Topic: sensory predictions

While moving through the world, such as biking down a bumpy trail, our bodies encounter external forces that cannot be predicted by our own motor output. Delayed sensory feedback is generally too slow to allow accurate state estimation during responses to external forces, so the nervous system may solve this problem by using prior information to make rapid sensory predictions during feedback control. However, it is unknown how flexibly these priors can be set nor how neural activity underlies these fundamental abilities. To answer this, we designed a reaching task in which subjects (human or m

Activity in primary sensory areas is robustly driven by signals from their respective sensory inputs. However, both theory and experiment suggest that the same cortices are modulated by context-dependent internally generated activity. Predictive coding theories propose that generative models formed through learning modulate primary sensory responses on a moment-to-moment basis according to how well incoming signals conform to the animal’s predictions. Experimental data has shown that prediction errors can alter neural responses, even in the absence of behavioral relevance. Yet several aspects

The ease with which two-legged dogs can run illustrates the versatility and agility of mammals in learning arbitrary motor skills, an ability currently unrivaled by artificial systems. How the brain supports these capacities is unclear, as control mechanisms are distributed and interdependent between many brain regions. We make progress on this question by developing a neural network model comprising three major brain systems that are known to play central roles in learned motor control: the cerebellum (CB), primary motor cortex (M1), and associative sensorimotor cortex (ASC). We both leverage