Topic: self-motion

According to a classical control-theoretic perspective on self-movement, our brains pilot our bodies to achieve goals, sometimes using open-loop predictive models and sometimes with closed-loop feedback. From this perspective, synchronization of self-motion with a moving target is a simple control problem, where error between body and target is minimized by some combination of responsive and predictive control. However, self-movement can also result from reconciliation of multisensory prediction errors. When subjected to a visual/proprioceptive conflict, our bodies respond spontaneously and un

Neuronal computations are matched to optimally encode the sensory information that is available and relevant for the animal. However, the physical distribution of sensory information is often shaped by the animal’s own behavior. One prominent example is the encoding of optic flow fields that are generated during self-motion of the animal and will therefore depend on the type of locomotion. How evolution has matched computational resources to the behavioral constraints of an animal is not known. Here we use in vivo two photon imaging to record from a population of >3.500 local-direction selecti

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 mouse visual cortex (V1) when visual flow coupled to running was unexpectedly stopped. Here, we show such signals can be elicited by visual stimuli uncoupled to animal’s running. We recorded V1 neurons while presenting drifting gratings that unexpectedly stop