Distinctive regional patterns of dynamic neural noise in cortical activity

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biophysics

Background:

• Neurons display deterministic behavior influenced by stochastic factors.
• Estimating this neural random component is difficult due to unknown deterministic dynamics.
• Intrinsic dynamic neural noise is a key aspect of neural function.

Purpose of the Study:

• To estimate intrinsic dynamic neural noise from experimental time series.
• To achieve this without making prior assumptions about the underlying neural model.
• To identify neural noise as a potential biomarker.

Main Methods:

• Utilized the nonlinear approximate entropy profile.
• Evaluated the method with synthetic data from Izhikevich's models and simulated calcium dynamics.
• Applied the method to calcium imaging and electrophysiological data from animal models.

Main Results:

• Identified region-specific neural noise patterns, notably higher in mouse somatosensory cortex and zebrafish anterior telencephalic area.
• Observed greater neuronal stochasticity with genetically encoded calcium indicators compared to calcium dyes.
• Found that neural noise increases with recording depth.

Conclusions:

• The developed method effectively estimates intrinsic dynamic neural noise.
• Neural noise exhibits significant variability across brain regions, recording techniques, and depths.
• Intrinsic dynamic neural noise may serve as a crucial biomarker for neural dynamics.