Imagine you’re at a party where two people are vying for your attention. One moment you’re chatting with Person A, and the next, Person B steals your focus. This interesting back-and-forth, known as binocular rivalry, also happens in our brains when we process visual information from our two eyes. Scientists have long been fascinated by what drives these perceptual switches. In this study, researchers took a fresh approach by reconstructing the evolving percepts using a model network of neurons. By analyzing the activity patterns, they uncovered how the brain encodes dominant images and identified the factors that influence perceptual alternations. The findings not only aligned with existing experimental observations but also shed light on the potential causes of neurological disorders like autism, which are characterized by slower percept switching. This research offers exciting insights into the dynamic computations happening in our brains and paves the way for further understanding of how we perceive and how things can go awry.

When the two eyes are presented with highly distinct stimuli, the resulting visual percept generally switches every few seconds between the two monocular images in an irregular fashion, giving rise to a phenomenon known as binocular rivalry. While a host of theoretical studies have explored potential mechanisms for binocular rivalry in the context of evoked model dynamics in response to simple stimuli, here we investigate binocular rivalry directly through complex stimulus reconstructions based on the activity of a two-layer neuronal network model with competing downstream pools driven by disparate monocular stimuli composed of image pixels. To estimate the dynamic percept, we derive a linear input-output mapping rooted in the non-linear network dynamics and iteratively apply compressive sensing techniques for signal recovery. Utilizing a dominance metric, we are able to identify when percept alternations occur and use data collected during each dominance period to generate a sequence of percept reconstructions. We show that despite the approximate nature of the input-output mapping and the significant reduction in neurons downstream relative to stimulus pixels, the dominant monocular image is well-encoded in the network dynamics and improvements are garnered when realistic spatial receptive field structure is incorporated into the feedforward connectivity. Our model demonstrates gamma-distributed dominance durations and well obeys Levelt’s four laws for how dominance durations change with stimulus strength, agreeing with key recurring experimental observations often used to benchmark rivalry models. In light of evidence that individuals with autism exhibit relatively slow percept switching in binocular rivalry, we corroborate the ubiquitous hypothesis that autism manifests from reduced inhibition in the brain by systematically probing our model alternation rate across choices of inhibition strength. We exhibit sufficient conditions for producing binocular rivalry in the context of natural scene stimuli, opening a clearer window into the dynamic brain computations that vary with the generated percept and a potential path toward further understanding neurological disorders.

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