Integrating Structural Plasticity and Systems-Level Adaptation: Insights into Cortico-Basal Ganglia and Cerebellar Contributions to Motor Learning

 The article The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach (Baladron et al., 2023) presents a systems-level computational model that elucidates the complementary roles of the basal ganglia and cerebellum in motor learning. The authors propose that the cortico-basal ganglia loop facilitates the selection of motor actions based on a novelty-driven motor prediction error, while the cerebellum minimizes the remaining aiming error through fine-grained corrective adjustments. Using a robotic arm simulation, the model demonstrates successful learning of reaching movements and motor adaptation, emphasizing that different brain regions employ distinct learning algorithms—reinforcement learning in the basal ganglia and perturbation-based supervised learning in the cerebellum. This work supports integrative frameworks of motor learning, suggesting that the synergy between cortical, basal ganglia, and cerebellar systems enables both action selection and continuous performance refinement.

The computational framework proposed by Baladron et al. (2023) expands on several principles discussed in Cortico-basal ganglia plasticity in motor learning (Roth & Ding, 2024). Both articles highlight the fundamental role of the cortico-basal ganglia circuits in the acquisition and execution of motor skills. However, while Roth and Ding focus primarily on experimental evidence for synaptic and circuit-level plasticity within motor cortical and striatal pathways, Baladron et al. provide a functional computational perspective by incorporating cerebellar contributions to action refinement. Notably, Roth and Ding (2024) discuss plastic changes such as dendritic spine remodeling and the formation of stable spatiotemporal activity patterns in the motor cortex and striatum, but they do not model the interactive dynamics between these brain regions and the cerebellum during motor learning. The addition of cerebellar mechanisms in Baladron et al. (2023) provides critical insights into how online error correction complements the structural plasticity described by Roth and Ding.

In Cortico-basal ganglia plasticity in motor learning, Roth and Ding (2024) review advances in understanding how motor learning is supported by both functional and structural plasticity within the cortico-basal ganglia circuits. They emphasize that skilled motor behaviors are associated with the reorganization of activity patterns in cortical and striatal neurons, long-term synaptic potentiation, and dynamic changes in inhibitory interneuron activity. The review particularly underscores the importance of early plastic changes in superficial cortical layers (L2/3) and the gradual decorrelation of cortical activity patterns after extensive training, highlighting the evolving division of labor between cortical and subcortical systems over time. These findings establish a detailed biological basis for how motor memories are encoded and maintained.

Taken together, the two articles converge on the central role of the cortico-basal ganglia system in motor learning but differ in their emphasis. Roth and Ding (2024) provide rich experimental detail on the biological substrates of motor learning, focusing on the mechanisms of synaptic and circuit plasticity. In contrast, Baladron et al. (2023) present a computational model that extends this understanding by demonstrating how different learning signals—novelty-driven reinforcement for the basal ganglia and error-driven adjustment for the cerebellum—cooperate to optimize motor behavior. Together, these studies offer complementary perspectives, linking cellular and circuit-level adaptations with systems-level dynamics necessary for robust motor learning and adaptation.

 

Citations:

Baladron, J., Vitay, J., Fietzek, T., & Hamker, F. H. (2023). The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach. PLOS Computational Biology, 19(4), e1011024. https://doi.org/10.1371/journal.pcbi.1011024

Roth, R. H., & Ding, J. B. (2024). Cortico-basal ganglia plasticity in motor learning. Neuron, 112(15), 2486–2493. https://doi.org/10.1016/j.neuron.2024.06.014