Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis

Abstract

Spike-field coupling characterizes the relationship between neurophysiological activities observed at two different scales: on the one hand, the action potential produced by a neuron, on the other hand a mesoscopic “field” signal, reflecting subthreshold activities. This provides insights about the role of a specific unit in network dynamics. However, assessing the overall organization of neural circuits based on multivariate data requires going beyond pairwise approaches, and remains largely unaddressed. We develop Generalized Phase Locking Analysis (GPLA) as an multichannel extension of univariate spike-field coupling. GPLA estimates the dominant spatio-temporal distributions of field activity and neural ensembles, and the strength of the coupling between them. We demonstrate the statistical benefits and interpretability of this approach in various biophysical neuronal network models and Utah array recordings. In particular, we show that GPLA, combined with neural field modeling, help untangle the contribution of recurrent interactions to the spatio-temporal dynamics observed in multi-channel recordings.