TL;DR
This paper introduces a Gaussian process-based framework to decouple neural source signals from local field potentials, revealing cross-population correlations and phase coupling that are obscured in raw LFP data.
Contribution
The novel framework models current source densities as Gaussian processes, improving source identification and cross-population correlation analysis in neural recordings.
Findings
Layer-specific phase coupling in primate auditory cortex
Task-evoked CSDs show cross-probe correlations at specific depths
Depth-specific phase coupling in mouse visual areas
Abstract
Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As we show here, however, once disparate source signals are decoupled, their trial-to-trial fluctuations become more accessible, and cross-population correlations become more apparent. To decouple sources we introduce a general framework for estimation of current source densities (CSDs). In this framework, the set of LFPs result from noise being added to the transform of the CSD by a biophysical forward model, while the CSD is considered to be the sum of a zero-mean, stationary, spatiotemporal Gaussian process, having fast and slow components, and a mean function, which is the sum of multiple time-varying functions distributed across space, each varying across trials. We derived biophysical…
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