What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Neuroscience
- Statistics
Changsong Zhou spends much of his time researching Complex network, Topology, Synchronization, Phase synchronization and Synchronization of chaos.
His work deals with themes such as Artificial neural network, Synchronization, Network topology, Node and Neuroscience, which intersect with Complex network.
His study in the fields of Kuramoto model under the domain of Synchronization overlaps with other disciplines such as Function.
His Topology study integrates concerns from other disciplines, such as Complex system, Synchronization networks, Control theory and Degree distribution.
Many of his studies on Phase synchronization involve topics that are commonly interrelated, such as Data science.
Changsong Zhou interconnects Attractor and Coherence in the investigation of issues within Synchronization of chaos.
His most cited work include:
- Synchronization in complex networks (2397 citations)
- The synchronization of chaotic systems (1949 citations)
- Network synchronization, diffusion, and the paradox of heterogeneity. (399 citations)
What are the main themes of his work throughout his whole career to date?
Changsong Zhou mainly investigates Topology, Neuroscience, Synchronization, Complex network and Artificial neural network.
His Topology research includes themes of Topology, Control theory and Phase synchronization.
His Control theory research includes elements of Synchronization of chaos and Synchronization.
The various areas that he examines in his Phase synchronization study include Chaotic and Chaotic oscillators.
His study in Neuroscience is interdisciplinary in nature, drawing from both Oscillation and Mechanism.
Subnetwork is closely connected to Network topology in his research, which is encompassed under the umbrella topic of Complex network.
He most often published in these fields:
- Topology (19.31%)
- Neuroscience (18.32%)
- Synchronization (15.35%)
What were the highlights of his more recent work (between 2018-2021)?
- Neuroscience (18.32%)
- Cognitive psychology (10.40%)
- Cognition (11.88%)
In recent papers he was focusing on the following fields of study:
Changsong Zhou mostly deals with Neuroscience, Cognitive psychology, Cognition, Human brain and Structural equation modeling.
His study focuses on the intersection of Neuroscience and fields such as Oscillation with connections in the field of Dopamine.
His Valence study, which is part of a larger body of work in Cognitive psychology, is frequently linked to Perspective, bridging the gap between disciplines.
His Cognition study combines topics in areas such as Genetics, Entropy, Artificial intelligence, Brain activity and meditation and Pattern recognition.
His research in Human brain focuses on subjects like Connectome, which are connected to Neuroimaging.
His Mechanism research incorporates themes from Network topology, Structure, Topology and Work.
Between 2018 and 2021, his most popular works were:
- Hierarchical Connectome Modes and Critical State Jointly Maximize Human Brain Functional Diversity. (22 citations)
- Assessing spatiotemporal variability of brain spontaneous activity by multiscale entropy and functional connectivity. (16 citations)
- Patterns of individual differences in fiber tract integrity of the face processing brain network support neurofunctional models. (8 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Artificial intelligence
- Neuroscience
Changsong Zhou focuses on Cognition, Cognitive psychology, Entropy, Structural equation modeling and Artificial intelligence.
His study of Event-related potential is a part of Cognition.
His Cognitive psychology study incorporates themes from Dominance, Face and Functional specialization.
His Entropy research integrates issues from Wakefulness, Predictive power and Functional connectivity.
His studies deal with areas such as Resting state fMRI, Multi scale entropy and Electroencephalography as well as Structural equation modeling.
The concepts of his Artificial intelligence study are interwoven with issues in Coding, State and Pattern recognition.
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