Photonic Neural Networks with Spatiotemporal Dynamics : : Paradigms of Computing and Implementation.
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Place / Publishing House: | Singapore : : Springer,, 2023. ©2024. |
Year of Publication: | 2023 |
Edition: | 1st ed. |
Language: | English |
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Physical Description: | 1 online resource (277 pages) |
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Suzuki, Hideyuki. Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation. 1st ed. Singapore : Springer, 2023. ©2024. 1 online resource (277 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Intro -- Preface -- Contents -- Introduction -- Revival of Optical Computing -- 1 Introduction -- 2 Fourier Optical Processing -- 3 Digital Optical Computing -- 4 Optical Neurocomputing -- 5 Optical Special Purpose Processing -- 6 Nanophotonic Computing -- 7 Toward Further Extension -- References -- Nonlinear Dynamics and Computing in Recurrent Neural Networks -- 1 Introduction -- 2 Fundamental RNN Models and Energy Function -- 2.1 Amari-Hopfield Network with Binary States -- 2.2 Amari-Hopfield Network with Continuous States -- 2.3 Boltzmann Machine -- 3 Nonlinear Dynamics in Symmetrically Connected RNNs -- 3.1 Chaotic Neural Network -- 3.2 Chaotic Boltzmann Machine -- 3.3 Ising Machines -- 3.4 Herding System -- 4 Reservoir Computing -- 4.1 Training Input-Output Relation of RNNs -- 4.2 Echo State Network -- 4.3 Echo State Property and Reservoir Design -- 4.4 Neural Network Reservoirs -- 4.5 Physical Reservoir Computing -- 5 Towards Photonic Neural Network Computing -- References -- Fluorescence Energy Transfer Computing -- Fluorescence Energy Transfer Computing -- 1 Fluorescence Energy Transfer -- 2 FRET-Based Device -- 2.1 DNA for FRET Devices -- 2.2 FRET-Based Photonic Wire -- 2.3 FRET-Based Photonic Switch -- 2.4 FRET-Based Logic Gate -- 3 Scaffold DNA Logic -- 3.1 Basic Concept -- 3.2 Arrangement Control of Fluorescent Molecules -- 3.3 Design of Logic Operations -- 3.4 AND Operation -- 3.5 OR Operation -- 3.6 NOT Operation -- 3.7 Extended FRET Connection -- 4 Optical Program of DNA Scaffold Logic -- 4.1 Optical Control of FRET -- 4.2 Optical Control of FRET Pathway -- 4.3 Optically Programmable DNA Scaffold Logic -- 5 FRET Network-Based Computation -- References -- Quantum-Dot-Based Photonic Reservoir Computing -- 1 Introduction -- 2 Quantum-Dot-Based Physical Reservoir -- 2.1 Basics -- 2.2 Experimental Demonstration: Randomly-Dispersed QDR. 2.3 Experimental Demonstration: Electrophoresis -- 3 Non-linearity of the QD Reservoir -- 3.1 Experimental Setup -- 3.2 Qualitative Non-linearity -- 4 Spatio-Temporal Photonic Processing -- 4.1 Basics -- 4.2 Streak Measurement -- 4.3 Spatiotemporal Reservoir Model -- 4.4 Experimental Demonstration -- 5 Conclusion and Future Prospect -- References -- Exploring Integrated Device Implementation for FRET-Based Optical Reservoir Computing -- 1 Introduction -- 2 Proposed Device Structure -- 2.1 Device Structure -- 2.2 Network Mapping -- 2.3 Experiments -- 3 Proof-of-Concept Prototyping -- 3.1 Implementation -- 3.2 Evaluation -- 4 Discussion on Energy Advantage -- 4.1 Power Estimation Approach -- 4.2 Result -- 5 Summary -- References -- FRET Networks: Modeling and Analysis for Computing -- 1 Introduction -- 2 Spatiotemporal Model for FRET Networks -- 2.1 Multiple-Donor Model -- 2.2 Analytical Results -- 3 Simulation Methods -- 3.1 Deterministic Simulation -- 3.2 Stochastic Simulation -- 3.3 Comparison Between Deterministic and Stochastic Simulation -- 4 Information Processing Using FRET Networks -- 4.1 Nonlinear Function Approximation -- 4.2 Chaotic Time-Series Prediction -- 4.3 Handwritten Digit Recognition -- 5 Conclusions and Future Works -- References -- Quantum Walk on FRET Networks -- 1 Introduction of Quantum Walk -- 1.1 Classical Random Walk -- 1.2 Quantum Walk -- 1.3 Quantum Walk on FRET Networks -- 2 Application of Quantum Walk -- 2.1 Classical Parton Shower -- 2.2 Quantum Parton Shower Algorithm -- 3 Conclusion -- References -- Spatial-Photonic Spin System -- Spatial Photonic Ising Machine with Time/Space Division Multiplexing -- 1 Introduction -- 2 Spatial Photonic Ising Machine with Multiplexing -- 2.1 Basic Scheme of SPIM -- 2.2 Concept of SPIMs with Multiplexing -- 3 Time Division Multiplexed (TDM)-SPIM. 4 Space Division Multiplexing (SDM)-SPIM -- 5 Conclusion -- References -- Investigation on Oscillator-Based Ising Machines -- 1 Introduction -- 2 Ising Model and Ising Machines -- 3 Oscillator-Based Ising Machines -- 3.1 Phase Oscillators -- 3.2 Second-Harmonic Injection Locking -- 3.3 Kuramoto Model -- 3.4 Gradient Structure of the Kuramoto Model -- 3.5 Working Principle of OIMs -- 4 Experiments -- 4.1 MAX-CUT Problem -- 4.2 Experimental Setting and Evaluation Metrics -- 4.3 Effect of Properties of Noisy Fluctuation and Coarse Time Discretization -- 4.4 Effect of Injection and Noise Strength -- 4.5 Effect of Higher Harmonics in Coupling and Injection Schemes -- 5 Summary -- References -- Sampling-Like Dynamics of the Nonlinear Dynamical System Combined with Optimization -- 1 Herding: Sample Generation Using Nonlinear Dynamics and Monte Carlo Integration -- 1.1 Deterministic Tossup -- 1.2 Herding -- 1.3 Convergence of Herding -- 2 Herded Gibbs: Model-Based Herding on Spin System -- 2.1 Markov Chain Monte Carlo Method -- 2.2 Herded Gibbs -- 2.3 Sharing Weight Variables -- 2.4 Monte Carlo Integration Using HG -- 2.5 Rao-Blackwellization -- 3 Entropic Herding: Regularizer for Dynamical Sampling -- 3.1 Maximum Entropy Principle -- 3.2 Entropic Herding -- 3.3 Dynamical Entropic Maximization in Herding -- 3.4 Other Notes on Entropic Herding -- 4 Discussion -- 4.1 Application of Physical Combinatorial Optimization Using Herding -- 4.2 Generative Models and Herding -- 5 Conclusion -- References -- Photonic Reservoir Computing -- Reservoir Computing Based on Iterative Function Systems -- 1 Introduction -- 2 Iterative Function Systems -- 3 Iterative Function System-Based Reservoir Computing -- 4 Prediction Performance of IFSRC -- 4.1 Multi-step Ahead Prediction -- 4.2 Single-Step Ahead Prediction of Santa Fe Time-Series Data. 5 Experimental Performance of IFS Reservoir Computing -- 5.1 Optical Setup -- 5.2 Multi-step Ahead Prediction of Mackey-Glass Equation -- 5.3 Single-step Ahead Prediction of Santa Fe Time-Series Data -- 6 Relationship Between Performance and Spectral Radius -- 7 IFS Reservoir Computing with Optical Feedback Loop -- 8 Discussion -- References -- Bridging the Gap Between Reservoirs and Neural Networks -- 1 Introduction -- 2 Background -- 2.1 Lottery Ticket Hypotheses -- 2.2 Residual Neural Networks -- 3 Hidden-Fold Networks -- 3.1 Folded ResNet Architecture -- 3.2 Supermask Training -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Results -- 5 Summary -- References -- Brain-Inspired Reservoir Computing Models -- 1 Introduction -- 2 Reservoir-Based Predictive Coding Model -- 3 Reservoir-Based Reinforcement Learning Model -- 4 Integrated Model and Mental simulation -- 5 Summary -- References. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Tanida, Jun. Hashimoto, Masanori. Print version: Suzuki, Hideyuki Photonic Neural Networks with Spatiotemporal Dynamics Singapore : Springer,c2023 9789819950713 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30882937 Click to View |
language |
English |
format |
eBook |
author |
Suzuki, Hideyuki. |
spellingShingle |
Suzuki, Hideyuki. Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation. Intro -- Preface -- Contents -- Introduction -- Revival of Optical Computing -- 1 Introduction -- 2 Fourier Optical Processing -- 3 Digital Optical Computing -- 4 Optical Neurocomputing -- 5 Optical Special Purpose Processing -- 6 Nanophotonic Computing -- 7 Toward Further Extension -- References -- Nonlinear Dynamics and Computing in Recurrent Neural Networks -- 1 Introduction -- 2 Fundamental RNN Models and Energy Function -- 2.1 Amari-Hopfield Network with Binary States -- 2.2 Amari-Hopfield Network with Continuous States -- 2.3 Boltzmann Machine -- 3 Nonlinear Dynamics in Symmetrically Connected RNNs -- 3.1 Chaotic Neural Network -- 3.2 Chaotic Boltzmann Machine -- 3.3 Ising Machines -- 3.4 Herding System -- 4 Reservoir Computing -- 4.1 Training Input-Output Relation of RNNs -- 4.2 Echo State Network -- 4.3 Echo State Property and Reservoir Design -- 4.4 Neural Network Reservoirs -- 4.5 Physical Reservoir Computing -- 5 Towards Photonic Neural Network Computing -- References -- Fluorescence Energy Transfer Computing -- Fluorescence Energy Transfer Computing -- 1 Fluorescence Energy Transfer -- 2 FRET-Based Device -- 2.1 DNA for FRET Devices -- 2.2 FRET-Based Photonic Wire -- 2.3 FRET-Based Photonic Switch -- 2.4 FRET-Based Logic Gate -- 3 Scaffold DNA Logic -- 3.1 Basic Concept -- 3.2 Arrangement Control of Fluorescent Molecules -- 3.3 Design of Logic Operations -- 3.4 AND Operation -- 3.5 OR Operation -- 3.6 NOT Operation -- 3.7 Extended FRET Connection -- 4 Optical Program of DNA Scaffold Logic -- 4.1 Optical Control of FRET -- 4.2 Optical Control of FRET Pathway -- 4.3 Optically Programmable DNA Scaffold Logic -- 5 FRET Network-Based Computation -- References -- Quantum-Dot-Based Photonic Reservoir Computing -- 1 Introduction -- 2 Quantum-Dot-Based Physical Reservoir -- 2.1 Basics -- 2.2 Experimental Demonstration: Randomly-Dispersed QDR. 2.3 Experimental Demonstration: Electrophoresis -- 3 Non-linearity of the QD Reservoir -- 3.1 Experimental Setup -- 3.2 Qualitative Non-linearity -- 4 Spatio-Temporal Photonic Processing -- 4.1 Basics -- 4.2 Streak Measurement -- 4.3 Spatiotemporal Reservoir Model -- 4.4 Experimental Demonstration -- 5 Conclusion and Future Prospect -- References -- Exploring Integrated Device Implementation for FRET-Based Optical Reservoir Computing -- 1 Introduction -- 2 Proposed Device Structure -- 2.1 Device Structure -- 2.2 Network Mapping -- 2.3 Experiments -- 3 Proof-of-Concept Prototyping -- 3.1 Implementation -- 3.2 Evaluation -- 4 Discussion on Energy Advantage -- 4.1 Power Estimation Approach -- 4.2 Result -- 5 Summary -- References -- FRET Networks: Modeling and Analysis for Computing -- 1 Introduction -- 2 Spatiotemporal Model for FRET Networks -- 2.1 Multiple-Donor Model -- 2.2 Analytical Results -- 3 Simulation Methods -- 3.1 Deterministic Simulation -- 3.2 Stochastic Simulation -- 3.3 Comparison Between Deterministic and Stochastic Simulation -- 4 Information Processing Using FRET Networks -- 4.1 Nonlinear Function Approximation -- 4.2 Chaotic Time-Series Prediction -- 4.3 Handwritten Digit Recognition -- 5 Conclusions and Future Works -- References -- Quantum Walk on FRET Networks -- 1 Introduction of Quantum Walk -- 1.1 Classical Random Walk -- 1.2 Quantum Walk -- 1.3 Quantum Walk on FRET Networks -- 2 Application of Quantum Walk -- 2.1 Classical Parton Shower -- 2.2 Quantum Parton Shower Algorithm -- 3 Conclusion -- References -- Spatial-Photonic Spin System -- Spatial Photonic Ising Machine with Time/Space Division Multiplexing -- 1 Introduction -- 2 Spatial Photonic Ising Machine with Multiplexing -- 2.1 Basic Scheme of SPIM -- 2.2 Concept of SPIMs with Multiplexing -- 3 Time Division Multiplexed (TDM)-SPIM. 4 Space Division Multiplexing (SDM)-SPIM -- 5 Conclusion -- References -- Investigation on Oscillator-Based Ising Machines -- 1 Introduction -- 2 Ising Model and Ising Machines -- 3 Oscillator-Based Ising Machines -- 3.1 Phase Oscillators -- 3.2 Second-Harmonic Injection Locking -- 3.3 Kuramoto Model -- 3.4 Gradient Structure of the Kuramoto Model -- 3.5 Working Principle of OIMs -- 4 Experiments -- 4.1 MAX-CUT Problem -- 4.2 Experimental Setting and Evaluation Metrics -- 4.3 Effect of Properties of Noisy Fluctuation and Coarse Time Discretization -- 4.4 Effect of Injection and Noise Strength -- 4.5 Effect of Higher Harmonics in Coupling and Injection Schemes -- 5 Summary -- References -- Sampling-Like Dynamics of the Nonlinear Dynamical System Combined with Optimization -- 1 Herding: Sample Generation Using Nonlinear Dynamics and Monte Carlo Integration -- 1.1 Deterministic Tossup -- 1.2 Herding -- 1.3 Convergence of Herding -- 2 Herded Gibbs: Model-Based Herding on Spin System -- 2.1 Markov Chain Monte Carlo Method -- 2.2 Herded Gibbs -- 2.3 Sharing Weight Variables -- 2.4 Monte Carlo Integration Using HG -- 2.5 Rao-Blackwellization -- 3 Entropic Herding: Regularizer for Dynamical Sampling -- 3.1 Maximum Entropy Principle -- 3.2 Entropic Herding -- 3.3 Dynamical Entropic Maximization in Herding -- 3.4 Other Notes on Entropic Herding -- 4 Discussion -- 4.1 Application of Physical Combinatorial Optimization Using Herding -- 4.2 Generative Models and Herding -- 5 Conclusion -- References -- Photonic Reservoir Computing -- Reservoir Computing Based on Iterative Function Systems -- 1 Introduction -- 2 Iterative Function Systems -- 3 Iterative Function System-Based Reservoir Computing -- 4 Prediction Performance of IFSRC -- 4.1 Multi-step Ahead Prediction -- 4.2 Single-Step Ahead Prediction of Santa Fe Time-Series Data. 5 Experimental Performance of IFS Reservoir Computing -- 5.1 Optical Setup -- 5.2 Multi-step Ahead Prediction of Mackey-Glass Equation -- 5.3 Single-step Ahead Prediction of Santa Fe Time-Series Data -- 6 Relationship Between Performance and Spectral Radius -- 7 IFS Reservoir Computing with Optical Feedback Loop -- 8 Discussion -- References -- Bridging the Gap Between Reservoirs and Neural Networks -- 1 Introduction -- 2 Background -- 2.1 Lottery Ticket Hypotheses -- 2.2 Residual Neural Networks -- 3 Hidden-Fold Networks -- 3.1 Folded ResNet Architecture -- 3.2 Supermask Training -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Results -- 5 Summary -- References -- Brain-Inspired Reservoir Computing Models -- 1 Introduction -- 2 Reservoir-Based Predictive Coding Model -- 3 Reservoir-Based Reinforcement Learning Model -- 4 Integrated Model and Mental simulation -- 5 Summary -- References. |
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. |
title_fullStr |
Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation. |
title_full_unstemmed |
Photonic Neural Networks with Spatiotemporal Dynamics : Paradigms of Computing and Implementation. |
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, |
publishDate |
2023 |
physical |
1 online resource (277 pages) |
edition |
1st ed. |
contents |
Intro -- Preface -- Contents -- Introduction -- Revival of Optical Computing -- 1 Introduction -- 2 Fourier Optical Processing -- 3 Digital Optical Computing -- 4 Optical Neurocomputing -- 5 Optical Special Purpose Processing -- 6 Nanophotonic Computing -- 7 Toward Further Extension -- References -- Nonlinear Dynamics and Computing in Recurrent Neural Networks -- 1 Introduction -- 2 Fundamental RNN Models and Energy Function -- 2.1 Amari-Hopfield Network with Binary States -- 2.2 Amari-Hopfield Network with Continuous States -- 2.3 Boltzmann Machine -- 3 Nonlinear Dynamics in Symmetrically Connected RNNs -- 3.1 Chaotic Neural Network -- 3.2 Chaotic Boltzmann Machine -- 3.3 Ising Machines -- 3.4 Herding System -- 4 Reservoir Computing -- 4.1 Training Input-Output Relation of RNNs -- 4.2 Echo State Network -- 4.3 Echo State Property and Reservoir Design -- 4.4 Neural Network Reservoirs -- 4.5 Physical Reservoir Computing -- 5 Towards Photonic Neural Network Computing -- References -- Fluorescence Energy Transfer Computing -- Fluorescence Energy Transfer Computing -- 1 Fluorescence Energy Transfer -- 2 FRET-Based Device -- 2.1 DNA for FRET Devices -- 2.2 FRET-Based Photonic Wire -- 2.3 FRET-Based Photonic Switch -- 2.4 FRET-Based Logic Gate -- 3 Scaffold DNA Logic -- 3.1 Basic Concept -- 3.2 Arrangement Control of Fluorescent Molecules -- 3.3 Design of Logic Operations -- 3.4 AND Operation -- 3.5 OR Operation -- 3.6 NOT Operation -- 3.7 Extended FRET Connection -- 4 Optical Program of DNA Scaffold Logic -- 4.1 Optical Control of FRET -- 4.2 Optical Control of FRET Pathway -- 4.3 Optically Programmable DNA Scaffold Logic -- 5 FRET Network-Based Computation -- References -- Quantum-Dot-Based Photonic Reservoir Computing -- 1 Introduction -- 2 Quantum-Dot-Based Physical Reservoir -- 2.1 Basics -- 2.2 Experimental Demonstration: Randomly-Dispersed QDR. 2.3 Experimental Demonstration: Electrophoresis -- 3 Non-linearity of the QD Reservoir -- 3.1 Experimental Setup -- 3.2 Qualitative Non-linearity -- 4 Spatio-Temporal Photonic Processing -- 4.1 Basics -- 4.2 Streak Measurement -- 4.3 Spatiotemporal Reservoir Model -- 4.4 Experimental Demonstration -- 5 Conclusion and Future Prospect -- References -- Exploring Integrated Device Implementation for FRET-Based Optical Reservoir Computing -- 1 Introduction -- 2 Proposed Device Structure -- 2.1 Device Structure -- 2.2 Network Mapping -- 2.3 Experiments -- 3 Proof-of-Concept Prototyping -- 3.1 Implementation -- 3.2 Evaluation -- 4 Discussion on Energy Advantage -- 4.1 Power Estimation Approach -- 4.2 Result -- 5 Summary -- References -- FRET Networks: Modeling and Analysis for Computing -- 1 Introduction -- 2 Spatiotemporal Model for FRET Networks -- 2.1 Multiple-Donor Model -- 2.2 Analytical Results -- 3 Simulation Methods -- 3.1 Deterministic Simulation -- 3.2 Stochastic Simulation -- 3.3 Comparison Between Deterministic and Stochastic Simulation -- 4 Information Processing Using FRET Networks -- 4.1 Nonlinear Function Approximation -- 4.2 Chaotic Time-Series Prediction -- 4.3 Handwritten Digit Recognition -- 5 Conclusions and Future Works -- References -- Quantum Walk on FRET Networks -- 1 Introduction of Quantum Walk -- 1.1 Classical Random Walk -- 1.2 Quantum Walk -- 1.3 Quantum Walk on FRET Networks -- 2 Application of Quantum Walk -- 2.1 Classical Parton Shower -- 2.2 Quantum Parton Shower Algorithm -- 3 Conclusion -- References -- Spatial-Photonic Spin System -- Spatial Photonic Ising Machine with Time/Space Division Multiplexing -- 1 Introduction -- 2 Spatial Photonic Ising Machine with Multiplexing -- 2.1 Basic Scheme of SPIM -- 2.2 Concept of SPIMs with Multiplexing -- 3 Time Division Multiplexed (TDM)-SPIM. 4 Space Division Multiplexing (SDM)-SPIM -- 5 Conclusion -- References -- Investigation on Oscillator-Based Ising Machines -- 1 Introduction -- 2 Ising Model and Ising Machines -- 3 Oscillator-Based Ising Machines -- 3.1 Phase Oscillators -- 3.2 Second-Harmonic Injection Locking -- 3.3 Kuramoto Model -- 3.4 Gradient Structure of the Kuramoto Model -- 3.5 Working Principle of OIMs -- 4 Experiments -- 4.1 MAX-CUT Problem -- 4.2 Experimental Setting and Evaluation Metrics -- 4.3 Effect of Properties of Noisy Fluctuation and Coarse Time Discretization -- 4.4 Effect of Injection and Noise Strength -- 4.5 Effect of Higher Harmonics in Coupling and Injection Schemes -- 5 Summary -- References -- Sampling-Like Dynamics of the Nonlinear Dynamical System Combined with Optimization -- 1 Herding: Sample Generation Using Nonlinear Dynamics and Monte Carlo Integration -- 1.1 Deterministic Tossup -- 1.2 Herding -- 1.3 Convergence of Herding -- 2 Herded Gibbs: Model-Based Herding on Spin System -- 2.1 Markov Chain Monte Carlo Method -- 2.2 Herded Gibbs -- 2.3 Sharing Weight Variables -- 2.4 Monte Carlo Integration Using HG -- 2.5 Rao-Blackwellization -- 3 Entropic Herding: Regularizer for Dynamical Sampling -- 3.1 Maximum Entropy Principle -- 3.2 Entropic Herding -- 3.3 Dynamical Entropic Maximization in Herding -- 3.4 Other Notes on Entropic Herding -- 4 Discussion -- 4.1 Application of Physical Combinatorial Optimization Using Herding -- 4.2 Generative Models and Herding -- 5 Conclusion -- References -- Photonic Reservoir Computing -- Reservoir Computing Based on Iterative Function Systems -- 1 Introduction -- 2 Iterative Function Systems -- 3 Iterative Function System-Based Reservoir Computing -- 4 Prediction Performance of IFSRC -- 4.1 Multi-step Ahead Prediction -- 4.2 Single-Step Ahead Prediction of Santa Fe Time-Series Data. 5 Experimental Performance of IFS Reservoir Computing -- 5.1 Optical Setup -- 5.2 Multi-step Ahead Prediction of Mackey-Glass Equation -- 5.3 Single-step Ahead Prediction of Santa Fe Time-Series Data -- 6 Relationship Between Performance and Spectral Radius -- 7 IFS Reservoir Computing with Optical Feedback Loop -- 8 Discussion -- References -- Bridging the Gap Between Reservoirs and Neural Networks -- 1 Introduction -- 2 Background -- 2.1 Lottery Ticket Hypotheses -- 2.2 Residual Neural Networks -- 3 Hidden-Fold Networks -- 3.1 Folded ResNet Architecture -- 3.2 Supermask Training -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Results -- 5 Summary -- References -- Brain-Inspired Reservoir Computing Models -- 1 Introduction -- 2 Reservoir-Based Predictive Coding Model -- 3 Reservoir-Based Reinforcement Learning Model -- 4 Integrated Model and Mental simulation -- 5 Summary -- References. |
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Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30882937 |
illustrated |
Not Illustrated |
oclc_num |
1409705951 |
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Networks -- 1 Introduction -- 2 Fundamental RNN Models and Energy Function -- 2.1 Amari-Hopfield Network with Binary States -- 2.2 Amari-Hopfield Network with Continuous States -- 2.3 Boltzmann Machine -- 3 Nonlinear Dynamics in Symmetrically Connected RNNs -- 3.1 Chaotic Neural Network -- 3.2 Chaotic Boltzmann Machine -- 3.3 Ising Machines -- 3.4 Herding System -- 4 Reservoir Computing -- 4.1 Training Input-Output Relation of RNNs -- 4.2 Echo State Network -- 4.3 Echo State Property and Reservoir Design -- 4.4 Neural Network Reservoirs -- 4.5 Physical Reservoir Computing -- 5 Towards Photonic Neural Network Computing -- References -- Fluorescence Energy Transfer Computing -- Fluorescence Energy Transfer Computing -- 1 Fluorescence Energy Transfer -- 2 FRET-Based Device -- 2.1 DNA for FRET Devices -- 2.2 FRET-Based Photonic Wire -- 2.3 FRET-Based Photonic Switch -- 2.4 FRET-Based Logic Gate -- 3 Scaffold DNA Logic -- 3.1 Basic Concept -- 3.2 Arrangement Control of Fluorescent Molecules 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for FRET-Based Optical Reservoir Computing -- 1 Introduction -- 2 Proposed Device Structure -- 2.1 Device Structure -- 2.2 Network Mapping -- 2.3 Experiments -- 3 Proof-of-Concept Prototyping -- 3.1 Implementation -- 3.2 Evaluation -- 4 Discussion on Energy Advantage -- 4.1 Power Estimation Approach -- 4.2 Result -- 5 Summary -- References -- FRET Networks: Modeling and Analysis for Computing -- 1 Introduction -- 2 Spatiotemporal Model for FRET Networks -- 2.1 Multiple-Donor Model -- 2.2 Analytical Results -- 3 Simulation Methods -- 3.1 Deterministic Simulation -- 3.2 Stochastic Simulation -- 3.3 Comparison Between Deterministic and Stochastic Simulation -- 4 Information Processing Using FRET Networks -- 4.1 Nonlinear Function Approximation -- 4.2 Chaotic Time-Series Prediction -- 4.3 Handwritten Digit Recognition -- 5 Conclusions and Future Works -- References -- Quantum Walk on FRET Networks -- 1 Introduction of Quantum Walk -- 1.1 Classical Random Walk -- 1.2 Quantum Walk -- 1.3 Quantum Walk on FRET Networks -- 2 Application of Quantum Walk -- 2.1 Classical Parton Shower -- 2.2 Quantum Parton Shower Algorithm -- 3 Conclusion -- References -- Spatial-Photonic Spin System -- Spatial Photonic Ising Machine with Time/Space Division Multiplexing -- 1 Introduction -- 2 Spatial Photonic Ising Machine with Multiplexing -- 2.1 Basic Scheme of SPIM -- 2.2 Concept of SPIMs with Multiplexing -- 3 Time Division Multiplexed (TDM)-SPIM.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4 Space Division Multiplexing (SDM)-SPIM -- 5 Conclusion -- References -- Investigation on Oscillator-Based Ising Machines -- 1 Introduction -- 2 Ising Model and Ising Machines -- 3 Oscillator-Based Ising Machines -- 3.1 Phase Oscillators -- 3.2 Second-Harmonic Injection Locking -- 3.3 Kuramoto Model -- 3.4 Gradient Structure of the Kuramoto Model -- 3.5 Working Principle of OIMs -- 4 Experiments -- 4.1 MAX-CUT Problem -- 4.2 Experimental Setting and Evaluation Metrics -- 4.3 Effect of Properties of Noisy Fluctuation and Coarse Time Discretization -- 4.4 Effect of Injection and Noise Strength -- 4.5 Effect of Higher Harmonics in Coupling and Injection Schemes -- 5 Summary -- References -- Sampling-Like Dynamics of the Nonlinear Dynamical System Combined with Optimization -- 1 Herding: Sample Generation Using Nonlinear Dynamics and Monte Carlo Integration -- 1.1 Deterministic Tossup -- 1.2 Herding -- 1.3 Convergence of Herding -- 2 Herded Gibbs: Model-Based Herding on Spin System -- 2.1 Markov Chain Monte Carlo Method -- 2.2 Herded Gibbs -- 2.3 Sharing Weight Variables -- 2.4 Monte Carlo Integration Using HG -- 2.5 Rao-Blackwellization -- 3 Entropic Herding: Regularizer for Dynamical Sampling -- 3.1 Maximum Entropy Principle -- 3.2 Entropic Herding -- 3.3 Dynamical Entropic Maximization in Herding -- 3.4 Other Notes on Entropic Herding -- 4 Discussion -- 4.1 Application of Physical Combinatorial Optimization Using Herding -- 4.2 Generative Models and Herding -- 5 Conclusion -- References -- Photonic Reservoir Computing -- Reservoir Computing Based on Iterative Function Systems -- 1 Introduction -- 2 Iterative Function Systems -- 3 Iterative Function System-Based Reservoir Computing -- 4 Prediction Performance of IFSRC -- 4.1 Multi-step Ahead Prediction -- 4.2 Single-Step Ahead Prediction of Santa Fe Time-Series Data.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5 Experimental Performance of IFS Reservoir Computing -- 5.1 Optical Setup -- 5.2 Multi-step Ahead Prediction of Mackey-Glass Equation -- 5.3 Single-step Ahead Prediction of Santa Fe Time-Series Data -- 6 Relationship Between Performance and Spectral Radius -- 7 IFS Reservoir Computing with Optical Feedback Loop -- 8 Discussion -- References -- Bridging the Gap Between Reservoirs and Neural Networks -- 1 Introduction -- 2 Background -- 2.1 Lottery Ticket Hypotheses -- 2.2 Residual Neural Networks -- 3 Hidden-Fold Networks -- 3.1 Folded ResNet Architecture -- 3.2 Supermask Training -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Results -- 5 Summary -- References -- Brain-Inspired Reservoir Computing Models -- 1 Introduction -- 2 Reservoir-Based Predictive Coding Model -- 3 Reservoir-Based Reinforcement Learning Model -- 4 Integrated Model and Mental simulation -- 5 Summary -- References.</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources.</subfield></datafield><datafield tag="590" ind1=" " ind2=" "><subfield code="a">Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. </subfield></datafield><datafield tag="655" ind1=" " ind2="4"><subfield code="a">Electronic books.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tanida, Jun.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hashimoto, Masanori.</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Suzuki, Hideyuki</subfield><subfield code="t">Photonic Neural Networks with Spatiotemporal Dynamics</subfield><subfield code="d">Singapore : Springer,c2023</subfield><subfield code="z">9789819950713</subfield></datafield><datafield tag="797" ind1="2" ind2=" "><subfield code="a">ProQuest (Firm)</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30882937</subfield><subfield code="z">Click to View</subfield></datafield></record></collection> |