Simultaneous silence organizes structured higher-order interactions in neural populations.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Neural activity patterns are shaped by biological constraints.
• Characterizing neural activity involves individual rates, pairwise correlations, and higher-order interactions (HOIs).
• The fundamental properties of HOIs in neural populations remain largely unexplored.

Purpose of the Study:

• To investigate the primary characteristics of higher-order interactions (HOIs) in neural activity.
• To determine if a concise feature can summarize complex HOIs.
• To explore the role of inhibitory neurons in maintaining these interactions.

Main Methods:

• Analysis of spontaneous neural activity in cultured hippocampal slices.
• Quantification of simultaneous silence (SS) events.
• Comparison of observed SS frequency with predictions based on individual rates and pairwise correlations.
• Modeling of neural population activity with spiking nonlinearity and correlated input.

Main Results:

• Simultaneous silence (SS) is a more frequent feature than predicted by lower-order statistics.
• SS effectively explains structured HOIs, including alternating signs at successive interaction orders.
• Inhibitory neurons are crucial for maintaining significant SS.
• Structured HOIs predicted by SS were replicated in a computational neural population model.

Conclusions:

• Simultaneous silence (SS) is a ubiquitous feature that concisely summarizes higher-order interactions (HOIs) in neural populations.
• SS provides a new framework for understanding the constraints on neural activity patterns.
• These findings suggest SS may play a role in neural information processing.