Information-transfer characteristics in network motifs

Mori, Fumito; Okada, Takashi

doi:10.1103/physrevresearch.5.013037

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…Underwater LiDAR (O-LiDAR) technology for remote sensing is influenced by system parameters, water body optical properties, and environmental factors, affecting the accuracy of underwater transmission characteristics [25]. Lastly, information processing in biological systems, 3 such as gene regulatory networks, is affected by input-signal characteristics and network structural properties, with network motifs playing a significant role in information flow [26].…”

Section: B Signal Transmission Characteristicsmentioning

confidence: 99%

Transmission Lines in Modern Communication Systems: A Systematic Review

Briones,

Fung,

Arboleda

2024

Preprint

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This review aims to comprehensively synthesize the most recent research, collected from reputable databases, focusing specifically on transmission line. It examines advancements and challenges in data transmission. Fiber optics remains dominant, with new fiber designs and signal processing techniques like SDM improving capacity. Researchers are also addressing signal distortion and crosstalk. Beyond fiber optics, the review explores findings in coaxial cables and the promise of graphene for future high-speed transmission. Novel methods for mitigating crosstalk are presented. The findings highlight a trend towards more dependable, effective, and versatile transmission methods, with implications for various industries and electronic systems.

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Section: B Signal Transmission Characteristicsmentioning

confidence: 99%

Transmission Lines in Modern Communication Systems: A Systematic Review

Briones,

Fung,

Arboleda

2024

Preprint

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Finding synchronization state of higher-order motif networks by dynamic learning

Ye,

Wu,

Ding

et al. 2024

Phys. Rev. Research

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Synchronization of action potentials is one of the important phenomena for neural networks to achieve biological functions. How to find the optimal parameter space of neural networks in a synchronized state is of great significance for studying their synchronization. This study explores the parameter space of triplet motif-based higher-order networks in a synchronized state through the dynamic learning of synchronization (DLS) technique, which dynamically modulates the connection weights between motifs to alter their firing patterns. Our study delves into regular, Erdós-Rënyi random graphs, small-world, and scale-free networks, emphasizing the high-order motif interactions that characterize these networks. Our key findings indicate that the DLS technique successfully promotes synchronization within high-order motif networks with various connection patterns, although the degree of synchronization in networks where motifs are interconnected by chemical synapses is slightly weaker than those interconnected by electrical synapses. Additionally, we demonstrate the pattern of weight changes during the regulation of network firing states by DLS, finding that the evolution of weight distributions correlates with the network's topological structure. This work might provide new insights into complex network synchronization and lays the foundation for further exploration of using DLS technology to synchronize higher-order networks through external factors. Published by the American Physical Society 2024

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Information-transfer characteristics in network motifs

Cited by 2 publications

References 59 publications

Transmission Lines in Modern Communication Systems: A Systematic Review

Transmission Lines in Modern Communication Systems: A Systematic Review

Finding synchronization state of higher-order motif networks by dynamic learning

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