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020 _a9789819950720
_9978-981-99-5072-0
024 7 _a10.1007/978-981-99-5072-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
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072 7 _aUYQ
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082 0 4 _a006.3
_223
245 1 0 _aPhotonic Neural Networks with Spatiotemporal Dynamics
_h[electronic resource] :
_bParadigms of Computing and Implementation /
_cedited by Hideyuki Suzuki, Jun Tanida, Masanori Hashimoto.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aVIII, 278 p. 128 illus., 108 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aRevival 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.
506 0 _aOpen Access
520 _aThis 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 andphotonic 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.
650 0 _aArtificial intelligence.
650 0 _aNeural networks (Computer science) .
650 0 _aNonlinear Optics.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
650 2 4 _aNonlinear Optics.
700 1 _aSuzuki, Hideyuki.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aTanida, Jun.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aHashimoto, Masanori.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819950713
776 0 8 _iPrinted edition:
_z9789819950737
776 0 8 _iPrinted edition:
_z9789819950744
856 4 0 _uhttps://doi.org/10.1007/978-981-99-5072-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-SOB
999 _c37466
_d37466