Contextual effects during sensorimotor adaptation are an emergent property of population coding in a...

The cerebellum is crucial for sensorimotor adaptation, using error information to keep the sensorimotor system well calibrated. Here, we introduce a population-coding model to explore this process, focusing on how implicit adaptation is modulated by context. The model consists of a two-layered network, designed to capture activity in both the cerebellar cortex and deep cerebellar nuclei. A core feature of the model is that within each layer, the processing units are tuned to both movement direction and the direction of movement error. The model recapitulates a large range of contextual effects such as how prior learning, error uncertainty, or temporal delay affect the rate of adaptation. While these effects have traditionally been taken to indicate a form of meta-learning or context-dependent memory within the adaptation system, the current results show that they can emerge from the population activity of a cerebellar-like hierarchical network that does not represent context.