Advances in Neuromorphic Memristor Science and Applications

ISBN-13: 9789400744905 / Angielski / Twarda / 2012 / 320 str.

Robert Kozma; Robinson E. Pino; Giovanni E. Pazienza

Opis

Physical implementation of the memristor at industrial scale sparked the interest from various disciplines, ranging from physics, nanotechnology, electrical engineering, neuroscience, to intelligent robotics. As any promising new technology, it has raised hopes and questions; it is an extremely challenging task to live up to the high expectations and to devise revolutionary and feasible future applications for memristive devices. The possibility of gathering prominent scientists in the heart of the Silicon Valley given by the 2011 International Joint Conference on Neural Networks held in San Jose, CA, has offered us the unique opportunity of organizing a series of special events on the present status and future perspectives in neuromorphic memristor science. This book presents a selection of the remarkable contributions given by the leaders of the field and it may serve as inspiration and future reference to all researchers that want to explore the extraordinary possibilities given by this revolutionary concept.

Spis treści

Foreword

Part I  Fundamental Concepts of Memristors and Neuromorphic Systems  

Prolog – Memristor Minds; Greg Snider

1. Are Memristors the Future of AI? – A Review of Recent Progress and Future Perspectives; Robert Kozma, Robinson E. Pino, and Giovanni E. Pazienza  
2. Biologically-Inspired Electronics with Memory Circuit Elements; Massimiliano Di Ventra and Yuriy Pershin 
3. Persuading Computers to Act More Like Brains; Heather Ames, Massimiliano Versace, Anatoli Gorchetchnikov, Benjamin Chandler, Gennady Livitz, Jasmin Léveillé, Ennio Mingolla, Dick Carter, Hisham Abdalla, and Greg Snider 
4. Memristors for More Than Just Memory: How to Use Learning to Expand Applications; Paul J. Werbos 

Part II  Computational Models of Memristors

1. Computational Intelligence and Neuromorphic Computing Architectures; Robinson E. Pino 
2. Reconfigurable Memristor Fabrics for Heterogeneous Computing; Dhireesha Kudithipudi and Cory E. Merkel  
3. Statistical Memristor Model and Its Applications in Neuromorphic Computing; Hai (Helen) Li and Pino E. Robinson 
4. Adaptive Resonance Theory Design in Mixed Memristive-Fuzzy Hardware; Max Versace, Robert T. Kozma, and Donald C. Wunsch 
5. Memristor SPICE Modeling; Chris Yakopcic, Tarek M. Taha, Guru Subramanyam, and Robinson E. Pino  

Part III. Hardware Embodiments with Memristive Properties and Applications

1. Energy-Efficient Memristive Analog and Digital Electronics; Sung Mo “Steve” Kang and Sangho Shin 
2. Memristor Models for Pattern Recognition Systems; Fernando Corinto, A. Ascoli, and Marco Gilli 
3. Phase Change Memory and Chalcogenide Materials Neuromorphic Applications: Emphasis on Synaptic Plasticity; Manan Suri, and Barbara DeSalvo
4. A Columnar V1/V2 Visual Cortex Model and Emulation; Robinson E. Pino and Michael Moore 
5.   Polymer and Nanoparticle-Composite Bistable Devices: Physics of Operation and Initial Applications
   Robert A. Nawrocki, Richard M. Voyles, and Sean E. Shaeen 

Index

A Columnar V1/V2 Visual Cortex Model and Emulation; Robinson E. Pino and Michael Moore 
6.   Polymer and Nanoparticle-Composite Bistable Devices: Physics of Operation and Initial Applications
   Robert A. Nawrocki, Richard M. Voyles, and Sean E. Shaeen 

Index

Prolog – Memristor Minds; Greg Snider

1. Are Memristors the Future of AI? – A Review of Recent Progress and Future Perspectives; Robert Kozma, Robinson E. Pino, and Giovanni E. Pazienza  
2. Biologically-Inspired Electronics with Memory Circuit Elements; Massimiliano Di Ventra and Yuriy Pershin 
3. Persuading Computers to Act More Like Brains; Heather Ames, Massimiliano Versace, Anatoli Gorchetchnikov, Benjamin Chandler, Gennady Livitz, Jasmin Léveillé, Ennio Mingolla, Dick Carter, Hisham Abdalla, and Greg Snider 
4. Memristors for More Than Just Memory: How to Use Learning to Expand Applications; Paul J. Werbos 

Part II  Computational Models of Memristors

1. Computational Intelligence and Neuromorphic Computing Architectures; Robinson E. Pino 
2. Reconfigurable Memristor Fabrics for Heterogeneous Computing; Dhireesha Kudithipudi and Cory E. Merkel  
3. Statistical Memristor Model and Its Applications in Neuromorphic Computing; Hai (Helen) Li and Pino E. Robinson 
4. Adaptive Resonance Theory Design in Mixed Memristive-Fuzzy Hardware; Max Versace, Robert T. Kozma, and Donald C. Wunsch 
5. Memristor SPICE Modeling; Chris Yakopcic, Tarek M. Taha, Guru Subramanyam, and Robinson E. Pino  

Part III. Hardware Embodiments with Memristive Properties and Applications

1. Energy-Efficient Memristive Analog and Digital Electronics; Sung Mo “Steve” Kang and Sangho Shin 
2. Memristor Models for Pattern Recognition Systems; Fernando Corinto, A. Ascoli, and Marco Gilli 
3. Phase Change Memory and Chalcogenide Materials Neuromorphic Applications: Emphasis on Synaptic Plasticity; Manan Suri, and Barbara DeSalvo
4. A Columnar V1/V2 Visual Cortex Model and Emulation; Robinson E. Pino and Michael Moore 
5.   Polymer and Nanoparticle-Composite Bistable Devices: Physics of Operation and Initial Applications
   Robert A. Nawrocki, Richard M. Voyles, and Sean E. Shaeen 

Index

A Columnar V1/V2 Visual Cortex Model and Emulation; Robinson E. Pino and Michael Moore 
6.   Polymer and Nanoparticle-Composite Bistable Devices: Physics of Operation and Initial Applications
   Robert A. Nawrocki, Richard M. Voyles, and Sean E. Shaeen 

Index

Part I  Fundamental Concepts of Memristors and Neuromorphic Systems  

Prolog – Memristor Minds; Greg Snider

1. Are Memristors the Future of AI? – A Review of Recent Progress and Future Perspectives; Robert Kozma, Robinson E. Pino, and Giovanni E. Pazienza  
2. Biologically-Inspired Electronics with Memory Circuit Elements; Massimiliano Di Ventra and Yuriy Pershin 
3. Persuading Computers to Act More Like Brains; Heather Ames, Massimiliano Versace, Anatoli Gorchetchnikov, Benjamin Chandler, Gennady Livitz, Jasmin Léveillé, Ennio Mingolla, Dick Carter, Hisham Abdalla, and Greg Snider 
4. Memristors for More Than Just Memory: How to Use Learning to Expand Applications; Paul J. Werbos 

Part II  Computational Models of Memristors

1. Computational Intelligence and Neuromorphic Computing Architectures; Robinson E. Pino 
2. Reconfigurable Memristor Fabrics for Heterogeneous Computing; Dhireesha Kudithipudi and Cory E. Merkel  
3. Statistical Memristor Model and Its Applications in Neuromorphic Computing; Hai (Helen) Li and Pino E. Robinson 
4. Adaptive Resonance Theory Design in Mixed Memristive-Fuzzy Hardware; Max Versace, Robert T. Kozma, and Donald C. Wunsch 
5. Memristor SPICE Modeling; Chris Yakopcic, Tarek M. Taha, Guru Subramanyam, and Robinson E. Pino  

Part III. Hardware Embodiments with Memristive Properties and Applications

1. Energy-Efficient Memristive Analog and Digital Electronics; Sung Mo “Steve” Kang and Sangho Shin 
2. Memristor Models for Pattern Recognition Systems; Fernando Corinto, A. Ascoli, and Marco Gilli 
3. Phase Change Memory and Chalcogenide Materials Neuromorphic Applications: Emphasis on Synaptic Plasticity; Manan Suri, and Barbara DeSalvo
4. A Columnar V1/V2 Visual Cortex Model and Emulation; Robinson E. Pino and Michael Moore 
5.   Polymer and Nanoparticle-Composite Bistable Devices: Physics of Operation and Initial Applications
   Robert A. Nawrocki, Richard M. Voyles, and Sean E. Shaeen 

Index

A Columnar V1/V2 Visual Cortex Model and Emulation; Robinson E. Pino and Michael Moore 
6.   Polymer and Nanoparticle-Composite Bistable Devices: Physics of Operation and Initial Applications
   Robert A. Nawrocki, Richard M. Voyles, and Sean E. Shaeen 

Index

Prolog – Memristor Minds; Greg Snider

1. Are Memristors the Future of AI? – A Review of Recent Progress and Future Perspectives; Robert Kozma, Robinson E. Pino, and Giovanni E. Pazienza  
2. Biologically-Inspired Electronics with Memory Circuit Elements; Massimiliano Di Ventra and Yuriy Pershin 
3. Persuading Computers to Act More Like Brains; Heather Ames, Massimiliano Versace, Anatoli Gorchetchnikov, Benjamin Chandler, Gennady Livitz, Jasmin Léveillé, Ennio Mingolla, Dick Carter, Hisham Abdalla, and Greg Snider 
4. Memristors for More Than Just Memory: How to Use Learning to Expand Applications; Paul J. Werbos 

Part II  Computational Models of Memristors

1. Computational Intelligence and Neuromorphic Computing Architectures; Robinson E. Pino 
2. Reconfigurable Memristor Fabrics for Heterogeneous Computing; Dhireesha Kudithipudi and Cory E. Merkel  
3. Statistical Memristor Model and Its Applications in Neuromorphic Computing; Hai (Helen) Li and Pino E. Robinson 
4. Adaptive Resonance Theory Design in Mixed Memristive-Fuzzy Hardware; Max Versace, Robert T. Kozma, and Donald C. Wunsch 
5. Memristor SPICE Modeling; Chris Yakopcic, Tarek M. Taha, Guru Subramanyam, and Robinson E. Pino  

Part III. Hardware Embodiments with Memristive Properties and Applications

1. Energy-Efficient Memristive Analog and Digital Electronics; Sung Mo “Steve” Kang and Sangho Shin 
2. Memristor Models for Pattern Recognition Systems; Fernando Corinto, A. Ascoli, and Marco Gilli 
3. Phase Change Memory and Chalcogenide Materials Neuromorphic Applications: Emphasis on Synaptic Plasticity; Manan Suri, and Barbara DeSalvo
4. A Columnar V1/V2 Visual Cortex Model and Emulation; Robinson E. Pino and Michael Moore 
5.   Polymer and Nanoparticle-Composite Bistable Devices: Physics of Operation and Initial Applications
   Robert A. Nawrocki, Richard M. Voyles, and Sean E. Shaeen 

Index

A Columnar V1/V2 Visual Cortex Model and Emulation; Robinson E. Pino and Michael Moore 
6.   Polymer and Nanoparticle-Composite Bistable Devices: Physics of Operation and Initial Applications
   Robert A. Nawrocki, Richard M. Voyles, and Sean E. Shaeen 

Blurb

Posited by Professor Leon Chua at UC Berkeley more than 40 years ago, memristors, a nonlinear element in electrical circuitry, are set to revolutionize computing technology. Finally discovered by scientists at Hewlett-Packard in 2008, memristors  generate huge interest because they can facilitate nanoscale, real-time computer learning, as well as due to their potential of serving as instant memories. . This edited volume bottles some of the excitement about memristors, providing a state-of-the-art overview of neuromorphic memristor theory, as well as its technological and practical aspects.

Based on work presented to specialist memristor seminars organized by the editors, the volume takes readers from a general introduction the fundamental concepts involved, to specialized analysis of computational modeling, hardware, and applications. The latter include the ground-breaking potential of memristors in facilitating hybrid wetware-hardware technologies for in-vitro experiments. The book evinces, and devotes space to the discussion of, the socially transformative potential of memristors, which could be as pervasive as was the invention of the silicon chip: machines that learn in the style of brains, are a computational Holy Grail.

With contributions from key players in a fast-moving field, this edited volume is the first to cover memristors in the depth needed to trigger the further advances that surely lie around the corner.