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Emergence of phase clusters and coexisting states reveals the structure-function relationship
Dong Yu1, Yong Wu1, Qianming Ding1
1Department of Physics and Institute of Biophysics, <a href="https://ror.org/03x1jna21">Central China Normal University</a>, Wuhan 430079, China.
Physical Review. E
|June 22, 2024
Summary
This study reveals how brain structure and function are dynamically linked. Adaptive neural networks show that structural organization, like modularity, leads to specific collective behaviors and functional states.
Area of Science:
- Computational neuroscience
- Network science
- Complex systems
Background:
- The Brain Connectome Project highlights the importance of brain structure.
- Understanding the relationship between neural structure and emergent function is a key challenge.
- Adaptive neural networks offer a model to study this dynamic interplay.
Purpose of the Study:
- To investigate the relationship between structural organization and functional dynamics in an adaptive neural network.
- To explore how synaptic plasticity rules influence network behavior.
- To identify structural correlates of emergent functional states.
Main Methods:
- Simulated an adaptive neural network with spike-time dependent synaptic plasticity.
- Varied the plasticity boundary to observe network dynamics.
- Applied graph theory to analyze network structure and community detection.
- Investigated relationships between modularity, core-periphery structure, and collective behaviors.
Main Results:
- The network exhibited diverse collective behaviors: phase synchronization, phase locking, hierarchical synchronization (phase clusters), and coexisting states.
- Hierarchical synchronization correlated with community structure (modules).
- Coexisting states were linked to hierarchical self-organization and core-periphery structures.
- Sparsely connected modules formed phase clusters; core-periphery structures emerged with coexisting states.
Conclusions:
- Neural function emerges dynamically from underlying structure.
- Structure is not static but is influenced by function in a complex feedback loop.
- Demonstrated an equivalence between structure and function in adaptive neural systems.
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