A probabilistic model of visual segmentation explains early visual cortical dynamics

Neural dynamics are a central feature of biological neural systems. In the visual cortex, the functional role of neural dynamics is poorly understood, particularly for static stimuli. The statistics of still natural images have long been used by models that successfully predict firing in the early visual cortex. Those models assume that neurons represent inferences about latent image features, but they often ignore the dynamics of said inference. Here, we test if inferential dynamics for images explain visual-cortical dynamics. To do so, we present a biologically plausible model of cortical computation that assumes the inference of features is coupled with inference about how those features are organized into meaningful parts, also known as segmentation. Given an input image, our model iterates between inferring the features and the segments, through recurrence, thus inducing dynamics in the feature representation. We make theoretical and exact predictions for the induced neural dynamics. Our model predicts both gain and variability in evoked responses to natural images, and we show that it captures classical single-neuron experimental findings such as gain dynamics and variability decay during stimulus presentation. The model also predicts that those metrics are more heterogeneous than previously thought, reflecting the complexity of inference for natural scenes, and that their population-level organization reflects the inferred segments. Finally, our analytical formulation provides a clear interpretation of those results. Insights from our normative model could generalize to segmentation problems in other experimental modalities, and help address the cost of inference in artificial networks.