Visual attention to features and space in mice using reverse correlation

Stimulus-dependent differences in cortical versus subcortical contributions to visual detection in mice

The primary visual cortex (V1) and the superior colliculus (SC) both occupy stations early in the processing of visual information. They have long been thought to perform distinct functions, with the V1 supporting the perception of visual features and the SC regulating orienting to visual inputs. However, growing evidence suggests that the SC supports the perception of many of the same visual features traditionally associated with the V1. To distinguish V1 and SC contributions to visual processing, it is critical to determine whether both areas causally contribute to the detection of specific visual stimuli. Here, mice reported changes in visual contrast or luminance near their perceptual threshold while white noise patterns of optogenetic stimulation were delivered to V1 or SC inhibitory neurons. We then performed a reverse correlation analysis on the optogenetic stimuli to estimate a neuronal-behavioral kernel (NBK), a moment-to-moment estimate of the impact of V1 or SC inhibition on stimulus detection. We show that the earliest moments of stimulus-evoked activity in the SC are critical for the detection of both luminance and contrast changes. Strikingly, there was a robust stimulus-aligned modulation in the V1 contrast-detection NBK but no sign of a comparable modulation for luminance detection. The data suggest that behavioral detection of visual contrast depends on both V1 and SC spiking, whereas mice preferentially use SC activity to detect changes in luminance. Electrophysiological recordings showed that neurons in both the SC and V1 responded strongly to both visual stimulus types, while the reverse correlation analysis reveals when these neuronal signals actually contribute to visually guided behaviors.

Emergent human-like covert attention in feedforward convolutional neural networks

Covert attention allows the selection of locations or features of the visual scene without moving the eyes. Cues and contexts predictive of a target's location orient covert attention and improve perceptual performance. The performance benefits are widely attributed to theories of covert attention as a limited resource, zoom, spotlight, or weighting of visual information. However, such concepts are difficult to map to neuronal populations. We show that a feedforward convolutional neural network (CNN) trained on images to optimize target detection accuracy and with no explicit incorporation of an attention mechanism, a limited resource, or feedback connections learns to utilize cues and contexts in the three most prominent covert attention tasks (Posner cueing, set size effects in search, and contextual cueing) and predicts the cue/context influences on human accuracy. The CNN's cueing/context effects generalize across network training schemes, to peripheral and central pre-cues, discrimination tasks, and reaction time measures, and critically do not vary with reductions in network resources (size). The CNN shows comparable cueing/context effects to a model that optimally uses image information to make decisions (Bayesian ideal observer) but generalizes these effects to cue instances unseen during training. Together, the findings suggest that human-like behavioral signatures of covert attention in the three landmark paradigms might be an emergent property of task accuracy optimization in neuronal populations without positing limited attentional resources. The findings might explain recent behavioral results showing cueing and context effects across a variety of simple organisms with no neocortex, from archerfish to fruit flies.

Rodent attention: Probing the mouse mind with reverse correlation

A novel approach to studying attention in mice reveals processes similar to those in humans and lays out an efficient way to explore its neuronal correlates in a genetically tractable animal model.