arXiv - PHYS - Adaptation and Self-Organizing Systems最新文献_第4页

Metastability of multi-population Kuramoto-Sakaguchi oscillators 多群体仓本坂口振荡器的转移性

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-05-24 DOI: arxiv-2405.15396

Bojun Li, Nariya Uchida

{"title":"Metastability of multi-population Kuramoto-Sakaguchi oscillators","authors":"Bojun Li, Nariya Uchida","doi":"arxiv-2405.15396","DOIUrl":"https://doi.org/arxiv-2405.15396","url":null,"abstract":"An Ott-Antonsen reduced $M$-population of Kuramoto-Sakaguchi oscillators is\u0000investigated, focusing on the influence of the phase-lag parameter $alpha$ on\u0000the collective dynamics. For oscillator populations coupled on a ring, we\u0000obtained a wide variety of spatiotemporal patterns, including coherent states,\u0000traveling waves, partially synchronized states, modulated states, and\u0000incoherent states. Back-and-forth transitions between these states are found,\u0000which suggest metastability. Linear stability analysis reveals the stable\u0000regions of coherent states with different winding numbers $q$. Within certain\u0000$alpha$ ranges, the system settles into stable traveling wave solutions\u0000despite the coherent states also being linearly stable. For around $alpha\u0000approx 0.46pi$, the system displays the most frequent metastable transitions\u0000between coherent states and partially synchronized states, while for $alpha$\u0000closer to $pi/2$, metastable transitions arise between partially synchronized\u0000states and modulated states. This model captures metastable dynamics akin to\u0000brain activity, offering insights into the synchronization of brain networks.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141169743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synchronization through frequency shuffling 通过频率洗牌实现同步

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-05-22 DOI: arxiv-2405.13569

Manaoj Aravind, Vaibhav Pachaulee, Mrinal Sarkar, Ishant Tiwari, Shamik Gupta, P. Parmananda

引用次数: 0

Phase holonomy underlies puzzling temporal patterns in Kuramoto models with two sub-populations 相位整体性是具有两个子种群的仓本模型中令人费解的时间模式的基础

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-05-15 DOI: arxiv-2405.09696

Aladin Crnkić, Vladimir Jaćimović

引用次数: 0

Multiple stochastic resonances and inverse stochastic resonances in asymmetric bistable system under the ultra-high frequency excitation 超高频激励下非对称双稳态系统中的多重随机共振和反随机共振

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-05-13 DOI: arxiv-2405.07804

Cong Wang, Zhongqiu Wang, Jianhua Yang, Miguel A. F. Sanjuán, Gong Tao, Zhen Shan, Mengen Shen

{"title":"Multiple stochastic resonances and inverse stochastic resonances in asymmetric bistable system under the ultra-high frequency excitation","authors":"Cong Wang, Zhongqiu Wang, Jianhua Yang, Miguel A. F. Sanjuán, Gong Tao, Zhen Shan, Mengen Shen","doi":"arxiv-2405.07804","DOIUrl":"https://doi.org/arxiv-2405.07804","url":null,"abstract":"Ultra-high frequency linear frequency modulation (UHF-LFM) signal, as a kind\u0000of typical non-stationary signal, has been widely used in microwave radar and\u0000other fields, with advantages such as long transmission distance, strong\u0000anti-interference ability, and wide bandwidth. Utilizing optimal dynamics\u0000response has unique advantages in weak feature identification under strong\u0000background noise. We propose a new stochastic resonance method in an asymmetric\u0000bistable system with the time-varying parameter to handle this special\u0000non-stationary signal. Interestingly, the nonlinear response exhibits multiple\u0000stochastic resonances (MSR) and inverse stochastic resonances (ISR) under\u0000UHF-LFM signal excitation, and some resonance regions may deviate or collapse\u0000due to the influence of system asymmetry. In addition, we analyze the responses\u0000of each resonance region and the mechanism and evolution law of each resonance\u0000region in detail. Finally, we significantly expand the resonance region within\u0000the parameter range by optimizing the time scale, which verifies the\u0000effectiveness of the proposed time-varying scale method. The mechanism and\u0000evolution law of MSR and ISR will provide references for researchers in related\u0000fields.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"160 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mechanical memories in solids, from disorder to design 固体中的机械记忆，从无序到设计

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-05-13 DOI: arxiv-2405.08158

Joseph D. Paulsen, Nathan C. Keim

引用次数: 0

Adaptive control of recurrent neural networks using conceptors 利用概念器对递归神经网络进行自适应控制

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-05-12 DOI: arxiv-2405.07236

Guillaume Pourcel, Mirko Goldmann, Ingo Fischer, Miguel C. Soriano

{"title":"Adaptive control of recurrent neural networks using conceptors","authors":"Guillaume Pourcel, Mirko Goldmann, Ingo Fischer, Miguel C. Soriano","doi":"arxiv-2405.07236","DOIUrl":"https://doi.org/arxiv-2405.07236","url":null,"abstract":"Recurrent Neural Networks excel at predicting and generating complex\u0000high-dimensional temporal patterns. Due to their inherent nonlinear dynamics\u0000and memory, they can learn unbounded temporal dependencies from data. In a\u0000Machine Learning setting, the network's parameters are adapted during a\u0000training phase to match the requirements of a given task/problem increasing its\u0000computational capabilities. After the training, the network parameters are kept\u0000fixed to exploit the learned computations. The static parameters thereby render\u0000the network unadaptive to changing conditions, such as external or internal\u0000perturbation. In this manuscript, we demonstrate how keeping parts of the\u0000network adaptive even after the training enhances its functionality and\u0000robustness. Here, we utilize the conceptor framework and conceptualize an\u0000adaptive control loop analyzing the network's behavior continuously and\u0000adjusting its time-varying internal representation to follow a desired target.\u0000We demonstrate how the added adaptivity of the network supports the\u0000computational functionality in three distinct tasks: interpolation of temporal\u0000patterns, stabilization against partial network degradation, and robustness\u0000against input distortion. Our results highlight the potential of adaptive\u0000networks in machine learning beyond training, enabling them to not only learn\u0000complex patterns but also dynamically adjust to changing environments,\u0000ultimately broadening their applicability.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140935703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of pulse exposure on chimera state in ensemble of FitzHugh-Nagumo systems 脉冲暴露对 FitzHugh-Nagumo 系统集合中嵌合体状态的影响

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-05-10 DOI: arxiv-2405.06833

Elena Rybalova, Nadezhda Semenova

引用次数: 0

A simple theory for training response of deep neural networks 深度神经网络响应训练的简单理论

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-05-07 DOI: arxiv-2405.04074

Kenichi Nakazato

引用次数: 0

Synchrony for weak coupling in the complexified Kuramoto model 复合仓本模型中弱耦合的同步性

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-04-28 DOI: arxiv-2404.19637

Moritz Thümler, Shesha G. M. Srinivas, Malte Schröder, Marc Timme

引用次数: 0

A Central Pattern Generator Network for Simple Control of Gait Transitions in Hexapod Robots based on Phase Reduction 基于相位还原的六足机器人步态转换简易控制中央模式发生器网络

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-04-26 DOI: arxiv-2404.17139

Norihisa Namura, Hiroya Nakao

引用次数: 0