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Photonic neural networks with spatiotemporal dynamicsparadigms of computing and implementation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Photonic neural networks with spatiotemporal dynamicsedited by Hideyuki Suzuki, Jun Tanida, Masanori Hashimoto.
其他題名:	paradigms of computing and implementation /
其他作者:	Suzuki, Hideyuki.
出版者:	Singapore :Springer Nature Singapore :2024.
面頁冊數:	viii, 278 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science)
電子資源:	https://doi.org/10.1007/978-981-99-5072-0
ISBN:	9789819950720$q(electronic bk.)

Photonic neural networks with spatiotemporal dynamicsparadigms of computing and implementation /
Photonic neural networks with spatiotemporal dynamicsparadigms of computing and implementation /[electronic resource] :edited by Hideyuki Suzuki, Jun Tanida, Masanori Hashimoto. - Singapore :Springer Nature Singapore :2024. - viii, 278 p. :ill., digital ;24 cm.

Revival of Optical Computing -- Nonlinear Dynamics of Recurrent Neural Networks for Computing -- Fluorescence Energy Transfer Computing -- Quantum-Dot Based Photonic Reservoir Computing -- Exploring Integrated Device Implementation for FRET-based Optical Reservoir Computing -- FRET Networks -- Quantum Walk on FRET Networks -- Spatial photonic Ising machine with time/space division multiplexing -- Computing using Oscillatory Phenomena -- Sampling-like Dynamics of the Nonlinear Dynamical System Combined with Optimization -- Reservoir Computing Based on Iterative Function Systems -- Bridging the Gap between Reservoirs and Neural Networks -- Brain-Inspired Reservoir Computing Models.

Open access.

This open access book presents an overview of recent advances in photonic neural networks with spatiotemporal dynamics. The computing and implementation paradigms presented in this book are outcomes of interdisciplinary studies by collaborative researchers from the three fields of nonlinear mathematical science, information photonics, and integrated systems engineering. This book offers novel multidisciplinary viewpoints on photonic neural networks, illustrating recent advances in three types of computing methodologies: fluorescence energy transfer computing, spatial-photonic spin system, and photonic reservoir computing. The book consists of four parts: Part I introduces the backgrounds of optical computing and neural network dynamics; Part II presents fluorescence energy transfer computing, a novel computing technology based on nanoscale networks of fluorescent particles; Parts III and IV review the models and implementation of spatial-photonic spin systems and photonic reservoir computing, respectively. These contents are beneficial to researchers in a broad range of fields, including information science, mathematical science, applied physics, and engineering, to better understand the novel computing concepts of photonic neural networks with spatiotemporal dynamics.

ISBN: 9789819950720$q(electronic bk.)

Standard No.: 10.1007/978-981-99-5072-0doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

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520 $a This open access book presents an overview of recent advances in photonic neural networks with spatiotemporal dynamics. The computing and implementation paradigms presented in this book are outcomes of interdisciplinary studies by collaborative researchers from the three fields of nonlinear mathematical science, information photonics, and integrated systems engineering. This book offers novel multidisciplinary viewpoints on photonic neural networks, illustrating recent advances in three types of computing methodologies: fluorescence energy transfer computing, spatial-photonic spin system, and photonic reservoir computing. The book consists of four parts: Part I introduces the backgrounds of optical computing and neural network dynamics; Part II presents fluorescence energy transfer computing, a novel computing technology based on nanoscale networks of fluorescent particles; Parts III and IV review the models and implementation of spatial-photonic spin systems and photonic reservoir computing, respectively. These contents are beneficial to researchers in a broad range of fields, including information science, mathematical science, applied physics, and engineering, to better understand the novel computing concepts of photonic neural networks with spatiotemporal dynamics.
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