Human Memory Modeled with Standard Analog and Digital Circuits: Inspiration for Man-made Computers / Edition 1 available in Hardcover
- Pub. Date:
Gain a new perspective on how the brain works and inspires new avenues for design in computer science and engineering
This unique book is the first of its kind to introduce human memory and basic cognition in terms of physical circuits, beginning with the possibilities of ferroelectric behavior of neural membranes, moving to the logical properties of neural pulses recognized as solitons, and finally exploring the architecture of cognition itself. It encourages invention via the methodical study of brain theory, including electrically reversible neurons, neural networks, associative memory systems within the brain, neural state machines within associative memory, and reversible computers in general. These models use standard analog and digital circuits that, in contrast to models that include non-physical components, may be applied directly toward the goal of constructing a machine with artificial intelligence based on patterns of the brain.
Writing from the circuits and systems perspective, the author reaches across specialized disciplines including neuroscience, psychology, and physics to achieve uncommon coverage of:
- Neural membranes
- Neural pulses and neural memory
- Circuits and systems for memorizing and recalling
- Dendritic processing and human learning
- Artificial learning in artificial neural networks
- The asset of reversibility in man and machine
- Electrically reversible nanoprocessors
- Reversible arithmetic
- Hamiltonian circuit finders
- Quantum versus classical
Each chapter introduces and develops new material and ends with exercises for readers to put their skills into practice. Appendices are provided for non-experts who want a quick overview of brain anatomy, brain psychology, and brain scanning. The nature of this book, with its summaries of major bodies of knowledge, makes it a most valuable reference for professionals, researchers, and students with career goals in artificial intelligence, intelligent systems, neural networks, computer architecture, and neuroscience.
A solutions manual is available for instructors; to obtain a copy please email the editorial department at firstname.lastname@example.org.
|Product dimensions:||6.30(w) x 9.30(h) x 0.90(d)|
About the Author
JOHN ROBERT BURGER taught at the University of the Pacific in Stockton, California; California State University, Northridge; and the Oregon Institute of Technology, where he developed a course in artificial neural networks. Dr. Burger and his students have designed and fabricated many CMOS integrated circuits over the years. He is interested in the propagation of electrical wavefronts in conductors, neural and otherwise, an interest he has enjoyed since his boyhood telegraph inventions.
Table of Contents
1 BRAIN BEHAVIOR POINTS THE WAY.
Uses of models.
Why Thinking Dissipates So Few Calories?
The Miracle of Parallel Processing.
The Benefits of Reading This Book.
Overview of the Book.
Applications of the Models in the Book.
2 NEURAL MEMBRANES AND ANIMAL ELECTRICITY.
The Physical Neuron.
Ionic Solutions and Stray Electrons.
3 NEURAL PULSES AND NEURAL MEMORY.
Derivation of a Neural Pulse Using Basic Physics.
Neuron Signal Propagation.
Modeling Neurons as Adiabatic.
Neurons for Memory.
Appendix: Asymptotically Adiabatic Circuits.
4 CIRCUITS AND SYSTEMS FOR MEMORIZATION AND RECALL.
Psychological Considerations When Modeling Human Memory.
Basic Assumptions to Create A Model.
Short-Term Memory and Consciousness.
Discussion of the Model.
Enable Neural Logic.
Models for Memorization.
5 DENDRITIC PROCESSING AND HUMAN LEARNING.
Biological Versus Artificial Neural Networks.
Neurons for Combinational Learning.
Neurons for State-Machine Learning.
Dendritic Processing Models.
Enabled Logic Directly at the Soma.
Comments on the Adiabatic Nature of Dendrites.
Appendix: Circuit Simulations of Neural Soliton Propagation.
6 ARTIFICIAL LEARNING IN ARTIFICIAL NEURAL NETWORKS.
Artificial Learning Methods.
Discussion of Learning Methods.
7 THE ASSET OF REVERSIBILITY IN HUMANS AND MACHINES.
Neural Models that Explain Savants.
Parallel Processing and the Savant Brain.
Computational Possibilities Using Conditional Toggle Memory.
The Cost of Computation.
Appendix: Split-Level Charge Recovery Logic.
8 ELECTRICALLY REVERSIBLE NANOPROCESSORS.
A Gauge for Classical Parallelism.
Design Rules for Electrical Reversibility.
Reversible System Architecture.
Architecture for Self-Analyzing Memory Words.
Electrically Reversible Toggle Circuit.
Reversible Addition Programming Example.
Reversible Subtraction Programming Example.
9 MULTIPLICATION, DIVISION, AND HAMILTONIAN CIRCUITS.
Solving Hard Problems.
The Initialization of Toggle Memory in Nanoprocessors.
Logically Reversible Programming Using Nanobrains.
10 QUANTUM VERSUS CLASSICAL COMPUTING.
Quantum Boolean Functions.
Quantum Computer Programming.
Historical Quantum Computing Algorithms.
APPENDIX A HUMAN BRAIN ANATOMY.
Components of a Brain.
APPENDIX B THE PSYCHOLOGICAL SCIENCE OF MEMORY.
Studies in Learning.
APPENDIX C BRAIN SCANNING.
Magnetic Resonance Imaging.
Functional Magnetic Resonance Imaging.
Positron Emission Tomography.
Computerized Axial Tomography.
APPENDIX D BIOGRAPHIES OF PERSONS OF SCIENTIFIC INTEREST.
FOR FURTHER STUDY.