Dynamical processing of orientation precision in the primary visual cortex

Abstract

In our daily visual environment, the primary visual cortex (V1) processes distributions of oriented features as the basis of our visual computations. Changes of the global, median orientation of such inputs form the basis of our canonical knowledge about V1. However, another overlooked but defining characteristic of these sensory variables is their precision, which characterizes the level of variance in the input to V1. Such variability is an intrinsic part of natural images, yet it remains unclear if and how V1 accounts for the changes in orientation precision to achieve its robust orientation recognition performances. Here, we used naturalistic stimuli to characterize the response of V1 neurons to quantified variations of orientation precision. We found that about thirty percent of the recorded neurons showed a form of invariant responses to input precision. While feedforward mechanisms failed to account for the existence of these resilient neurons, neuronal competition within V1 explained the extent to which a neuron is invariant to precision. Using a decoding algorithm, we showed that the existence of such neurons in the population response of V1 can serve to encode both the orientation and its precision in the V1 population activity, which improves the robustness of the overall neural code. These precision-specific neurons operate with slow recurrent cortical dynamics, which supports the notion of predictive precisionweighted processes in V1.