system are built according to the same functional principles, which are shown to underlie the highest forms of brain function. Each network hierarchy is structurally and functionally organized in such a way that a lower control system in the nervous system becomes the controlled object for a higher one, and each level of control possesses a behavioral model of its controlled object. Baev writes with an interdisciplinary readership in mind. Neuroscientists, computer specialists and mathematicians, physicists, and clinicians devoted to deciphering the way the brain works will all find this book fascinating and stimulating reading.

The book contains black-and-white illustrations.

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Editorial Reviews

Doody's Review Service
Reviewer: Thomas H. Jobe, MD (University of Illinois at Chicago College of Medicine)
Description: This volume is a systematic presentation of the author's theory of neurocomputation. He has developed the "functional automatism" theory of neurocomputing to a high level of conceptual clarity. He shows how Kolmogorov's theorem (1957) can be applied to the new concept of a "spiking" dendritic tree to explain learning in reflex motor activity and how hierarchies of reflexes can control manipulation of external objects by an organism.
Purpose: The author has successfully applied the functional automatism concept of neurocomputing to motor reflex learning and control. His purpose is also to critique competing theories of neurocomputation.
Audience: This book is intended for neurobiologists, neurologists, neurosurgeons and neuropsychiatrists. It provides an ingenious model of Parkinson's Disease that explains how neurosurgical interventions that damage tissue can actually improve function.
Features: Several excellent diagrams and illustrations are included that illustrate concepts of motor control by hierarchical reflex integration. Experiments that illustrate "functional automatisms" are well illustrated.
Assessment: The author has achieved a lucid and well-argued presentation of his model of neurocomputation. The strength of the model rests on the fact that it originally derives from biological control systems (vonHolst) and then makes use of important analogical extensions to achieve generality and complexity not found in more "mechanical" physics based models such as those of Hopfield and Tank. The author's approach thus stands as an important biological alternative to the dominant mechanistic trend in neural network theory.
Thomas H. Jobe
This volume is a systematic presentation of the author's theory of neurocomputation. He has developed the "functional automatism" theory of neurocomputing to a high level of conceptual clarity. He shows how Kolmogorov's theorem (1957) can be applied to the new concept of a "spiking" dendritic tree to explain learning in reflex motor activity and how hierarchies of reflexes can control manipulation of external objects by an organism. The author has successfully applied the functional automatism concept of neurocomputing to motor reflex learning and control. His purpose is also to critique competing theories of neurocomputation. This book is intended for neurobiologists, neurologists, neurosurgeons and neuropsychiatrists. It provides an ingenious model of Parkinson's Disease that explains how neurosurgical interventions that damage tissue can actually improve function. Several excellent diagrams and illustrations are included that illustrate concepts of motor control by hierarchical reflex integration. Experiments that illustrate "functional automatisms" are well illustrated. The author has achieved a lucid and well-argued presentation of his model of neurocomputation. The strength of the model rests on the fact that it originally derives from biological control systems (vonHolst) and then makes use of important analogical extensions to achieve generality and complexity not found in more "mechanical" physics based models such as those of Hopfield and Tank. The author's approach thus stands as an important biological alternative to the dominant mechanistic trend in neural network theory.
Booknews
Presents a novel conceptual framework for neurobiology, achieved by applying control theory. It provides unifying principles for understanding the functional construction of the nervous system, and argues that all hierarchical levels of the nervous system are built according to the same functional principles, which underlie the highest forms of brain function. Within the framework, each network hierarchy is structurally and functionally organized so that a lower control system becomes the controlled object for a higher one, and each level of control processes a behavioral model of its controlled object. For neuroscientists, computer scientists and mathematicians, physicists, and clinicians interesting in deciphering how the brain works. Annotation c. by Book News, Inc., Portland, Or.

4 Stars! from Doody
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Product Details

  • ISBN-13: 9780817638597
  • Publisher: Birkhauser Verlag
  • Publication date: 4/30/1998
  • Edition description: 1996
  • Edition number: 1
  • Pages: 316
  • Product dimensions: 0.75 (w) x 6.14 (h) x 9.21 (d)

Table of Contents

Preface
Acknowledgments
Foreword
Foreword
Introduction 1
1 Limitations of Analytical Mechanistic Approaches to Biological Neural Networks 7
2 The Control Theory Approach to Biological Neural Networks 48
3 A Central Pattern Generator Includes A Model of Controlled Object: An Experimental Proof 71
4 The Spinal Motor Optimal Control System 87
5 Generalizing the Concept of a Neural Optimal Control System: A Generic Neural Optimal Control System 102
6 Learning in Artificial and Biological Neural Networks 110
7 The Hierarchy of Neural Control Systems 126
8 Application of the Concept of Optimal Control Systems to Inborn Motor Automatisms in Various Animal Species 132
9 The Stretch-Reflex System 143
10 The Cerebellum 148
11 The Skeletomotor Cortico-Basal Ganglia-Thalamocortical Circuit 156
12 The Limbic System 175
13 The Prefrontal Cortex 195
14 Conclusion 201
References 226
App. 1 The Main Properties of Sensory Information Sources and Channels 243
App. 2 Functioning of the Internal Model of the Controlled Object 246
App. 3 The Spinal Optimal Motor Control System as a Neural Network 249
Abbreviations 254
Index 257
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