Biological Neural Networks: Hierarchical Concept of Brain Function

1. Front Matter

   Pages i-xxxiii

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2. Introduction

   • Konstantin V. Baev

   Pages 1-6

3. Limitations of Analytical Mechanistic Approaches to Biological Neural Networks

   • Konstantin V. Baev

   Pages 7-47

4. The Control Theory Approach to Biological Neural Networks

   • Konstantin V. Baev

   Pages 48-70

5. A Central Pattern Generator Includes A Model of Controlled Object: An Experimental Proof

   • Konstantin V. Baev

   Pages 71-86

6. The Spinal Motor Optimal Control System

   • Konstantin V. Baev

   Pages 87-101

7. Generalizing the Concept of a Neural Optimal Control System: A Generic Neural Optimal Control System

   • Konstantin V. Baev

   Pages 102-109

8. Learning in Artificial and Biological Neural Networks

   • Konstantin V. Baev

   Pages 110-125

9. The Hierarchy of Neural Control Systems

   • Konstantin V. Baev

   Pages 126-131

10. Application of the Concept of Optimal Control Systems to Inborn Motor Automatisms in Various Animal Species

    • Konstantin V. Baev

    Pages 132-142

11. The Stretch-Reflex System

    • Konstantin V. Baev

    Pages 143-147

12. The Cerebellum

    • Konstantin V. Baev

    Pages 148-155

13. The Skeletomotor Cortico-Basal Ganglia-Thalamocortical Circuit

    • Konstantin V. Baev

    Pages 156-174

14. The Limbic System

    • Konstantin V. Baev

    Pages 175-194

15. The Prefrontal Cortex

    • Konstantin V. Baev

    Pages 195-200

16. Conclusion

    • Konstantin V. Baev

    Pages 201-242

17. Back Matter

    Pages 243-273

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This book is devoted to a novel conceptual theoretical framework of neuro­ science and is an attempt to show that we can postulate a very small number of assumptions and utilize their heuristics to explain a very large spectrum of brain phenomena. The major assumption made in this book is that inborn and acquired neural automatisms are generated according to the same func­ tional principles. Accordingly, the principles that have been revealed experi­ mentally to govern inborn motor automatisms, such as locomotion and scratching, are used to elucidate the nature of acquired or learned automat­ isms. This approach allowed me to apply the language of control theory to describe functions of biological neural networks. You, the reader, can judge the logic of the conclusions regarding brain phenomena that the book derives from these assumptions. If you find the argument flawless, one can call it common sense and consider that to be the best praise for a chain of logical conclusions. For the sake of clarity, I have attempted to make this monograph as readable as possible. Special attention has been given to describing some of the concepts of optimal control theory in such a way that it will be under­ standable to a biologist or physician. I have also included plenty of illustra­ tive examples and references designed to demonstrate the appropriateness and applicability of these conceptual theoretical notions for the neurosciences.

• Biological Networks
• Cortex
• Signal
• Structure and Organization
• artificial intelligence
• behavior
• model
• semantics

• Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, USA

  Konstantin V. Baev

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