Exotic states induced by coevolving connection weights and phases in complex networks

Thamizharasan, S.; Chandrasekar, V. K.; Senthilvelan, M.; Berner, Rico; Schoell, Eckehard; Senthilkumar, D. V.

doi:10.1103/physreve.105.034312

Cited by 14 publications

(3 citation statements)

References 66 publications

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“…III C. While these two types of adaptation, affecting the coupling or nodal dynamics, may appear independently, it is also not uncommon that they act in concert guiding the system's self-organization. 32,33 So far, most of the systematic insights on the role of adaptation have been gained regarding its impact on synchronization, including how it gives rise to different states of (partial) synchrony, 16,21,[34][35][36][37] or the way it modifies the order of synchronization transition 28 and the associated nucleation process. 38 Another active branch of research concerns adaptation as a general control mechanism, establishing its role in inducing critical transitions 30,31 and triggering of alternating or cyclic activity patterns.…”

Section: B Adaptation Slow Feedback and Noise-by Igor Franovi ćmentioning

confidence: 99%

Perspectives on adaptive dynamical systems

Sawicki

Berner

Loos

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.

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Section: B Adaptation Slow Feedback and Noise-by Igor Franovi ćmentioning

confidence: 99%

Perspectives on adaptive dynamical systems

Sawicki

Berner

Loos

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, the Kuramoto model has been extended to study synchronization on static 17,28,29 , temporal 30 and adaptive networks [31][32][33][34][35][36][37][38] . Despite the simple structure, extended Kuramoto models can exhibit many different dynamical regimes such as solitary [39][40][41][42] and chimera states [43][44][45][46][47] , and sophisticated methods have been developed for their analysis 48 .…”

Section: Introductionmentioning

confidence: 99%

Synchronization transitions in Kuramoto networks with higher-mode interaction

Berner¹,

Lu²,

Sokolov³

2023

Preprint

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“…The Kuramoto model of phase oscillators is extensively used for understanding a plethora of collective dynamics phenomena 46 – 49 . Here we implement a network of phase oscillator model neurons with SP and a standard additive STDP to study the collective dynamics that results from the interplay of these distinct adaptive mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of phase oscillator networks with synaptic weight and structural plasticity

Chauhan

Khaledi-Nasab

Neiman

et al. 2022

Sci Rep

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We study the dynamics of Kuramoto oscillator networks with two distinct adaptation processes, one varying the coupling strengths and the other altering the network structure. Such systems model certain networks of oscillatory neurons where the neuronal dynamics, synaptic weights, and network structure interact with and shape each other. We model synaptic weight adaptation with spike-timing-dependent plasticity (STDP) that runs on a longer time scale than neuronal spiking. Structural changes that include addition and elimination of contacts occur at yet a longer time scale than the weight adaptations. First, we study the steady-state dynamics of Kuramoto networks that are bistable and can settle in synchronized or desynchronized states. To compare the impact of adding structural plasticity, we contrast the network with only STDP to one with a combination of STDP and structural plasticity. We show that the inclusion of structural plasticity optimizes the synchronized state of a network by allowing for synchronization with fewer links than a network with STDP alone. With non-identical units in the network, the addition of structural plasticity leads to the emergence of correlations between the oscillators’ natural frequencies and node degrees. In the desynchronized regime, the structural plasticity decreases the number of contacts, leading to a sparse network. In this way, adding structural plasticity strengthens both synchronized and desynchronized states of a network. Second, we use desynchronizing coordinated reset stimulation and synchronizing periodic stimulation to induce desynchronized and synchronized states, respectively. Our findings indicate that a network with a combination of STDP and structural plasticity may require stronger and longer stimulation to switch between the states than a network with STDP only.

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Exotic states induced by coevolving connection weights and phases in complex networks

Cited by 14 publications

References 66 publications

Perspectives on adaptive dynamical systems

Perspectives on adaptive dynamical systems

Synchronization transitions in Kuramoto networks with higher-mode interaction

Dynamics of phase oscillator networks with synaptic weight and structural plasticity

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