Human Memory Modeled with Standard Analog and

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.

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.

Preface	p. xiii
Brain Behavior Points The Way	p. 1
Introduction	p. 1
Modeling	p. 2
Why Thinking Dissipates So Few Calories	p. 5
The Miracle of Parallel Processing	p. 7
Singularity	p. 8
The Benefits of Reading This Book	p. 10
Overview of the Book	p. 12
Applications of the Models in the Book	p. 16
Conclusions	p. 23
Exercises	p. 25
Neural Membranes And Animal Electricity	p. 27
Introduction	p. 27
The Physical Neuron	p. 28
Ionic Solutions and Stray Electrons	p. 36
Nernst Voltage	p. 39
Ion-Channel Model	p. 44
Applications	p. 54
Conclusions	p. 56
Exercises	p. 57
Neural Pulses and Neural Memory	p. 61
Introduction	p. 61
Derivation of a Neural Pulse Using Basic Physics	p. 63
Neuron Signal Propagation	p. 72
Modeling Neurons as Adiabatic	p. 77
Neurons for Memory	p. 79
Applications	p. 86
Conclusions	p. 87
Exercises	p. 88
Appendix: Asymptotically Adiabatic Circuits	p. 90
Circuits And Systems For Memorization And Recall	p. 93
Introduction	p. 93
Psychological Considerations When Modeling Human Memory	p. 94
Basic Assumptions to Create a Model	p. 96
Short-Term Memory and Consciousness	p. 98
Cognitive Architecture	p. 100
Discussion of the Model	p. 105
Enabled Neural Logic	p. 107
Models for Memorization	p. 115
Applications	p. 119
Conclusions	p. 120
Exercises	p. 121
Dendritic Processing And Human Learning	p. 125
Introduction	p. 125
Biological Versus Artificial Neural Networks	p. 127
Dendrites	p. 129
Neurons for Combinational Learning	p. 138
Neurons for State-Machine Learning	p. 140
Learning Circuits	p. 141
Dendritic Processing Models	p. 143
Enabled Logic Directly at the Soma	p. 150
Comments on the Adiabatic Nature of Dendrites	p. 153
Applications	p. 153
Conclusions	p. 154
Exercises	p. 156
Appendix: Circuit Simulations of Neural Soliton Propagation	p. 157
Conclusions	p. 167
Artificial Learning In Artificial Neural Networks	p. 169
Introduction	p. 169
Artificial Neurons	p. 170
Artificial Learning Methods	p. 175
Discussion of Learning Methods	p. 185
Conclusion	p. 188
Exercises	p. 189
The Asset of Reversibility in Humans And Machines	p. 193
Introduction	p. 193
Savants	p. 195
Neural Models that Explain Savants	p. 197
Parallel Processing and the Savant Brain	p. 199
Computational Possibilities Using Conditional Toggle Memory	p. 202
The Cost of Computation	p. 206
Reversible Programming	p. 211
Conclusions	p. 216
Exercises	p. 216
Appendix: Split-Level Charge Recovery Logic	p. 219
Electrically Reversible Nanoprocessors	p. 223
Introduction	p. 223
A Gauge for Classical Parallelism	p. 224
Design Rules for Electrical Reversibility	p. 226
Reversible System Architecture	p. 227
Architecture for Self-Analyzing Memory Words	p. 229
Electrically Reversible Toggle Circuit	p. 234
Reversible Addition Programming Example	p. 242
Reversible Subtraction Programming Example	p. 245
Conclusions	p. 245
Exercises	p. 246
Multiplication, Division, And Hamiltonian Circuits	p. 249
Introduction	p. 249
Unsigned Multiplication	p. 251
Restoring Division	p. 253
Solving Hard Problems	p. 259
Hamiltonian Circuits	p. 262
The Initialization of Toggle Memory in Nanoprocessors	p. 266
Logically Reversible Programming Using Nanobrains	p. 269
Conclusions	p. 269
Exercises	p. 270
Quantum Versus Classical Computing	p. 273
Introduction	p. 273
Physical Quoits	p. 275
Quantum Boolean Functions	p. 282
Quantum Computer Programming	p. 289
Historical Quantum Computing Algorithms	p. 294
Conclusions	p. 296
Exercises	p. 298
Human Brain Anatomy	p. 301
Components of a Brain	p. 301
Forebrain Structure	p. 305
The Psychological Science of Memory	p. 309
Short-Term Memory	p. 309
Long-Term Memory	p. 310
Studies in Learning	p. 313
Memory Retrieval	p. 318
Serial Reproduction	p. 319
Memory Theorists	p. 319
Dreams	p. 321
Brain Scanning	p. 323
Electroencephalography	p. 323
Magnetic Resonance Imaging	p. 326
Functional Magnetic Resonance Imaging	p. 328
Positron Emission Tomography	p. 329
Computerized Axial Tomography	p. 331
Biographies of Persons of Scientific Interest	p. 335
For Further Study	p. 359
Index	p. 361
Table of Contents provided by Ingram. All Rights Reserved.

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

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