Human Memory Modeled with Standard Analog and Digital Circuits
by
John Robert Burger
Introduces 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.
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Complete 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; and, 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.
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General info
Publisher & Imprint:
Wiley-Blackwell (an imprint of John Wiley & Sons Ltd)
City:
Chicester
Pages:
366
More info:
height 239 mm
width 164 mm
weight 644 gr
thickness 23 mm
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Age recommended:
Professional and scholarly
Subject Indexing & Classification
Dewey: 612.823312
Library of Congress Subject: Artificial intelligence
Summary
Human Memory Modeled with Standard Analog and Digital Circuits
Preface. Chapter 1: Brain Behavior Points the Way. Introduction. Introduction to modeling. Modeling goals of the past. Uses of models. Why does thinking dissipate so few calories? The miracle of parallel processing. The singularity. How does this book benefit a reader personally? Overview of the chapters in this book. Applications of the models in this book. Artificial Membranes. Imitation Neurons. Artificial Neural Networks. Computer Design. Robotics. Artificial Intelligence. Neuroscience. The makings of cognitive architecture. General Education. Conclusions. Exercises. Chapter 2: Neural Membranes and Animal Electricity. Introduction. Introducing the physical neuron. Neural membranes. Ionic solutions and stray electrons. Nernst voltages. The neuron as pulse generator. Ion channel model. Ion channels as energetic particles hitting ferroelectric membranes. Applications. Conclusions. Exercises. Chapter 3: Neural Pulses and Neural Memory. Introduction. Telegraphist's equations. Derivation of a neural pulse using basic physics. Charge transfer analysis. Sodium electrical current. Potassium electrical current. Resting voltage. Continuity equation. Neuron signal propagation. Active axon analysis. Modeling neurons as adiabatic. Introduction to neurons for memory. Introduction to short term memory. Energy dissipation because of short term memory. Introduction to long term memory. Introduction to memorization. Energy dissipation in long term memory. Applications. Conclusions. Exercises. Asymptotically adiabatic circuits. Chapter 4: Circuits and systems for memorizing and recalling. Introduction. Psychological considerations when modeling human memory. Basic assumptions to create a model. Emotions are just another feature. Short term memory and consciousness. What will you think of next? . Memory searches. Cognitive architecture. Cognitive architecture including subliminal analysis. Cue Editor. Subliminal analyzer. Optional technicalities. Sensory inputs. Short term memory. Long term memory word. Recognition. Enabled neural logic. Recall Circuits. Memory cell. Memory standard cells. Readout details. Multi read circuit. Basic memory search. Pseudorandom memory search example. Richard Semon. Models for memorizing. Memorization enable. Circuit model for memorizing new memories. Multi write circuit. Calories for memorizing. Applications. Robotics. Artificial Intelligence in a robot. Conclusions. Chapter 5: Dendritic processing and human learning. Introduction. Basic information about dendrites. Learning Circuits. Dendritic processing models. Enabled logic directly at the soma. Comments on the adiabatic nature of dendrites. Applications. Conclusions. Exercises. Chapter 5 Attachment 1. Circuit Simulations of Neural Soliton Propagation. Simulations. Logic generation in dendrites. AND function. Tapered circuits. Conclusions. Chapter 6: Artificial learning in artificial neural networks. Introduction. Artificial neurons. Artificial learning methods. Single layer networks. Multilayer networks. Discussion of learning methods. Conclusion. Exercises. Chapter 7: The asset of reversibility in man and machine. Introduction. Neural models to explain savants. Instant neural state machine learning. Massively parallel processing. Parallel processing and the savant brain. Computational possibilities using conditional toggle memory. Types of reversibility. The cost of computation. Comments on information loss. Short term memory. Long term memory. Conclusions. Exercises. Attachment 7-1. Split-level charge recovery logic (SCRL) . Chapter 8: Electrically reversible nanoprocessor. Introduction. A gage for classical parallelism. Design rules for electrical reversibility. Reversible system architecture. Architecture for self-analyzing memory words. Electrically reversible toggle circuit. Comments on power supplies for C1, C2 and C3. Reversible Addition Programming Example. Reversible Subtraction Programming Example. Conclusions. Exercises. Chapter 9: Multiplications, divisions and Hamiltonian circuits. Introduction. Unsigned Multiplication. Wiring diagram for reversible parallel unsigned multiplication. Restoring Division. Solving hard problems. Hamiltonian Circuits. Strategy for detecting Hamiltonian circuits. The initialization of toggle memory in nanoprocessors. Logically reversible programming using nanobrains. Conclusions. Exercises. Chapter 10: Quantum versus classical. Introduction. Mathematical description of qubits. Initialization of state vectors to have equal probabilities for each element. Qubit manipulations. Quantum Boolean functions. Entanglement. Quantum Boolean function identification. Quantum computer programming. Overview of historical quantum computing algorithms. Conclusions. Exercises. Appendix 1: Overview of human brain anatomy. Components of a brain. Forebrain structure. Appendix 2: The Psychological Science of Memory. Short term memory. Long term memory. Studies in learning. Over learning. Encoding of analog sensory information. Serial reproduction. Richard Semon. Sigmund Freud. Dreams. Appendix 3: Brain Scanning. Units. Appendix 4: Biographies of scientifically interesting individuals important to this book. FOR FURTHER STUDY. INDEX.
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