Contextual effects during sensorimotor adaptation are an emergent property of population coding in a cerebellar-inspired model.

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.