Feature selectivity can explain mismatch signals in mouse visual cortex.

Sensory experience often depends 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 the mouse visual cortex (V1) when visual flow coupled to running was unexpectedly stopped. Here, we show that such signals can be elicited by visual stimuli uncoupled to an animal running. We record V1 neurons while presenting drifting gratings that unexpectedly stop. We find strong responses to visual perturbations, which are enhanced during running. Perturbation responses are strongest in the preferred orientation of individual neurons, and perturbation-responsive neurons are more likely to prefer slow visual speeds. Our results indicate that prediction error signals can be explained by the convergence of known motor and sensory signals, providing a purely sensory and motor explanation for purported mismatch signals. [Display omitted] • Mouse primary visual cortex neurons respond to perturbations of visual flow • Perturbation responses are enhanced by running • Perturbation responses are stronger in the preferred orientation of the neurons • Perturbation-responsive neurons are tuned to slow visual speeds Muzzu and Saleem find neurons in the mouse visual cortex responding to stops or perturbations of open-loop visual flow. Perturbation responses are enhanced by running, and responsive neurons prefer low visual speeds. These results suggest concurrent selectivity for low visual speeds and running can explain sensorimotor mismatch signals in the visual cortex. [ABSTRACT FROM AUTHOR]