A new model for Cerebellar computation
This work addresses a specific issue in neuroscience for understanding cerebellar motor adaptation, but it appears incremental as it modifies an existing model to fit new data.
The authors tackled the problem that the standard state space model fails to explain cerebellar computation in motor adaptation tasks where visual feedback is irrelevant, and they proposed a new model where learning and forgetting are coupled and error-size dependent, which uniquely accounts for both classical and recent experimental results.
The standard state space model is widely believed to account for the cerebellar computation in motor adaptation tasks [1]. Here we show that several recent experiments [2-4] where the visual feedback is irrelevant to the motor response challenge the standard model. Furthermore, we propose a new model that accounts for the the results presented in [2-4]. According to this new model, learning and forgetting are coupled and are error size dependent. We also show that under reasonable assumptions, our proposed model is the only model that accounts for both the classical adaptation paradigm as well as the recent experiments [2-4].