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Memristive Devices for Brain-Inspired Computing

Memristive Devices for Brain-Inspired Computing
From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks

by Sabina Spiga,Abu Sebastian,Damien Querlioz,Bipin Rajendran

  • Publisher : Woodhead Publishing
  • Release : 2020-06-12
  • Pages : 564
  • ISBN : 0081027877
  • Language : En, Es, Fr & De
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Memristive Devices for Brain-Inspired Computing: From Materials, Devices, and Circuits to Applications—Computational Memory, Deep Learning, and Spiking Neural Networks reviews the latest in material and devices engineering for optimizing memristive devices beyond storage applications and toward brain-inspired computing. The book provides readers with an understanding of four key concepts, including materials and device aspects with a view of current materials systems and their remaining barriers, algorithmic aspects comprising basic concepts of neuroscience as well as various computing concepts, the circuits and architectures implementing those algorithms based on memristive technologies, and target applications, including brain-inspired computing, computational memory, and deep learning. This comprehensive book is suitable for an interdisciplinary audience, including materials scientists, physicists, electrical engineers, and computer scientists. Provides readers an overview of four key concepts in this emerging research topic including materials and device aspects, algorithmic aspects, circuits and architectures and target applications Covers a broad range of applications, including brain-inspired computing, computational memory, deep learning and spiking neural networks Includes perspectives from a wide range of disciplines, including materials science, electrical engineering and computing, providing a unique interdisciplinary look at the field

Celebrating the International Year of the Periodic Table: Beyond Mendeleev 150

Celebrating the International Year of the Periodic Table: Beyond Mendeleev 150
A Book

by Mikhail V. Kurushkin,W. H. Eugen Schwarz,Eugene A. Goodilin

  • Publisher : Frontiers Media SA
  • Release : 2021-01-11
  • Pages : 129
  • ISBN : 2889663191
  • Language : En, Es, Fr & De
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Memristors for Neuromorphic Circuits and Artificial Intelligence Applications

Memristors for Neuromorphic Circuits and Artificial Intelligence Applications
A Book

by Jordi Suñé

  • Publisher : MDPI
  • Release : 2020-04-09
  • Pages : 244
  • ISBN : 3039285769
  • Language : En, Es, Fr & De
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Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.

Organic Flexible Electronics

Organic Flexible Electronics
Fundamentals, Devices, and Applications

by Piero Cosseddu,Mario Caironi

  • Publisher : Woodhead Publishing
  • Release : 2020-10-07
  • Pages : 664
  • ISBN : 012818891X
  • Language : En, Es, Fr & De
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Organic Electronics is a novel field of electronics that has gained an incredible attention over the past few decades. New materials, device architectures and applications have been continuously introduced by the academic and also industrial communities, and novel topics have raised strong interest in such communities, as molecular doping, thermoelectrics, bioelectronics and many others. Organic Flexible Electronics is mainly divided into three sections. The first part is focused on the fundamentals of organic electronics, such as charge transport models in these systems and new approaches for the design and synthesis of novel molecules. The first section addresses the main challenges that are still open in this field, including the important role of interfaces for achieving high-performing devices or the novel approaches employed for improving reliability issues. The second part discusses the most innovative devices which have been developed in recent years, such as devices for energy harvesting, flexible batteries, high frequency circuits, and flexible devices for tattoo electronics and bioelectronics. Finally the book reviews the most important applications moving from more standard flexible back panels to wearable and textile electronics and more futuristic applications like ingestible systems. Reviews the fundamental properties and methods for optimizing organic electronic materials including chemical doping and techniques to address stability issues; Discusses the most promising organic electronic devices for energy, electronics, and biomedical applications; Addresses key applications of organic electronic devices in imagers, wearable electronics, bioelectronics.

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications
A Book

by Christos Volos,Viet-Thanh Pham

  • Publisher : Academic Press
  • Release : 2021-07-01
  • Pages : 568
  • ISBN : 0128232021
  • Language : En, Es, Fr & De
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Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and in neuromorphic circuits and artificial intelligence. The book is mainly devoted to recent results, critical aspects and perspectives of ongoing research on relevant topics, all involving networks of mem-elements devices in diverse applications. Sections contribute to the discussion of memristive materials and transport mechanisms, presenting various types of physical structures that can be fabricated to realize mem-elements in integrated circuits and device modeling. As the last decade has seen an increasing interest in recent advances in mem-elements and their applications in neuromorphic circuits and artificial intelligence, this book will attract researchers in various fields. Covers a broad range of interdisciplinary topics between mathematics, circuits, realizations, and practical applications related to nonlinear dynamical systems, nanotechnology, analog and digital systems, computer science and artificial intelligence Presents recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) Includes interesting applications of mem-elements in nonlinear dynamical systems, analog and digital systems, neuromorphic circuits, computer science and artificial intelligence

Handbook of Graphene

Handbook of Graphene
Biomaterials

by Sulaiman Wadi Harun

  • Publisher : John Wiley & Sons
  • Release : 2019-06-14
  • Pages : 344
  • ISBN : 1119469791
  • Language : En, Es, Fr & De
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The seventh volume in a series of handbooks on graphene research and applications The Handbook of Graphene, Volume 7: Biomaterials presents an overview of various graphene research initiatives and specific biomedical applications, where the properties of graphene are used differently. The book shares information on how graphene and graphene-based materials are utilized for the following types of applications: bio-targeting; medical and biomedical; drug delivery; antibacterial; and biological, biosensing and bioimaging. Topics covered include the role of graphene-based materials in: regenerative medicine; resistive memories and transistors; and implants in biomedicine. The impact of graphene-based biomaterials on biomedical applications is discussed, as are graphene-based systems in the delivery of therapeutics to the brain and central nervous system.

Real-Time Modelling and Processing for Communication Systems

Real-Time Modelling and Processing for Communication Systems
Applications and Practices

by Muhammad Alam,Wael Dghais,Yuanfang Chen

  • Publisher : Springer
  • Release : 2017-12-27
  • Pages : 282
  • ISBN : 3319722158
  • Language : En, Es, Fr & De
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This book presents cutting-edge work on real-time modelling and processing, a highly active research field in both the research and industrial domains. Going beyond conventional real-time systems, major efforts are required to develop accurate and computational efficient real-time modelling algorithms and design automation tools that reflect the technological advances in high-speed and ultra-low-power transceiver communication architectures based on nanoscale devices. The book addresses basic and more advanced topics, such as I/O buffer circuits for ensuring reliable chip-to-chip communication, I/O buffer behavioural modelling, multiport empirical models for memory interfaces, compact behavioural modelling for memristive devices, and resource reservation modelling for distributed embedded systems. The respective chapters detail new research findings, new models, algorithms, implementations and simulations of the above-mentioned topics. As such, the book will help both graduate students and researchers understand the latest research into real-time modelling and processing.

Resistive Switching: Oxide Materials, Mechanisms, Devices and Operations

Resistive Switching: Oxide Materials, Mechanisms, Devices and Operations
A Book

by Jennifer Rupp

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 129
  • ISBN : 3030424243
  • Language : En, Es, Fr & De
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Handbook of Memristor Networks

Handbook of Memristor Networks
A Book

by Leon Chua,Georgios Ch. Sirakoulis,Andrew Adamatzky

  • Publisher : Springer Nature
  • Release : 2019-11-12
  • Pages : 1368
  • ISBN : 331976375X
  • Language : En, Es, Fr & De
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This Handbook presents all aspects of memristor networks in an easy to read and tutorial style. Including many colour illustrations, it covers the foundations of memristor theory and applications, the technology of memristive devices, revised models of the Hodgkin-Huxley Equations and ion channels, neuromorphic architectures, and analyses of the dynamic behaviour of memristive networks. It also shows how to realise computing devices, non-von Neumann architectures and provides future building blocks for deep learning hardware. With contributions from leaders in computer science, mathematics, electronics, physics, material science and engineering, the book offers an indispensable source of information and an inspiring reference text for future generations of computer scientists, mathematicians, physicists, material scientists and engineers working in this dynamic field.

Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices

Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices
A Book

by Manan Suri

  • Publisher : Springer
  • Release : 2017-01-21
  • Pages : 210
  • ISBN : 813223703X
  • Language : En, Es, Fr & De
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This book covers all major aspects of cutting-edge research in the field of neuromorphic hardware engineering involving emerging nanoscale devices. Special emphasis is given to leading works in hybrid low-power CMOS-Nanodevice design. The book offers readers a bidirectional (top-down and bottom-up) perspective on designing efficient bio-inspired hardware. At the nanodevice level, it focuses on various flavors of emerging resistive memory (RRAM) technology. At the algorithm level, it addresses optimized implementations of supervised and stochastic learning paradigms such as: spike-time-dependent plasticity (STDP), long-term potentiation (LTP), long-term depression (LTD), extreme learning machines (ELM) and early adoptions of restricted Boltzmann machines (RBM) to name a few. The contributions discuss system-level power/energy/parasitic trade-offs, and complex real-world applications. The book is suited for both advanced researchers and students interested in the field.

Nanoelectronic Materials, Devices and Modeling

Nanoelectronic Materials, Devices and Modeling
A Book

by Qiliang Li,Hao Zhu

  • Publisher : MDPI
  • Release : 2019-07-15
  • Pages : 242
  • ISBN : 3039212257
  • Language : En, Es, Fr & De
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As CMOS scaling is approaching the fundamental physical limits, a wide range of new nanoelectronic materials and devices have been proposed and explored to extend and/or replace the current electronic devices and circuits so as to maintain progress with respect to speed and integration density. The major limitations, including low carrier mobility, degraded subthreshold slope, and heat dissipation, have become more challenging to address as the size of silicon-based metal oxide semiconductor field effect transistors (MOSFETs) has decreased to nanometers, while device integration density has increased. This book aims to present technical approaches that address the need for new nanoelectronic materials and devices. The focus is on new concepts and knowledge in nanoscience and nanotechnology for applications in logic, memory, sensors, photonics, and renewable energy. This research on nanoelectronic materials and devices will be instructive in finding solutions to address the challenges of current electronics in switching speed, power consumption, and heat dissipation and will be of great interest to academic society and the industry.

Deep Learning Classifiers with Memristive Networks

Deep Learning Classifiers with Memristive Networks
Theory and Applications

by Alex Pappachen James

  • Publisher : Springer
  • Release : 2019-04-08
  • Pages : 213
  • ISBN : 3030145247
  • Language : En, Es, Fr & De
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This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.

Nonlinear Circuits and Systems with Memristors

Nonlinear Circuits and Systems with Memristors
Nonlinear Dynamics and Analogue Computing Via the Flux-Charge Analysis Method

by Fernando Corinto

  • Publisher : Springer Nature
  • Release : 2020
  • Pages : 461
  • ISBN : 3030556514
  • Language : En, Es, Fr & De
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Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design

Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design
A Book

by Nan Zheng,Pinaki Mazumder

  • Publisher : John Wiley & Sons
  • Release : 2019-12-09
  • Pages : 296
  • ISBN : 1119507383
  • Language : En, Es, Fr & De
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Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.

Emerging Technology and Architecture for Big-data Analytics

Emerging Technology and Architecture for Big-data Analytics
A Book

by Anupam Chattopadhyay,Chip Hong Chang,Hao Yu

  • Publisher : Springer
  • Release : 2017-06-01
  • Pages : 330
  • ISBN : 3319548409
  • Language : En, Es, Fr & De
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This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn’t work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics.

Memristor Technology: Synthesis and Modeling for Sensing and Security Applications

Memristor Technology: Synthesis and Modeling for Sensing and Security Applications
A Book

by Heba Abunahla,Baker Mohammad

  • Publisher : Springer
  • Release : 2017-09-18
  • Pages : 106
  • ISBN : 3319656996
  • Language : En, Es, Fr & De
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This book provides readers with a single-source guide to fabricate, characterize and model memristor devices for sensing applications. The authors describe a correlated, physics-based model to simulate and predict the behavior of devices fabricated with different oxide materials, active layer thickness, and operating temperature. They discuss memristors from various perspectives, including working mechanisms, different synthesis methods, characterization procedures, and device employment in radiation sensing and security applications.

Advances in Neural Networks- ISNN 2013

Advances in Neural Networks- ISNN 2013
10th International Symposium on Neural Networks, ISNN 2013, Dalian, China, July 4-6, 2013, Proceedings, Part I

by Chengan Guo,Zeng-Guang Hou,Zhigang Zeng

  • Publisher : Springer
  • Release : 2013-07-04
  • Pages : 687
  • ISBN : 364239065X
  • Language : En, Es, Fr & De
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The two-volume set LNCS 7951 and 7952 constitutes the refereed proceedings of the 10th International Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in following topics: computational neuroscience, cognitive science, neural network models, learning algorithms, stability and convergence analysis, kernel methods, large margin methods and SVM, optimization algorithms, varational methods, control, robotics, bioinformatics and biomedical engineering, brain-like systems and brain-computer interfaces, data mining and knowledge discovery and other applications of neural networks.

Architectures and Algorithms for Intrinsic Computation with Memristive Devices

Architectures and Algorithms for Intrinsic Computation with Memristive Devices
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2016
  • Pages : 156
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Neuromorphic engineering is the research field dedicated to the study and design of brain-inspired hardware and software tools. Recent advances in emerging nanoelectronics promote the implementation of synaptic connections based on memristive devices. Their non-volatile modifiable conductance was shown to exhibit the synaptic properties often used in connecting and training neural layers. With their nanoscale size and non-volatile memory property, they promise a next step in designing more area and energy efficient neuromorphic hardware. My research deals with the challenges of harnessing memristive device properties that go beyond the behaviors utilized for synaptic weight storage. Based on devices that exhibit non-linear state changes and volatility, I present novel architectures and algorithms that can harness such features for computation. The crossbar architecture is a dense array of memristive devices placed in-between horizontal and vertical nanowires. The regularity of this structure does not inherently provide the means for nonlinear computation of applied input signals. Introducing a modulation scheme that relies on nonlinear memristive device properties, heterogeneous state patterns of applied spatiotemporal input data can be created within the crossbar. In this setup, the untrained and dynamically changing states of the memristive devices offer a useful platform for information processing. Based on the MNIST data set I'll demonstrate how the temporal aspect of memristive state volatility can be utilized to reduce system size and training complexity for high dimensional input data. With 3 times less neurons and 15 times less synapses to train as compared to other memristor-based implementations, I achieve comparable classification rates of up to 93%. Exploiting dynamic state changes rather than precisely tuned stable states, this approach can tolerate device variation up to 6 times higher than reported levels. Random assemblies of memristive networks are analyzed as a substrate for intrinsic computation in connection with reservoir computing; a computational framework that harnesses observations of inherent dynamics within complex networks. Architectural and device level considerations lead to new levels of task complexity, which random memristive networks are now able to solve. A hierarchical design composed of independent random networks benefits from a diverse set of topologies and achieves prediction errors (NRMSE) on the time-series prediction task NARMA-10 as low as 0.15 as compared to 0.35 for an echo state network. Physically plausible network modeling is performed to investigate the relationship between network dynamics and energy consumption. Generally, increased network activity comes at the cost of exponentially increasing energy consumption due to nonlinear voltage-current characteristics of memristive devices. A trade-off, that allows linear scaling of energy consumption, is provided by the hierarchical approach. Rather than designing individual memristive networks with high switching activity, a collection of less dynamic, but independent networks can provide more diverse network activity per unit of energy. My research extends the possibilities of including emerging nanoelectronics into neuromorphic hardware. It establishes memristive devices beyond storage and motivates future research to further embrace memristive device properties that can be linked to different synaptic functions. Pursuing to exploit the functional diversity of memristive devices will lead to novel architectures and algorithms that study rather than dictate the behavior of such devices, with the benefit of creating robust and efficient neuromorphic hardware.

Advances in Memristors, Memristive Devices and Systems

Advances in Memristors, Memristive Devices and Systems
A Book

by Sundarapandian Vaidyanathan,Christos Volos

  • Publisher : Springer
  • Release : 2017-02-15
  • Pages : 511
  • ISBN : 3319517244
  • Language : En, Es, Fr & De
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This book reports on the latest advances in and applications of memristors, memristive devices and systems. It gathers 20 contributed chapters by subject experts, including pioneers in the field such as Leon Chua (UC Berkeley, USA) and R.S. Williams (HP Labs, USA), who are specialized in the various topics addressed in this book, and covers broad areas of memristors and memristive devices such as: memristor emulators, oscillators, chaotic and hyperchaotic memristive systems, control of memristive systems, memristor-based min-max circuits, canonic memristors, memristive-based neuromorphic applications, implementation of memristor-based chaotic oscillators, inverse memristors, linear memristor devices, delayed memristive systems, flux-controlled memristive emulators, etc. Throughout the book, special emphasis is given to papers offering practical solutions and design, modeling, and implementation insights to address current research problems in memristors, memristive devices and systems. As such, it offers a valuable reference book on memristors and memristive devices for graduate students and researchers with a basic knowledge of electrical and control systems engineering.

Memristor Networks

Memristor Networks
A Book

by Andrew Adamatzky,Leon Chua

  • Publisher : Springer Science & Business Media
  • Release : 2013-12-18
  • Pages : 720
  • ISBN : 3319026305
  • Language : En, Es, Fr & De
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Using memristors one can achieve circuit functionalities that are not possible to establish with resistors, capacitors and inductors, therefore the memristor is of great pragmatic usefulness. Potential unique applications of memristors are in spintronic devices, ultra-dense information storage, neuromorphic circuits and programmable electronics. Memristor Networks focuses on the design, fabrication, modelling of and implementation of computation in spatially extended discrete media with many memristors. Top experts in computer science, mathematics, electronics, physics and computer engineering present foundations of the memristor theory and applications, demonstrate how to design neuromorphic network architectures based on memristor assembles, analyse varieties of the dynamic behaviour of memristive networks and show how to realise computing devices from memristors. All aspects of memristor networks are presented in detail, in a fully accessible style. An indispensable source of information and an inspiring reference text, Memristor Networks is an invaluable resource for future generations of computer scientists, mathematicians, physicists and engineers.