Interactions between circuit architecture and plasticity in a closed-loop system

Abstract

Determining the sites of plasticity underlying changes in neural activity and behavior is critical for understanding mechanisms of learning. Identifying such sites from neural recording data can be challenging due to feedback pathways that impede reasoning about cause and effect. We studied interactions between feedback, neural activity, and plasticity in the context of a closed-loop motor learning task for which there is disagreement about the loci and directions of plasticity. We constructed a set of models that differed in the strength of their recurrent feedback. Despite these differences, each model successfully fit a large set of neural and behavioral data. However, the patterns of plasticity predicted by the models fundamentally differed, with the sign of plasticity at a key site changing from depression to potentiation as feedback strength increased. Guided by our analysis, we suggest how such models can be experimentally disambiguated. Our results address a long-standing debate regarding cerebellum-dependent motor learning and demonstrate how learning-related changes in neural activity can appear to contradict the sign of the underlying plasticity when feedback is present.