Photonic Neural Networks with Spatiotemporal Dynamics : : Paradigms of Computing and Implementation / / edited by Hideyuki Suzuki, Jun Tanida, Masanori Hashimoto.
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 mathemat...
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Place / Publishing House: | Singapore : : Springer Nature Singapore :, Imprint: Springer,, 2024. |
Year of Publication: | 2024 |
Edition: | 1st ed. 2024. |
Language: | English |
Physical Description: | 1 online resource (277 pages) |
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(CKB)5680000000354353 (MiAaPQ)EBC30882937 (Au-PeEL)EBL30882937 (DE-He213)978-981-99-5072-0 (PPN)272920037 (OCoLC)1409705951 (EXLCZ)995680000000354353 |
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Suzuki, Hideyuki. Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation / edited by Hideyuki Suzuki, Jun Tanida, Masanori Hashimoto. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. 1 online resource (277 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. 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 Artificial intelligence. Neural networks (Computer science). Nonlinear Optics. Artificial Intelligence. Mathematical Models of Cognitive Processes and Neural Networks. 981-9950-71-6 Tanida, Jun. Hashimoto, Masanori. |
language |
English |
format |
eBook |
author |
Suzuki, Hideyuki. |
spellingShingle |
Suzuki, Hideyuki. Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation / 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. |
author_facet |
Suzuki, Hideyuki. Tanida, Jun. Hashimoto, Masanori. |
author_variant |
h s hs |
author2 |
Tanida, Jun. Hashimoto, Masanori. |
author2_variant |
j t jt m h mh |
author2_role |
TeilnehmendeR TeilnehmendeR |
author_sort |
Suzuki, Hideyuki. |
title |
Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation / |
title_sub |
Paradigms of Computing and Implementation / |
title_full |
Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation / edited by Hideyuki Suzuki, Jun Tanida, Masanori Hashimoto. |
title_fullStr |
Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation / edited by Hideyuki Suzuki, Jun Tanida, Masanori Hashimoto. |
title_full_unstemmed |
Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation / edited by Hideyuki Suzuki, Jun Tanida, Masanori Hashimoto. |
title_auth |
Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation / |
title_new |
Photonic Neural Networks with Spatiotemporal Dynamics : |
title_sort |
photonic neural networks with spatiotemporal dynamics : paradigms of computing and implementation / |
publisher |
Springer Nature Singapore : Imprint: Springer, |
publishDate |
2024 |
physical |
1 online resource (277 pages) |
edition |
1st ed. 2024. |
contents |
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. |
isbn |
981-9950-72-4 981-9950-71-6 |
callnumber-first |
Q - Science |
callnumber-subject |
Q - General Science |
callnumber-label |
Q334-342 |
callnumber-sort |
Q 3334 3342 |
illustrated |
Not Illustrated |
dewey-hundreds |
000 - Computer science, information & general works |
dewey-tens |
000 - Computer science, knowledge & systems |
dewey-ones |
006 - Special computer methods |
dewey-full |
006.3 |
dewey-sort |
16.3 |
dewey-raw |
006.3 |
dewey-search |
006.3 |
oclc_num |
1409705951 |
work_keys_str_mv |
AT suzukihideyuki photonicneuralnetworkswithspatiotemporaldynamicsparadigmsofcomputingandimplementation AT tanidajun photonicneuralnetworkswithspatiotemporaldynamicsparadigmsofcomputingandimplementation AT hashimotomasanori photonicneuralnetworkswithspatiotemporaldynamicsparadigmsofcomputingandimplementation |
status_str |
n |
ids_txt_mv |
(CKB)5680000000354353 (MiAaPQ)EBC30882937 (Au-PeEL)EBL30882937 (DE-He213)978-981-99-5072-0 (PPN)272920037 (OCoLC)1409705951 (EXLCZ)995680000000354353 |
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is_hierarchy_title |
Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation / |
author2_original_writing_str_mv |
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