Brainware : Bio-Inspired Architecture and Its Hardware Implementation

✍ Scribed by Tsutomu Miki

• Publisher: World Scientific Publishing Company
• Year: 2001
• Tongue: English
• Leaves: 245
• Category: Library

✦ Synopsis

Discusses a 'bio-inspired' implementation of human-brain computer architecture called brainware, through 8 papers from an IIZUKA conference held in 1998. Serves as a reference for graduate students and inspiration for further research on the topic.

✦ Table of Contents

Series Editor's Preface......Page 6
Volume Editor's Preface......Page 8
Contents......Page 10
1.1 Introduction......Page 12
1.2 Bio Processing vs. Electronic Processing......Page 13
1.3 Neuron MOS and Elemental Logic Gate......Page 15
1.4 Application to Binary Logic Gate......Page 17
1.5 Example of Hardware Algorithm: Center of Mass Circuit......Page 20
1.6 Right-Brain Computing Model and Association Processor......Page 22
1.7 Applications of Association Processor Architecture......Page 29
1.8 Conclusions......Page 38
References......Page 40
2.1 Introduction......Page 44
2.2 Operation Principles of Adaptive-Learning Neuron Circuits......Page 46
2.3 Neuron Integrated Circuits Composed of MFSFETs and CUJT Oscillation Circuits......Page 50
2.4 Neuron Circuit Using CMOS Schmitt-Trigger Oscillator......Page 60
2.5 Conclusions......Page 64
References......Page 67
3.1 Introduction......Page 72
3.2 A New VLSI Implementation Approach Using PWM Signals......Page 74
3.3 A Neural Circuit Using PWM Signals......Page 77
3.4 Arbitrary Nonlinear Transformation Using PWM Signals......Page 86
3.5 Conclusion......Page 93
References......Page 96
4.1 Introduction......Page 100
4.2 Methodology for application-specific design of vision circuits and systems......Page 102
4.3 Design examples of integrated low-power vision systems......Page 111
4.4 Conclusions and Future Work......Page 127
References......Page 129
5.1 Introduction......Page 134
5.2 Correlation Neural Networks for the Motion Detection......Page 135
5.3 Velocity Sensing Circuits and Networks for the Correlation Model......Page 136
5.4 Simulation Results......Page 138
5.5 VSC Networks and Computational Algorithm for Optical Flows......Page 140
5.6 Summary and Discussion......Page 141
5.7 Acknowledgement......Page 142
References......Page 144
6.1 Introduction......Page 146
6.2 The Cellular Automaton for Morphological Picture Processing......Page 148
6.3 Construction of Cell Circuits using vMOS FETs......Page 153
6.5 Image Thinning and Shrinking by a Cellular Automaton......Page 156
6.6 Hardware Embodiment of the Cellular Automaton for Thinning and Shrinking......Page 161
6.7 Simulation of Operational Characteristics of a vMOS Cellular Automaton......Page 162
6.8 Constructing a vMOS Circuit that has Low Power Dissipation......Page 166
6.9 Conclusions......Page 171
Appendix A Simulation Analyses of Low Power vMOS circuits......Page 172
References......Page 173
7.1 Introduction......Page 174
7.2 Capacitor-Transistor Pair......Page 175
7.3 Return-Map Unit......Page 176
7.4 CMOS Chaos Multivibrator......Page 179
7.4 Chaos Generation from the Pipelined A-D Converter......Page 184
7.6 Conclusions......Page 187
References......Page 188
8.1 Introduction......Page 190
8.2 Computational Consequence of Passive Dendrites
......Page 192
8.3 Functional Significance of Active Dendrites......Page 197
8.4 Discussion and Conclusion......Page 205
Appendix A Compartmental Model of Pyramidal Neuron......Page 208
References......Page 213
About the Authors......Page 218
Keyword Index......Page 240