Optimal Interplay between Synaptic Strengths and Network Structure Enhances Activity Fluctuations and Information...

Area of Science:

• Computational neuroscience
• Network science
• Information theory

Background:

• Understanding activity propagation in spiking neural networks is crucial for neuroscience.
• The interplay between network structure and synaptic parameters remains an open problem.

Purpose of the Study:

• To investigate activity propagation in spiking networks with hierarchical modular topology using an information-theoretical approach.
• To determine how synaptic strength and modularity influence information flow at both pairwise and population levels.

Main Methods:

• Utilized an information-theoretical framework to analyze activity propagation.
• Modeled spiking neural networks with hierarchical modular topology.
• Systematically varied global synaptic strength and the number of modules.

Main Results:

• Optimized pairwise information propagation is achieved by increasing synaptic strength or module number.
• Population information propagation shows an optimal interplay between synaptic strength and modularity, peaking at a specific module count.
• Increased cross-correlations among neuron pairs correlate with enhanced network information propagation.

Conclusions:

• Network topology and synaptic strength critically influence information transmission in cortical networks.
• Distinct mechanisms underlie pairwise versus population information propagation.
• Results suggest specific roles for modularity and synaptic strength in neural information processing.