This book describes the types of computation that can be performed by biologically plausible neural networks and shows how they may be implemented in different systems of the brain. It is structured in three sections, each of which addresses a different need. The first introduces and analyzes the op
Biological Neural Networks: Hierarchical Concept of Brain Function
β Scribed by Konstantin V. Baev (auth.)
- Publisher
- BirkhΓ€user Basel
- Year
- 1996
- Tongue
- English
- Leaves
- 306
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This book is devoted to a novel conceptual theoretical framework of neuroΒ 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 funcΒ tional principles. Accordingly, the principles that have been revealed experiΒ mentally to govern inborn motor automatisms, such as locomotion and scratching, are used to elucidate the nature of acquired or learned automatΒ 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 underΒ standable to a biologist or physician. I have also included plenty of illustraΒ tive examples and references designed to demonstrate the appropriateness and applicability of these conceptual theoretical notions for the neurosciences.
β¦ Table of Contents
Front Matter....Pages i-xxxiii
Introduction....Pages 1-6
Limitations of Analytical Mechanistic Approaches to Biological Neural Networks....Pages 7-47
The Control Theory Approach to Biological Neural Networks....Pages 48-70
A Central Pattern Generator Includes A Model of Controlled Object: An Experimental Proof....Pages 71-86
The Spinal Motor Optimal Control System....Pages 87-101
Generalizing the Concept of a Neural Optimal Control System: A Generic Neural Optimal Control System....Pages 102-109
Learning in Artificial and Biological Neural Networks....Pages 110-125
The Hierarchy of Neural Control Systems....Pages 126-131
Application of the Concept of Optimal Control Systems to Inborn Motor Automatisms in Various Animal Species....Pages 132-142
The Stretch-Reflex System....Pages 143-147
The Cerebellum....Pages 148-155
The Skeletomotor Cortico-Basal Ganglia-Thalamocortical Circuit....Pages 156-174
The Limbic System....Pages 175-194
The Prefrontal Cortex....Pages 195-200
Conclusion....Pages 201-242
Back Matter....Pages 243-273
β¦ Subjects
Life Sciences, general; Biomedicine general
π SIMILAR VOLUMES
Exploring one of the most exciting and potentially rewarding areas of scientific research, the study of the principles and mechanisms underlying brain function, this book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated
<p><P>Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions
<p><P>Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions
<p><P>Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions