Human Memory Modeled with Standard Analog and Digital Circuits
Inspiration for Man-made Computers
(Sprache: Englisch)
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...
This unique book is the first of its kind to introduce human memory and basic cognition in terms of physical circuits, beginning...
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Klappentext zu „Human Memory Modeled with Standard Analog and Digital Circuits “
Gain a new perspective on how the brain works and inspires new avenues for design in computer science and engineeringThis 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
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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.
A solutions manual is available for instructors; to obtain a copy please email the editorial department at ialine@wiley.com.
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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.
Inhaltsverzeichnis zu „Human Memory Modeled with Standard Analog and Digital Circuits “
PrefaceChapter 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
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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
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
... weniger
Autoren-Porträt von John Robert Burger
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.
Bibliographische Angaben
- Autor: John Robert Burger
- 2009, 1. Auflage, 384 Seiten, Maße: 16,1 x 24 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0470424354
- ISBN-13: 9780470424353
- Erscheinungsdatum: 13.07.2009
Sprache:
Englisch
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