Stabilizing synchrony by inhomogeneity

Bolhasani, Ehsan; Valizadeh, Alireza

doi:10.1038/srep13854

Cited by 6 publications

(2 citation statements)

References 69 publications

(94 reference statements)

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“…A different body of work has shown that, for periodic oscillators, heterogeneity can in certain cases facilitate synchronization [27][28][29][30][31][32]. A natural question is then whether a similar effect would be possible for chaotic oscillators despite the fact that their dynamics exhibit sensitive dependence on parameters and that an invariant synchronization manifold no longer exists for nonidentical chaotic oscillators.…”

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confidence: 99%

Synchronizing Chaos with Imperfections

2021

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Previous research on nonlinear oscillator networks has shown that chaos synchronization is attainable for identical oscillators but deteriorates in the presence of parameter mismatches. Here, we identify regimes for which the opposite occurs and show that oscillator heterogeneity can synchronize chaos for conditions under which identical oscillators cannot. This effect is not limited to small mismatches and is observed for random oscillator heterogeneity on both homogeneous and heterogeneous network structures. The results are demonstrated experimentally using networks of Chua's oscillators and are further supported by numerical simulations and theoretical analysis. In particular, we propose a general mechanism based on heterogeneity-induced mode mixing that provides insights into the observed phenomenon. Since individual differences are ubiquitous and often unavoidable in real systems, it follows that such imperfections can be an unexpected source of synchronization stability.

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confidence: 99%

Synchronizing Chaos with Imperfections

2021

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“…These qualitatively different responses to stimulation lead to dramatically different synchronization patterns in neural networks. Previous studies have shown that, type I cells exhibit relatively poor propensity for synchronization under excitatory couplings, while type II cells are synchronized better 11,14,15 .…”

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confidence: 95%

Emergence of global synchronization in directed excitatory networks of type I neurons

Ziaeemehr

Zarei

Sheshbolouki

2020

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Collective behavior of neural networks depends on the cellular and synaptic properties of the neurons. The phase-response curve (PRC) is an experimentally obtainable measure of cellular properties that quantifies the shift in the next spike time of a neuron as a function of the phase at which stimulus delivered to that neuron. The neuronal PRCs can be classified as having either purely positive value (type I) or distinct positive and negative regions (type II). Networks of type 1 PRCs tend not to synchronize via mutual excitatory synaptic connections. We study the synchronization properties of identical type I and type II neurons, assuming excitatory unidirectional synapses. Performing the linear stability analysis and the numerical simulation of the extended Kuramoto model, we show that Feedforward loops favour synchronization of type I neurons, while feedback loops destroy their synchronization tendency. The results are robust to large directed networks constructed from only feedforward or mostly feedback loops, and high synchronization level observed for directed acyclic graphs with type I neurons. The synchronizability of type I neurons depends on both the directionality of the connectivity network and the topology of its undirected backbone. The abundance of feedforward motifs enhances the synchronizability of the directed acyclic graphs.

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