LEADER 05382nam 2200637Ia 450 001 9910830294803321 005 20170814180836.0 010 $a1-282-27902-5 010 $a9786612279027 010 $a0-470-46425-9 010 $a0-470-46419-4 035 $a(CKB)1000000000790159 035 $a(EBL)456054 035 $a(SSID)ssj0000354332 035 $a(PQKBManifestationID)11264312 035 $a(PQKBTitleCode)TC0000354332 035 $a(PQKBWorkID)10314131 035 $a(PQKB)10316299 035 $a(MiAaPQ)EBC456054 035 $a(OCoLC)441875023 035 $a(CaSebORM)9780470424353 035 $a(EXLCZ)991000000000790159 100 $a20081106d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHuman memory modeled with standard analog and digital circuits$b[electronic resource] $einspiration for man-made computers /$fJohn Robert Burger 205 $a1st edition 210 $aHoboken $cWiley$dc2009 215 $a1 online resource (384 p.) 300 $aDescription based upon print version of record. 311 $a0-470-42435-4 320 $aIncludes bibliographical references and index. 327 $aHUMAN MEMORY MODELED WITH STANDARD ANALOG AND DIGITAL CIRCUITS; CONTENTS; PREFACE; 1 BRAIN BEHAVIOR POINTS THE WAY; Introduction; Modeling; 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 327 $aDerivation of a Neural Pulse Using Basic PhysicsNeuron 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; Enabled Neural Logic; Models for Memorization; Applications; Conclusions; Exercises; 5 DENDRITIC PROCESSING AND HUMAN LEARNING; Introduction 327 $aBiological Versus Artificial Neural NetworksDendrites; 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 327 $aIntroductionSavants; 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 327 $aReversible Subtraction Programming ExampleConclusions; 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 327 $aForebrain Structure 330 $aGain 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 me 606 $aMemory$xComputer simulation 606 $aArtificial intelligence 615 0$aMemory$xComputer simulation. 615 0$aArtificial intelligence. 676 $a612.8/23312 676 $a612.823312 700 $aBurger$b John Robert$f1940-$01613918 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830294803321 996 $aHuman memory modeled with standard analog and digital circuits$93943469 997 $aUNINA