A new model for Cerebellar computation

TL;DR

This paper challenges the standard cerebellar computation model in motor adaptation and introduces a new model where learning and forgetting are coupled and error-dependent, explaining both classical and recent experimental results.

Contribution

The paper proposes a novel cerebellar computation model that accounts for recent experimental findings challenging the standard model.

Findings

The new model explains recent experiments where visual feedback is irrelevant.

Learning and forgetting are coupled and depend on error size in the model.

The model uniquely accounts for both classical and recent adaptation paradigms.

Abstract

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].