Coherence resonance and stochastic synchronization in a small-world neural network: an interplay in the presence of spike-timing-dependent plasticity

Nonlinear Dynamics, Journal Year: 2023, Volume and Issue: 111(8), P. 7789 - 7805

Published: Jan. 12, 2023

Language: Английский

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Identification of Influential Nodes in Complex Networks With Degree and Average Neighbor Degree

IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Journal Year: 2023, Volume and Issue: 13(3), P. 734 - 742

Published: June 7, 2023

Identifying influential nodes in complex networks is vital for understanding their structure and dynamic behavior. Although methods based on a single characteristic of have been demonstrated to be effective specific scenarios, the information they provide dealing with global aspect network often limited or incomplete. In this paper, we present measure that combines degree average neighbor evaluate influence nodes. As supplement, also propose corresponding gravity index. Experiments synthetic real-world show method proposed paper superior previous most scenarios. Our strategy highly competitive compared current famous method.

Language: Английский

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Energy function for some maps and nonlinear oscillators

Applied Mathematics and Computation, Journal Year: 2023, Volume and Issue: 463, P. 128379 - 128379

Published: Oct. 4, 2023

Language: Английский

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Dynamics of neuron-like excitable Josephson junctions coupled by a metal oxide memristive synapse

Nonlinear Dynamics, Journal Year: 2023, Volume and Issue: 111(14), P. 13481 - 13497

Published: May 3, 2023

Language: Английский

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Transfer Learning With Optimal Transportation and Frequency Mixup for EEG-Based Motor Imagery Recognition

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2022, Volume and Issue: 30, P. 2866 - 2875

Published: Jan. 1, 2022

Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a degenerate performance due to the considerable individual variability. To address this problem, we develop novel domain adaptation method with optimal transport and frequency mixup for cross-subject transfer learning in motor imagery BCIs. Specifically, preprocessed EEG signals from source target are mapped into latent space an embedding module, where representation distributions label across domains large discrepancy. We assume that there exists non-linear coupling matrix between both domains, which can be utilized estimate distance of joint different domains. Depending on transport, Wasserstein is minimized, yielding alignment distributions. Moreover, new strategy also introduced generalize model, inputs trials mixed rather than raw space. The extensive experiments three evaluation benchmarks conducted validate proposed framework. All results demonstrate our achieves superior previous state-of-the-art approaches.

Language: Английский

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From Isles of Königsberg to Islets of Langerhans: Examining the Function of the Endocrine Pancreas Through Network Science

Frontiers in Endocrinology, Journal Year: 2022, Volume and Issue: 13

Published: June 15, 2022

Islets of Langerhans are multicellular microorgans located in the pancreas that play a central role whole-body energy homeostasis. Through secretion insulin and other hormones they regulate postprandial storage interprandial usage energy-rich nutrients. In these clusters hormone-secreting endocrine cells, intricate cell-cell communication is essential for proper function. Electrical coupling between insulin-secreting beta cells through gap junctions composed connexin36 particularly important, as it provides required, most basis coordinated responses cell population. The increasing evidence gap-junctional its modulation vital to well-regulated has stimulated immense interest how subpopulations heterogeneous functionally arranged throughout islets mediate intercellular signals. last decade, several novel techniques have been proposed assess cooperation islets, including prosperous combination imaging network science. present contribution, we review recent advances related application complex approaches uncover functional connectivity patterns among within islets. We first provide an accessible introduction basic principles theory, enumerating measures characterizing interactions quantifying integration segregation system. Then describe methodological construct networks, point out possible pitfalls, specify implications examinations. continue by highlighting findings obtained advanced supported network-based analyses, giving special emphasis current developments both mouse human well outlining challenges offered multilayer formalism exploring collective activity islet populations. Finally, emphasize analyses does not only represent innovative concept can be used interpret physiology but also fertile ground delineating normal from pathological function changes networks associated with development diabetes mellitus.

Language: Английский

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Energy-guided synapse coupling between neurons under noise

Journal of Biological Physics, Journal Year: 2023, Volume and Issue: 49(1), P. 49 - 76

Published: Jan. 14, 2023

Language: Английский

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Synchronization in repulsively coupled oscillators

Physical review. E, Journal Year: 2023, Volume and Issue: 107(1)

Published: Jan. 3, 2023

A long-standing expectation is that two repulsively coupled oscillators tend to oscillate in opposite directions. It has been difficult achieve complete synchrony identical with purely repulsive coupling. Here, we introduce a general coupling condition based on the linear matrix of dynamical systems for emergence synchronization pure oscillators. The proposed profiles (coupling matrices) define bidirectional cross-coupling link plays role indicator onset between We illustrate scheme several paradigmatic two-coupled chaotic and validate its effectiveness through stability analysis synchronous solution master function approach. further demonstrate selection even works perfectly large ensemble

Language: Английский

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A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks

IEEE Transactions on Cognitive and Developmental Systems, Journal Year: 2023, Volume and Issue: 16(2), P. 544 - 558

Published: May 26, 2023

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning realistic brain also utilizes intrinsic non-synaptic mechanisms neurons. The spike threshold biological neurons is a critical neuronal feature that exhibits rich dynamics millisecond timescale and has been proposed as an underlying mechanism facilitates information processing. In this study, we develop novel synergistic approach involves simultaneously weights thresholds SNNs. trained with synapse-threshold (STL-SNNs) achieve significantly superior performance static neuromorphic datasets than two degenerated single-learning models. During training, optimizes thresholds, providing network stable signal transmission via appropriate firing rates. Further analysis indicates STL-SNNs robust to noisy data exhibit low energy consumption deep structures. Additionally, STL-SNN can be further improved by introducing generalized joint decision framework. Overall, our findings indicate biologically plausible synergies between may provide promising developing highly efficient SNN methods.

Language: Английский

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A potential mechanism for Gibsonian resonance: behavioral entrainment emerges from local homeostasis in an unsupervised reservoir network

Cognitive Neurodynamics, Journal Year: 2023, Volume and Issue: 18(4), P. 1811 - 1834

Published: July 24, 2023

Language: Английский

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