Kavli Affiliate: Anirvan Nandy
| Authors: Anirban Das, Alec G. Sheffield, Anirvan S. Nandy and Monika P. Jadi
| Summary:
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 environment. 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 hierarchy. Based on the theoretical results that even weak correlated variability can substantially limit the encoding capacity of a neuronal pool, a second proposal is an improvement in the sensory information capacity of an encoding stage through a reduction in shared fluctuations. 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 attention. Using network-based statistical modeling, we estimated the strength of inter-layer information flow by measuring statistical dependencies that reflect how the cortical layers uniquely drive 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 framework, 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 fluctuations. By “decomposing” the modulation of dependency components and in combination with prior theoretical work, 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 flow between and improved information capacity within encoding stages.