Distinctive regional patterns of dynamic neural noise in cortical activity

Objective. Neurons exhibit deterministic behavior influenced by stochastic cellular or extracellular components. Estimating this random component is challenging due to unknown underlying deterministic dynamics. In this study, we aim to estimate the neural random component, termed intrinsic dynamic neural noise, from experimental time series without prior assumptions on the underlying neural model. Approach. The method relies on the nonlinear approximate entropy profile and was evaluated using synthetic data from Izhikevich’s models and simulated calcium dynamics driven by dynamical noise. We then applied the method to experimental time series from calcium imaging in mice and zebrafish brain regions, as well as electrophysiological data from a 128-channel cortical probe in anesthetized rats. Main results. The results show region-specific behavior, with higher dynamic neural noise in the somatosensory cortex of mice and anterior telencephalic area of zebrafish. Furthermore, neuronal stochasticity is greater in genetically encoded Ca 2 + indicators than in Ca 2 + dyes, and neural noise increases with recording depth. Significance. These findings offer insights into neural dynamics and suggest dynamic noise as a key biomarker.