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Human Memory Modeled with Standard Analog and Digital Circuits: Inspiration for Man-made Computers

Book Description

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 ialine@wiley.com.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Dedication
  5. Contents
  6. PREFACE
  7. CHAPTER 1: BRAIN BEHAVIOR POINTS THE WAY
    1. INTRODUCTION
    2. MODELING
    3. WHY THINKING DISSIPATES SO FEW CALORIES
    4. THE MIRACLE OF PARALLEL PROCESSING
    5. SINGULARITY
    6. THE BENEFITS OF READING THIS BOOK
    7. OVERVIEW OF THE BOOK
    8. APPLICATIONS OF THE MODELS IN THE BOOK
    9. CONCLUSIONS
    10. EXERCISES
  8. CHAPTER 2: NEURAL MEMBRANES AND ANIMAL ELECTRICITY
    1. INTRODUCTION
    2. THE PHYSICAL NEURON
    3. IONIC SOLUTIONS AND STRAY ELECTRONS
    4. NERNST VOLTAGE
    5. ION-CHANNEL MODEL
    6. APPLICATIONS
    7. CONCLUSIONS
    8. EXERCISES
  9. CHAPTER 3: NEURAL PULSES AND NEURAL MEMORY
    1. INTRODUCTION
    2. DERIVATION OF A NEURAL PULSE USING BASIC PHYSICS
    3. NEURON SIGNAL PROPAGATION
    4. MODELING NEURONS AS ADIABATIC
    5. NEURONS FOR MEMORY
    6. APPLICATIONS
    7. CONCLUSIONS
    8. EXERCISES
    9. APPENDIX: ASYMPTOTICALLY ADIABATIC CIRCUITS
  10. CHAPTER 4: CIRCUITS AND SYSTEMS FOR MEMORIZATION AND RECALL
    1. INTRODUCTION
    2. PSYCHOLOGICAL CONSIDERATIONS WHEN MODELING HUMAN MEMORY
    3. BASIC ASSUMPTIONS TO CREATE A MODEL
    4. SHORT-TERM MEMORY AND CONSCIOUSNESS
    5. COGNITIVE ARCHITECTURE
    6. DISCUSSION OF THE MODEL
    7. ENABLED NEURAL LOGIC
    8. MODELS FOR MEMORIZATION
    9. APPLICATIONS
    10. CONCLUSIONS
    11. EXERCISES
  11. CHAPTER 5: DENDRITIC PROCESSING AND HUMAN LEARNING
    1. INTRODUCTION
    2. BIOLOGICAL VERSUS ARTIFICIAL NEURAL NETWORKS
    3. DENDRITES
    4. NEURONS FOR COMBINATIONAL LEARNING
    5. NEURONS FOR STATE-MACHINE LEARNING
    6. LEARNING CIRCUITS
    7. DENDRITIC PROCESSING MODELS
    8. ENABLED LOGIC DIRECTLY AT THE SOMA
    9. COMMENTS ON THE ADIABATIC NATURE OF DENDRITES
    10. APPLICATIONS
    11. CONCLUSIONS
    12. EXERCISES
    13. APPENDIX: CIRCUIT SIMULATIONS OF NEURAL SOLITON PROPAGATION
    14. CONCLUSIONS
  12. CHAPTER 6: ARTIFICIAL LEARNING IN ARTIFICIAL NEURAL NETWORKS
    1. INTRODUCTION
    2. ARTIFICIAL NEURONS
    3. ARTIFICIAL LEARNING METHODS
    4. DISCUSSION OF LEARNING METHODS
    5. CONCLUSION
    6. EXERCISES
  13. CHAPTER 7: THE ASSET OF REVERSIBILITY IN HUMANS AND MACHINES
    1. INTRODUCTION
    2. SAVANTS
    3. NEURAL MODELS THAT EXPLAIN SAVANTS
    4. PARALLEL PROCESSING AND THE SAVANT BRAIN
    5. COMPUTATIONAL POSSIBILITIES USING CONDITIONAL TOGGLE MEMORY
    6. THE COST OF COMPUTATION
    7. REVERSIBLE PROGRAMMING
    8. CONCLUSIONS
    9. EXERCISES
    10. APPENDIX: SPLIT-LEVEL CHARGE RECOVERY LOGIC
  14. CHAPTER 8: ELECTRICALLY REVERSIBLE NANOPROCESSORS
    1. INTRODUCTION
    2. A GAUGE FOR CLASSICAL PARALLELISM
    3. DESIGN RULES FOR ELECTRICAL REVERSIBILITY
    4. REVERSIBLE SYSTEM ARCHITECTURE
    5. ARCHITECTURE FOR SELF-ANALYZING MEMORY WORDS
    6. ELECTRICALLY REVERSIBLE TOGGLE CIRCUIT
    7. REVERSIBLE ADDITION PROGRAMMING EXAMPLE
    8. REVERSIBLE SUBTRACTION PROGRAMMING EXAMPLE
    9. CONCLUSIONS
    10. EXERCISES
  15. CHAPTER 9: MULTIPLICATION, DIVISION, AND HAMILTONIAN CIRCUITS
    1. INTRODUCTION
    2. UNSIGNED MULTIPLICATION
    3. RESTORING DIVISION
    4. SOLVING HARD PROBLEMS
    5. HAMILTONIAN CIRCUITS
    6. THE INITIALIZATION OF TOGGLE MEMORY IN NANOPROCESSORS
    7. LOGICALLY REVERSIBLE PROGRAMMING USING NANOBRAINS
    8. CONCLUSIONS
    9. EXERCISES
  16. CHAPTER 10: QUANTUM VERSUS CLASSICAL COMPUTING
    1. INTRODUCTION
    2. PHYSICAL QUBITS
    3. QUANTUM BOOLEAN FUNCTIONS
    4. QUANTUM COMPUTER PROGRAMMING
    5. HISTORICAL QUANTUM COMPUTING ALGORITHMS
    6. CONCLUSIONS
    7. EXERCISES
  17. APPENDIX A: HUMAN BRAIN ANATOMY
    1. COMPONENTS OF A BRAIN
    2. FOREBRAIN STRUCTURE
  18. APPENDIX B: THE PSYCHOLOGICAL SCIENCE OF MEMORY
    1. SHORT-TERM MEMORY
    2. LONG-TERM MEMORY
    3. STUDIES IN LEARNING
    4. MEMORY RETRIEVAL
    5. SERIAL REPRODUCTION
    6. MEMORY THEORISTS
    7. DREAMS
  19. APPENDIX C: BRAIN SCANNING
    1. ELECTROENCEPHALOGRAPHY
    2. MAGNETIC RESONANCE IMAGING
    3. FUNCTIONAL MAGNETIC RESONANCE IMAGING
    4. POSITRON EMISSION TOMOGRAPHY
    5. COMPUTERIZED AXIAL TOMOGRAPHY
  20. APPENDIX D: BIOGRAPHIES OF PERSONS OF SCIENTIFIC INTEREST
    1. Why Read the Biographies of Others?
    2. René Descartes (1596–1650)
    3. Thomas Willis (1621–1675)
    4. Charles-Augustin de Coulomb (1736–1806)
    5. Luigi Galvani (1737–1798)
    6. Benjamin Rush (1746–1813)
    7. Robert Brown (1773–1858)
    8. André-Marie Ampère (1775–1836)
    9. Lorenzo Avogadro (1776–1856)
    10. Jan Evangelista Purkinje (1787–1869)
    11. Michael Faraday (1791–1867)
    12. William Rowan Hamilton (1805–1865)
    13. George Boole (1815–1864)
    14. Louis-Antoine Ranvier (1835–1922)
    15. Ludwig Boltzmann (1844–1906)
    16. Santiago Ramón y Cajal (1852–1934)
    17. Sigmund Freud (1856–1939)
    18. Svante August Arrhenius (1859–1927)
    19. Richard Semon (1859–1918)
    20. Walther Nernst (1864–1941)
    21. John Von Neumann (1903–1957)
    22. Donald Olding Hebb (1904–1985)
    23. Alan Turing (1912–1954)
    24. Herbert Alexander Simon (1916–2001)
    25. Richard Feynman (1918–1988)
    26. Rolf Landauer (1927–1999)
    27. Frank Rosenblatt (1928–1969)
    28. Gordon Earle Moore (1929– )
    29. John Joseph Hopfield (1933– )
    30. John R. Anderson (1947– )
    31. Kim Peek (1951– )
    32. Daniel Tammet (1979– )
  21. For Further Study
    1. General Science
    2. Perspectives in Circuits and Systems
    3. Textbooks
    4. Research Compendiums
  22. Index