Structure-dynamics relationships in bursting neuronal networks revealed using a prediction framework.

Area of Science:

• Computational Neuroscience
• Network Science
• Systems Biology

Background:

• Understanding the relationship between neuronal network structure and function is crucial in neuroscience.
• Previous studies often examine limited network structures and structural measures, hindering a comprehensive understanding.
• The impact of diverse network topologies on neural dynamics remains an active area of investigation.

Purpose of the Study:

• To identify key structural aspects that significantly influence neuronal network excitability.
• To compare the predictive power of various graph-theoretic measures for network activity properties.
• To develop a generalizable framework for structure-dynamics studies in biological systems.

Main Methods:

• Simulated neuronal network activity using two point-neuron models with chemical synapses.
• Collected statistics on emergent network bursts (count, length) across diverse network topologies.
• Employed a prediction framework using graph-theoretic measures to estimate activity properties and compared predictor performance.

Main Results:

• For networks with a sharp in-degree distribution, the clustering coefficient most accurately predicted activity properties.
• For networks with a broad in-degree distribution, the maximum eigenvalue of the connectivity graph provided the best prediction.
• These findings were robust across different neuron models, population choices, average degrees, and network sizes.

Conclusions:

• Different aspects of network structure are relevant for predicting network excitability.
• The choice of structural measure for prediction depends on the network's in-degree distribution characteristics.
• The integrative prediction framework offers a valuable tool for structure-dynamics research in biosciences.