ArticleScientific reports2021
Lag synchronization of coupled time-delayed FitzHugh-Nagumo neural networks via feedback control.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 44 citations in OpenAlex.
- Parameter estimation of the network of FitzHugh-Nagumo neurons based on the speed-gradient and filtering.Journal of computational neuroscience · 2025Article
- Imperfect chimera and synchronization in a hybrid adaptive conductance based exponential integrate and fire neuron model.Cognitive neurodynamics · 2024Article
- Synchronization in fractional-order neural networks by the energy balance strategy.Cognitive neurodynamics · 2024Article
- Article
- Strong delayed negative feedback.Frontiers in network physiology · 2024Article
- Dynamical behaviors, circuit design, and synchronization of a novel symmetric chaotic system with coexisting attractors.Scientific reports · 2023Article
- Phase synchronization and measure of criticality in a network of neural mass models.Scientific reports · 2022Article
- Lag Synchronization of Noisy and Nonnoisy Multiple Neurobiological Coupled FitzHugh-Nagumo Networks with and without Delayed Coupling.Computational intelligence and neuroscience · 2022Article
- A Frequency Domain Analysis of the Excitability and Bifurcations of the FitzHugh-Nagumo Neuron Model.The journal of physical chemistry letters · 2021Article
- An adaptive approach to machine learning for compact particle accelerators.Scientific reports · 2021Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synchronization plays a significant role in information transfer and decision-making by neurons and brain neural networks. The development of control strategies for synchronizing a network of chaotic neurons with time delays, different direction-dependent coupling (unidirectional and bidirectional), and noise, particularly under external disturbances, is an essential and very challenging task. Researchers have extensively studied the synchronization mechanism of two coupled time-delayed neurons with bidirectional coupling and without incorporating the effect of noise, but not for time-delayed neural networks. To overcome these limitations, this study investigates the synchronization problem in a network of coupled FitzHugh-Nagumo (FHN) neurons by incorporating time delays, different direction-dependent coupling (unidirectional and bidirectional), noise, and ionic and external disturbances in the mathematical models. More specifically, this study investigates the synchronization of time-delayed unidirectional and bidirectional ring-structured FHN neuronal systems with and without external noise. Different gap junctions and delay parameters are used to incorporate time-delay dynamics in both neuronal networks. We also investigate the influence of the time delays between connected neurons on synchronization conditions. Further, to ensure the synchronization of the time-delayed FHN neuronal networks, different adaptive control laws are proposed for both unidirectional and bidirectional neuronal networks. In addition, necessary and sufficient conditions to achieve synchronization are provided by employing the Lyapunov stability theory. The results of numerical simulations conducted for different-sized multiple networks of time-delayed FHN neurons verify the effectiveness of the proposed adaptive control schemes.
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