Sensorimotor prediction errors in the mouse olfactory cortex

During behavior, sensation and action operate in closed-loop. Movements shape sensory input, and sensory inputs guide motor commands: where one looks determines what one sees. Through experience, the brain learns the reciprocal relationship between sensory inputs and movements to build internal models that accurately predict the sensory consequences of upcoming actions (sensorimotor predictions). This exchange of sensory inputs and egocentric expectations is at the core of active perception. Experimental investigation of this idea has been sparse and split between behavioral interrogation of sensory-guided, precise motor control in primates (visuomotor adaption tasks) and the search for neuronal substrates of sensory predictions in rodents via simpler running-based closed-loop behaviors.
To study internal models both at behavioral and circuit-level, we developed a novel behavioral task where head-fixed mice are trained to steer the left-right location of an odor source by controlling a light-weight lever with their forepaws. In this manner, 1) we link a precise motor action to well-defined sensory expectations (odor location) and 2) subsequently violate the learnt expectations via online sensory feedback perturbations in trained animals. Strikingly, mice readily counter these sensorimotor errors, making precise corrective movements that provide us a read-out of their individually learnt sensorimotor expectations.
In other modalities (vision, audition), sensorimotor error-signals have been observed in primary cortical areas. Using chronic recordings during behavior, we tested whether the olfactory cortex performs similar sensorimotor computations. We find that odor-driven responses in cortical neurons are strongly re-shaped by olfacto-motor expectations. Transient perturbations often triggered responses that were stronger than those evoked by any other task variable. Our results suggest that olfactory cortex computes sensorimotor prediction errors by integrating sensory information with movement-related predictions, presumably relayed via top-down feedback.
Using cell-type analysis and activity manipulations, we are currently identifying the circuit elements that facilitate the comparison of olfactory inputs with predictions.