Locking of correlated neural activity to ongoing oscillations

TL;DR

This paper develops a theoretical framework to explain how correlated neural activity becomes locked to ongoing oscillations, revealing mechanisms involving network feedback and susceptibility that modulate covariances in oscillatory brain states.

Contribution

It introduces a mean-field and linear response theory approach to analytically describe oscillation-locking of neural correlations in recurrent networks, a novel insight into neural synchrony mechanisms.

Findings

Correlations are modulated by external input and network feedback.

Two mechanisms: susceptibility modulation and activity variance.

Resonant covariances can occur with inhibitory feedback.

Abstract

Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of neurons to fire. Synchronous activation of neurons has been hypothesized to be a separate channel of signal processing information in the brain. A salient question is therefore if and how oscillations interact with spike synchrony and in how far these channels can be considered separate. Experiments indeed showed that correlated spiking co-modulates with the static firing rate and is also tightly locked to the phase of beta-oscillations. While the dependence of correlations on the mean rate is well understood in feed-forward networks, it remains unclear why and by which mechanisms correlations tightly lock to an oscillatory cycle. We here demonstrate that such correlated activation of pairs of neurons is qualitatively explained by periodically-driven random networks. We identify the mechanisms by which covariances depend on a driving periodic stimulus. Mean-field theory combined with linear response theory yields closed-form expressions for the cyclostationary mean activities and pairwise zero-time-lag covariances of binary recurrent random networks. Two distinct mechanisms cause time-dependent covariances: the modulation of the susceptibility of single neurons (via the external input and network feedback) and the time-varying variances of single unit activities. For some parameters, the effectively inhibitory recurrent feedback leads to resonant covariances even if mean activities show non-resonant behavior. Our analytical results open the question of time-modulated synchronous activity to a quantitative analysis.