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Adaptive information processing, comprised of local computations and their efficient routing, is crucial for flexible brain function. Spatial attention is a quintessential example of this adaptive process. It is critical for recognizing and interacting with behaviorally relevant objects in a cluttered environment1. Object recognition is mediated by ensembles of computational units distributed across the ventral visual hierarchy. How the deployment of spatial attention aids these hierarchical computations is unclear. Based on pairwise correlation analysis, two key mechanisms have been proposed: First is an improvement in the efficacy of unique information directed from one encoding stage to another, suggested by evidence along the visual hierarchy2–6. Based on the theoretical results that even weak correlated variability can substantially limit the encoding capacity of a neuronal pool7, a second proposal is an improvement in the sensory information capacity of an encoding stage through a reduction in shared fluctuations8,9. However, pairwise analyses capture both unique and shared components of fluctuations, and therefore cannot disambiguate the proposed mechanisms. To test these proposals, it is crucial to estimate the attentional modulation of unique information flow across and shared information within the stages of the visual hierarchy. We investigated this in the multi-stage laminar network of visual area V4, an area strongly modulated by attention10–12. Using network-based statistical modeling, we estimated the strength of inter-layer information flow by measuring statistical dependencies that reflect how the cortical layersuniquelydrive each other’s neural activity. We quantified their modulation across attention conditions (attend-in vs. attend-away) in a change detection task and found that deployment of attention indeed strengthened unique dependencies between the input and superficial layers. Using the partial information decomposition framework13,14, we estimated modulation of shared dependencies and found that they are reduced within laminar populations, specifically the putative excitatory subpopulations. Surprisingly, we found a strengthening of unique dependencies within the laminar populations, a finding not previously predicted. Crucially, these modulation patterns were also observed across behavioral outcomes (hit vs. miss) that are thought to be mediated by endogenous state fluctuations15–17. By “decomposing” the modulation of dependency components and in combination with prior theoretical work7, our results suggest the following computational model of optimal sensory states that are attained by either task demands or endogenous fluctuations in brain state: enhanced information flowbetweenand improved information capacitywithinencoding stages.

Identical stimuli can be perceived or go unnoticed across successive presentations, producing divergent behavioral readouts despite similarities in sensory input. We hypothesized that fluctuations in neurophysiological states in the sensory neocortex, which could alter cortical processing at the level of neural subpopulations, underlies this perceptual variability. We analyzed cortical layer-specific electrophysiological activity in visual area V4 during a cued attention task. We find that hit trials are characterized by a larger pupil diameter and lower incidence of microsaccades, indicative of a behavioral state with increased arousal and perceptual stability. Target stimuli presented at perceptual threshold evoke elevated multi-unit activity in V4 neurons in hit trials compared to miss trials, across all cortical layers. Putative excitatory and inhibitory neurons are strongly positively modulated in the input (IV) and deep (V & VI) layers of the cortex during hit trials. Excitatory neurons in the superficial cortical layers exhibit lower variability in hit trials. Deep layer neurons are less phase-locked to low frequency rhythms in hits. Hits are also characterized by greater interlaminar coherence between the superficial and deep layers in the pre-stimulus period, and a complementary pattern between the input layer and both the superficial and deep layers in the stimulus-evoked period. Taken together, these results indicate that a state of elevated levels of arousal and perceptual stability allow enhanced processing of sensory stimuli, which contributes to hits at perceptual threshold.

Contrast is a key feature of the visual scene that aids object recognition. Attention has been shown to selectively enhance the responses to low contrast stimuli in visual area V4, a critical hub that sends projections both up and down the visual hierarchy. Veridical encoding of contrast information is a key computation in early visual areas, while later stages encode higher level features that benefit from improved sensitivity to low contrast. How area V4 meets these distinct information processing demands in the attentive state is not known. We found that attentional modulation of contrast responses in area V4 is cortical layer and cell-class specific. Putative excitatory neurons in the superficial output layers that project to higher areas show enhanced boosting of low contrast information. On the other hand, putative excitatory neurons of deep output layers that project to early visual areas exhibit contrast-independent scaling. Computational modeling revealed that such layer-wise differences may result from variations in spatial integration extent of inhibitory neurons. These findings reveal that the nature of interactions between attention and contrast in V4 is highly compartmentalized, in alignment with the demands of the visual processing hierarchy.

Saccades are a ubiquitous and crucial component of our visual system, allowing for the efficient deployment of the fovea and its accompanying neural resources. Initiation of a saccade is known to cause saccadic suppression, a temporary reduction in visual sensitivity1, 2 and visual cortical firing rates3–6. While saccadic suppression has been well characterized at the level of perception and single neurons, relatively little is known about the visual cortical networks governing this phenomenon. Here we examine the effects of saccadic suppression on distinct neural subpopulations within visual area V4. We find cortical layer- and cell type-specific differences in the magnitude and timing of peri-saccadic modulation. Neurons in the input layer show changes in firing rate and inter-neuronal correlations prior to saccade onset, indicating that this layer receives information about impending saccades. Putative inhibitory interneurons in the input layer elevate their firing rate during saccades, suggesting they play a role in suppressing the activity of other cortical subpopulations. A computational model of this circuit recapitulates our empirical observations and demonstrates that an input layer-targeting pathway can initiate saccadic suppression by enhancing local inhibitory activity. Collectively, our results provide a mechanistic understanding of how eye movement signaling interacts with cortical circuitry to enforce visual stability.

Deployment of covert attention to a spatial location can cause large decreases in low-frequency correlated variability among neurons in macaque area V4 whose receptive-fields lie at the attended location. It has been estimated that this reduction accounts for a substantial fraction of the attention-mediated improvement in sensory processing. These estimates depend on assumptions about how population signals are decoded and the conclusion that correlated variability impairs perception, is purely hypothetical. Here we test this proposal directly by optogenetically inducing low-frequency fluctuations, to see if this interferes with performance in an attention-demanding task. We find that low‐ frequency optical stimulation of neurons in V4 elevates correlations among pairs of neurons and impairs the animal’s ability to make fine sensory discriminations. Stimulation at higher frequencies does not impair performance, despite comparable modulation of neuronal responses. These results support the hypothesis that attention-dependent reductions in correlated variability contribute to improved perception of attended stimuli.