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Abstract
Variability in neuronal responses to identical stimuli is frequently correlated across a population. Attention is thought to reduce these correlations by suppressing noisy inputs shared by the population. However, even with precise control of the visual stimulus, the subject’s attentional state varies across trials. While these state fluctuations are bound to induce some degree of correlated variability, it is currently unknown how strong their effect is, as previous studies generally do not dissociate changes in attentional strength from changes in attentional state variability. We designed a novel paradigm that does so and find both a pronounced effect of attentional fluctuations on correlated variability at long timescales and attention-dependent reductions in correlations at short timescales. These effects predominate in layers 2/3, as expected from a feedback signal such as attention. Thus, significant portions of correlated variability can be attributed to fluctuations in internally generated signals, like attention, rather than noise.
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1 Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
2 Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA; Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany; Bernstein Centre for Computational Neuroscience, Tübingen, Germany
3 Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA; Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany; Bernstein Centre for Computational Neuroscience, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany
4 Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA; Bernstein Centre for Computational Neuroscience, Tübingen, Germany; Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA