Speaker
Description
Adaptation is a fundamental component of motor learning, enabling organisms to adjust to environmental changes while maintaining stable motor memories. A long-standing view is that this balance emerges from the interaction of learning processes operating at different timescales, yet the mechanisms that coordinate rapid adaptation with slower consolidation remain unclear. Because motor learning is distributed across multiple brain areas, this raises the question of how these systems interact to support both flexibility and stability.
Here, we propose a distributed learning framework in which adaptation and memory arise from the interaction of fast and slow systems that learn simultaneously but at different rates. We implement this in a multi-area architecture of two networks with distinct roles: a slow recurrent controller, corresponding to motor cortex, that stores stable, slowly evolving representations, and a fast feedforward adapter, corresponding to the cerebellum, that rapidly learns to predict the controller's error in response to perturbations.
We show that defining the two modules in this complementary way — with the adapter learning to predict the controller's error — allows a single signal to serve two roles: it supports efficient online correction of the system's output while simultaneously guiding the controller's local plasticity, enabling the gradual and efficient adaptation of its learned representations into long-term memory. This division of labor provides a mechanistic account of how fast error-driven learning can instruct slower, more stable representations through biologically plausible local rules. The model offers a unified perspective on how rapid adaptation and memory formation coexist within distributed circuits, and suggests how cerebellar–cortical interactions may support motor learning. More broadly, it highlights how predictive signals generated by fast processes can shape long-term representations in slower systems.
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