Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience

Area of Science:

• Neuroscience
• Network Science
• Computational Neuroscience

Background:

• Network connectivity is crucial for understanding neural activity dynamics.
• Graph-theoretic concepts from network science offer a framework for analysis.
• Interpreting these concepts requires careful consideration of the underlying neural dynamics.

Purpose of the Study:

• To review recent work linking network connectivity and neural activity dynamics.
• To highlight the importance of dynamics in interpreting network structures.
• To emphasize the role of theoretical and computational neuroscience.

Main Methods:

• Literature review of recent studies.
• Analysis of graph-theoretic measures in the context of neural dynamics.
• Discussion of the limitations of traditional network models for neuroscience.

Main Results:

• Graph-theoretic measures' relevance is highly dependent on network dynamics.
• Linear dynamics models may not fully capture neural network behavior.
• Nonlinear dynamics are essential for understanding neural network function.

Conclusions:

• Theoretical and computational neuroscience are vital for bridging network science and neural dynamics.
• A dynamic-sensitive approach is needed to interpret network connectivity in neuroscience.
• Future research should focus on nonlinear dynamics in neural networks.