Guided by Noise: Correlated Variability Channels Task-Relevant Information in Sensory Neurons
Shared trial-to-trial variability across sensory neurons is reliably reduced when perceptual performance improves, yet this variability is low-dimensional, so it could be ignored by an optimal readout mechanism. Why then is it so consistently related to behavior? We propose that shared variability reflects the information communicated to downstream areas, rather than noise to be avoided. Using a circuit model, we show that when sensory signals align with shared variability, behaviorally relevant information is amplified without compromising coding fidelity. Analyses of neural population recordings from multiple brain areas and tasks reveal that the dominant axis of shared variability consistently aligns with task-relevant stimulus features and action plans. Finally, the behavioral impact of microstimulation can be explained by the extent to which it changes projections onto the shared variability axis. These findings suggest that shared variability may illuminate, rather than obscure, the neural dimensions that guide behavior.