Microcircuit Synchronization and Heavy-Tailed Synaptic Weight Distribution Augment preBötzinger Complex Bursting...

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Respiratory Physiology

Background:

• The preBötzinger Complex (preBötC) generates the respiratory rhythm, essential for mammalian breathing.
• Existing models struggle to explain the robustness and flexibility of preBötC rhythm generation.
• Network synchronization is experimentally identified as the critical mechanism for initiating inspiratory bursts.

Purpose of the Study:

• To investigate the network dynamics underlying inspiratory burst initiation in the preBötC.
• To model preBötC synchronization using experimentally derived parameters.
• To identify key factors contributing to the robustness and flexibility of respiratory rhythm.

Main Methods:

• Development of minimal microcircuit models of the preBötC.
• Construction of physiologically plausible neural network graphs with 1000 excitatory neurons.
• Analysis using graph theory and machine learning to examine synchronization and attractor dynamics.

Main Results:

• Directed Erdős-Rényi graphs with lognormal synaptic weight distributions best replicated experimental burst dynamics.
• Input convergence of efferent connectivity predicted synchronization, particularly at the next-nearest neighbor level.
• Synaptic heterogeneity was crucial for robust and flexible preBötC attractor dynamics.

Conclusions:

• Synaptic heterogeneity, characterized by broad (lognormal) weight distributions, is vital for preBötC synchronization and rhythm generation.
• These findings suggest a ubiquitous computational motif for temporal processing and decision-making in neural systems.
• The study highlights synaptic heterogeneity as a key factor in the reliability and responsiveness of the respiratory network.