Abstract

Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.

Highlights

  • Measures of neural correlations and variability are widely used in neuroscience to characterize neural processes

  • We provide a mechanistic framework using computational simulations and detailed dynamical systems analyses to explain the quenching of neural variability and correlations during task-evoked states

  • When analyzed with covariance, we found these covariance increases to be weak relative to the observed covariance decreases. Though these correlation increases were only observed in 1 of 2 non-human primate (NHP), they were generally consistent with our functional magnetic resonance imaging (fMRI) data, which showed that though there were few correlation increases, variability and correlations across cortex were dominated by decreases during task states

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Summary

Introduction

Measures of neural correlations and variability are widely used in neuroscience to characterize neural processes. Neural variability has consistently been shown to be reduced during tasks across human functional magnetic resonance imaging (fMRI) [1,2,3], local neural populations [4,5,6], and both spiking [5,7] and mean-field rate models [8,9]. Despite this convergence in the neural variability literature, there are disparities in the use and interpretation of neural correlations. Despite the use of different terms, the target statistical inference behind these two techniques is consistent: to characterize the interaction among neural units

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