John Robert Burger

Human Memory Modeled with Standard Analog and Digital Circuits

Inspiration for Man-Made Computers

• Gebundenes Buch

Produktbeschreibung

Offering a new perspective on how the brain works and how it inspires new avenues for design in computer science and engineering, Human Memory Modeled with Standard Analog and Digital Circuits explains (artificial) neural networks by means of man-made circuit configurations inspired by brain sciences. The book reveals the workings of cognition and memory, including the ferroelectric behavior of neural membranes, logical properties of neural pulses, and the architecture of cognition. Ample examples of reversible programming for massively parallel processors (and quantum computers) provide graduate students, professionals, and researchers with a vital resource.

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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 [e-mail].

Produktdetails

• Verlag: Wiley & Sons
• Artikelnr. des Verlages: 14542435000
• 1. Auflage
• Seitenzahl: 384
• Erscheinungstermin: 1. Juli 2009
• Englisch
• Abmessung: 240mm x 161mm x 25mm
• Gewicht: 644g
• ISBN-13: 9780470424353
• ISBN-10: 0470424354
• Artikelnr.: 26178070

Autorenporträt

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.

Inhaltsangabe

PREFACE. 1 BRAIN BEHAVIOR POINTS THE WAY. Introduction. Modeling. Uses of
models. Why Thinking Dissipates So Few Calories? The Miracle of Parallel
Processing. Singularity. The Benefits of Reading This Book. Overview of the
Book. Applications of the Models in the Book. Conclusions. Exercises. 2
NEURAL MEMBRANES AND ANIMAL ELECTRICITY. Introduction. The Physical Neuron.
Ionic Solutions and Stray Electrons. Nernst Voltage. Ion-Channel Model.
Applications. Conclusions. Exercises. 3 NEURAL PULSES AND NEURAL MEMORY.
Introduction. Derivation of a Neural Pulse Using Basic Physics. Neuron
Signal Propagation. Modeling Neurons as Adiabatic. Neurons for Memory.
Applications. Conclusions. Exercises. Appendix: Asymptotically Adiabatic
Circuits. 4 CIRCUITS AND SYSTEMS FOR MEMORIZATION AND RECALL. Introduction.
Psychological Considerations When Modeling Human Memory. Basic Assumptions
to Create A Model. Short-Term Memory and Consciousness. Cognitive
Architecture. Discussion of the Model. Enable Neural Logic. Models for
Memorization. Applications. Conclusions. Exercises. 5 DENDRITIC PROCESSING
AND HUMAN LEARNING. Introduction. Biological Versus Artificial Neural
Networks. Dendrites Neurons for Combinational Learning. Neurons for
State-Machine Learning. Learning Circuits. Dendritic Processing Models.
Enabled Logic Directly at the Soma. Comments on the Adiabatic Nature of
Dendrites. Applications. Conclusions. Exercises. Appendix: Circuit
Simulations of Neural Soliton Propagation. Conclusions. 6 ARTIFICIAL
LEARNING IN ARTIFICIAL NEURAL NETWORKS. Introduction. Artificial Neurons.
Artificial Learning Methods. Discussion of Learning Methods. Conclusion.
Exercises. 7 THE ASSET OF REVERSIBILITY IN HUMANS AND MACHINES.
Introduction. Savants. Neural Models that Explain Savants. Parallel
Processing and the Savant Brain. Computational Possibilities Using
Conditional Toggle Memory. The Cost of Computation. Reversible Programming.
Conclusions. Exercises. Appendix: Split-Level Charge Recovery Logic. 8
ELECTRICALLY REVERSIBLE NANOPROCESSORS. Introduction. 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. Conclusions. Exercises. 9
MULTIPLICATION, DIVISION, AND HAMILTONIAN CIRCUITS. Introduction. Unsigned
Multiplication. Restoring Division. Solving Hard Problems. Hamiltonian
Circuits. The Initialization of Toggle Memory in Nanoprocessors. Logically
Reversible Programming Using Nanobrains. Conclusions. Exercises. 10 QUANTUM
VERSUS CLASSICAL COMPUTING. Introduction. Physical Qubits. Quantum Boolean
Functions. Quantum Computer Programming. Historical Quantum Computing
Algorithms. Conclusions. Exercises. APPENDIX A HUMAN BRAIN ANATOMY.
Components of a Brain. Forebrain Structure. APPENDIX B THE PSYCHOLOGICAL
SCIENCE OF MEMORY. Short-Term Memory. Long-Term Memory. Studies in
Learning. Memory Retrieval. Serial Reproduction. Memory Theorists. Dreams.
APPENDIX C BRAIN SCANNING. Electroencephalography. Magnetic Resonance
Imaging. Functional Magnetic Resonance Imaging. Positron Emission
Tomography. Computerized Axial Tomography. APPENDIX D BIOGRAPHIES OF
PERSONS OF SCIENTIFIC INTEREST. FOR FURTHER STUDY. INDEX.