Task-evoked activity quenches neural correlations and variability across cortical areas
Name
pcbi.1007983.pdf
Description
Published version
Size
4.36 MB
Format
Adobe PDF
Checksum (MD5)
e611784fe5fb62efcb5128605d7e44ff
Author(s)
Ito, Takuya
Brincat, Scott L
Siegel, Markus
Mill, Ravi D
He, Biyu J
Miller, Earl K
Rotstein, Horacio G
Cole, Michael W
Date Issued
2020
Journal
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Version
Final published version
Abstract
Copyright: © 2020 Ito et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.
MIT Department
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PCBI.1007983
