Feature selectivity explains mismatch signals in mouse visual cortex

Sensory experience is often dependent on one’s own actions, including self-motion. Theories of predictive coding postulate that actions are regulated by calculating prediction error, which is the difference between sensory experience and expectation based on self-generated actions. Signals consistent with prediction error have been reported in mouse visual cortex (V1) when visual flow coupled to running is unexpectedly perturbed. Here, we show that such signals can be elicited by visual stimuli uncoupled with the animal’s running. We recorded the activity of mouse V1 neurons while presenting drifting gratings that unexpectedly stopped. We found strong responses to visual perturbations, which were enhanced during running. If these perturbation responses are signals about sensorimotor mismatch, they should be largest for front-to-back visual flow expected from the animals’ running. Responses, however, did not show a bias for front-to-back visual flow. Instead, perturbation responses were strongest in the preferred orientation of individual neurons and perturbation responsive neurons were more likely to prefer slow visual speeds. Our results therefore indicate that prediction error signals can be explained by the convergence of known motor and sensory signals in visual cortex, providing a purely sensory and motor explanation for purported mismatch signals.