Optical Neural Networks

Optical Neural Networks PDF

Author: Cornelia Denz

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 467

ISBN-13: 3663122727

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During the next years neural networks and systems amenable to instructions will extend their influence in science and technology. A prominent point of interest in this field is assigned to optical networks: they are small and flexible, and due to their ability of parallel processing they are devoted to the construction of small systems. This monograph explains the fundamentals of optical neural networks to physicists, engineers and device constructors.

Neuromorphic Photonics

Neuromorphic Photonics PDF

Author: Paul R. Prucnal

Publisher: CRC Press

Published: 2017-05-08

Total Pages: 412

ISBN-13: 1498725244

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This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics." It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field.

Artificial Neural Networks

Artificial Neural Networks PDF

Author: Kevin L. Priddy

Publisher: SPIE Press

Published: 2005

Total Pages: 184

ISBN-13: 9780819459879

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This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.

Photonic Reservoir Computing

Photonic Reservoir Computing PDF

Author: Daniel Brunner

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2019-07-08

Total Pages: 391

ISBN-13: 3110582112

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Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.

Neural Networks for Perception

Neural Networks for Perception PDF

Author: Harry Wechsler

Publisher: Academic Press

Published: 2014-05-10

Total Pages: 384

ISBN-13: 1483262790

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Neural Networks for Perception, Volume 2: Computation, Learning, and Architectures explores the computational and adaptation problems related to the use of neuronal systems, and the corresponding hardware architectures capable of implementing neural networks for perception and of coping with the complexity inherent in massively distributed computation. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The text is organized into two sections. The first section, computation and learning, discusses topics on learning visual behaviors, some of the elementary theory of the basic backpropagation neural network architecture, and computation and learning in the context of neural network capacity. The second section is on hardware architecture. The chapters included in this part of the book describe the architectures and possible applications of recent neurocomputing models. The Cohen-Grossberg model of associative memory, hybrid optical/digital architectures for neorocomputing, and electronic circuits for adaptive synapses are some of the subjects elucidated. Neuroscientists, computer scientists, engineers, and researchers in artificial intelligence will find the book useful.

Photonic Reservoir Computing

Photonic Reservoir Computing PDF

Author: Daniel Brunner

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2019-07-08

Total Pages: 277

ISBN-13: 3110583496

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Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.

Optical Signal Processing, Computing, and Neural Networks

Optical Signal Processing, Computing, and Neural Networks PDF

Author: Frances T. S. Yu

Publisher: Wiley-Interscience

Published: 1992-11-19

Total Pages: 440

ISBN-13:

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In recent years, optical computing and optical neural networks research has enriched the field originally known as optical signal processing. Optical Signal Processing, Computing, and Neural Networks is a self-contained textbook that offers an introductory survey which examines photonics, linear and nonlinear signal processing, and numerical, symbolic, and neural computing. This comprehensive sourcebook is a basic text for students who lack an intensive background in optic, electromagnetic, computer, and neural network theories. It will also serve as a working reference for optical physicists and engineers involved in current research and development of modern optical signal processing that includes optical computing and neural networks. The first chapter of this book contains the basic coherent theory and concepts of optical transformation. The second chapter introduces the fundamental concept of optical signal processing and its architectures. The third chapter presents selected applications in coherent optics while the fourth chapter discusses white-light processing and its applications. The advances of spatial-light modulators are discussed as well as hybrid-optical architectures using spatial-light modulators in later chapters. Applications of photorefractive crystals in optical signal processing are presented in chapter 7. Digital-optical computing is described in chapter 8 while optical neural networks and their architectures, designs, and models are thoroughly covered in chapter 9. Examples and experimental results are included throughout the book to emphasize the concepts. Chapters include problem sets, 330 throughout, that reinforce key elements in the text.

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