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Updated: Oct 28, 2025

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
Published on: July 21, 2021
Novel Causal Relations between Neuronal Networks due to Synchronization.
Sentao Wang1, Hongbiao Chen1, Yang Zhan1
1Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Key Laboratory of Brain Connectome and Manipulation, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Key Laboratory of Translational Research for Brain Diseases, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.
Silent neuronal networks can surprisingly exert strong causal influences on activated networks. This occurs due to high synchronization and lower noise levels within the silent network, impacting information flow in interconnected systems.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Network Science
Background:
- Information transmission typically flows from activated to silent neuronal networks.
- The role of silent networks in influencing information flow is not well understood.
Purpose of the Study:
- To investigate the causal influence of silent neuronal networks on interconnected networks.
- To explore the dynamics underlying information transmission in multi-network systems.
Main Methods:
- Numerical simulations of a three-network motif.
- Analysis of network synchronization and noise levels.
Main Results:
- Silent neuronal networks can exert significant causal influence on activated networks.
- High synchronization within a silent network enhances its causal impact.
- Lower noise levels in a network correlate with its predominant influence.
Conclusions:
- Internal network dynamics, particularly synchronization and noise, critically affect information flow.
- Rethinking the directionality of information transmission in interconnected neuronal systems is necessary.
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