Suppr超能文献

延迟动力系统:网络、奇异态与储层计算。

Delayed dynamical systems: networks, chimeras and reservoir computing.

作者信息

Hart Joseph D, Larger Laurent, Murphy Thomas E, Roy Rajarshi

机构信息

Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD 20742, USA.

Department of Physics, University of Maryland, College Park, MD 20742, USA.

出版信息

Philos Trans A Math Phys Eng Sci. 2019 Sep 9;377(2153):20180123. doi: 10.1098/rsta.2018.0123. Epub 2019 Jul 22.

Abstract

We present a systematic approach to reveal the correspondence between time delay dynamics and networks of coupled oscillators. After early demonstrations of the usefulness of spatio-temporal representations of time-delay system dynamics, extensive research on optoelectronic feedback loops has revealed their immense potential for realizing complex system dynamics such as chimeras in rings of coupled oscillators and applications to reservoir computing. Delayed dynamical systems have been enriched in recent years through the application of digital signal processing techniques. Very recently, we have showed that one can significantly extend the capabilities and implement networks with arbitrary topologies through the use of field programmable gate arrays. This architecture allows the design of appropriate filters and multiple time delays, and greatly extends the possibilities for exploring synchronization patterns in arbitrary network topologies. This has enabled us to explore complex dynamics on networks with nodes that can be perfectly identical, introduce parameter heterogeneities and multiple time delays, as well as change network topologies to control the formation and evolution of patterns of synchrony. This article is part of the theme issue 'Nonlinear dynamics of delay systems'.

摘要

我们提出了一种系统方法来揭示时间延迟动力学与耦合振子网络之间的对应关系。在早期证明了时间延迟系统动力学的时空表示的有用性之后,对光电反馈回路的广泛研究揭示了它们在实现复杂系统动力学方面的巨大潜力,例如耦合振子环中的奇异态以及在储层计算中的应用。近年来,通过数字信号处理技术的应用,延迟动力系统得到了丰富。最近,我们表明通过使用现场可编程门阵列,可以显著扩展能力并实现具有任意拓扑结构的网络。这种架构允许设计合适的滤波器和多个时间延迟,并极大地扩展了在任意网络拓扑中探索同步模式的可能性。这使我们能够在具有完全相同节点的网络上探索复杂动力学,引入参数异质性和多个时间延迟,以及改变网络拓扑以控制同步模式的形成和演化。本文是主题为“延迟系统的非线性动力学”的一部分。

相似文献

1
Delayed dynamical systems: networks, chimeras and reservoir computing.
Philos Trans A Math Phys Eng Sci. 2019 Sep 9;377(2153):20180123. doi: 10.1098/rsta.2018.0123. Epub 2019 Jul 22.
2
Experiments with arbitrary networks in time-multiplexed delay systems.
Chaos. 2017 Dec;27(12):121103. doi: 10.1063/1.5016047.
3
Complex Dynamical Networks Constructed with Fully Controllable Nonlinear Nanomechanical Oscillators.
Nano Lett. 2017 Oct 11;17(10):5977-5983. doi: 10.1021/acs.nanolett.7b02026. Epub 2017 Sep 21.
4
Persistent Memory in Single Node Delay-Coupled Reservoir Computing.
PLoS One. 2016 Oct 26;11(10):e0165170. doi: 10.1371/journal.pone.0165170. eCollection 2016.
5
Complexity in electro-optic delay dynamics: modelling, design and applications.
Philos Trans A Math Phys Eng Sci. 2013 Aug 19;371(1999):20120464. doi: 10.1098/rsta.2012.0464. Print 2013 Sep 28.
6
Complex partial synchronization patterns in networks of delay-coupled neurons.
Philos Trans A Math Phys Eng Sci. 2019 Sep 9;377(2153):20180128. doi: 10.1098/rsta.2018.0128. Epub 2019 Jul 22.
7
Chimeras in globally coupled oscillators: A review.
Chaos. 2023 Sep 1;33(9). doi: 10.1063/5.0143872.
8
Constructing polynomial libraries for reservoir computing in nonlinear dynamical system forecasting.
Phys Rev E. 2024 Feb;109(2-1):024227. doi: 10.1103/PhysRevE.109.024227.
9
Fast physical repetitive patterns generation for masking in time-delay reservoir computing.
Sci Rep. 2021 Mar 23;11(1):6701. doi: 10.1038/s41598-021-86150-0.
10
Harnessing synthetic active particles for physical reservoir computing.
Nat Commun. 2024 Jan 29;15(1):774. doi: 10.1038/s41467-024-44856-5.

引用本文的文献

1
Deriving task specific performance from the information processing capacity of a reservoir computer.
Nanophotonics. 2022 Oct 3;12(5):937-947. doi: 10.1515/nanoph-2022-0415. eCollection 2023 Mar.
2
Reservoir computing with noise.
Chaos. 2023 Apr 1;33(4). doi: 10.1063/5.0130278.
3
Smallest Chimeras Under Repulsive Interactions.
Front Netw Physiol. 2021 Dec 21;1:778597. doi: 10.3389/fnetp.2021.778597. eCollection 2021.
6
Colocalized Sensing and Intelligent Computing in Micro-Sensors.
Sensors (Basel). 2020 Nov 6;20(21):6346. doi: 10.3390/s20216346.
7
First-order synchronization transition in a large population of strongly coupled relaxation oscillators.
Sci Adv. 2020 Sep 23;6(39). doi: 10.1126/sciadv.abb2637. Print 2020 Sep.
8
Nonlinear dynamics of delay systems: an overview.
Philos Trans A Math Phys Eng Sci. 2019 Sep 9;377(2153):20180389. doi: 10.1098/rsta.2018.0389. Epub 2019 Jul 22.

本文引用的文献

1
Topological Control of Synchronization Patterns: Trading Symmetry for Stability.
Phys Rev Lett. 2019 Feb 8;122(5):058301. doi: 10.1103/PhysRevLett.122.058301.
3
Symmetry- and input-cluster synchronization in networks.
Phys Rev E. 2018 Apr;97(4-1):042217. doi: 10.1103/PhysRevE.97.042217.
4
Evidence of a Critical Phase Transition in Purely Temporal Dynamics with Long-Delayed Feedback.
Phys Rev Lett. 2018 Apr 27;120(17):173901. doi: 10.1103/PhysRevLett.120.173901.
5
Laminar Chaos.
Phys Rev Lett. 2018 Feb 23;120(8):084102. doi: 10.1103/PhysRevLett.120.084102.
6
Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach.
Phys Rev Lett. 2018 Jan 12;120(2):024102. doi: 10.1103/PhysRevLett.120.024102.
7
Experiments with arbitrary networks in time-multiplexed delay systems.
Chaos. 2017 Dec;27(12):121103. doi: 10.1063/1.5016047.
8
Introduction to Focus Issue: Time-delay dynamics.
Chaos. 2017 Nov;27(11):114201. doi: 10.1063/1.5011354.
10
Network neuroscience.
Nat Neurosci. 2017 Feb 23;20(3):353-364. doi: 10.1038/nn.4502.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验