Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways
Year: 2020
Type: article
Abstract: The learning of motor skills unfolds over multiple timescales, with rapid initial gains in performance followed by a longer period in which the behavior becomes more refined, habitual, and automatized. While recent lesion and inactivation experiments have provided hints about how various brain areas might contribute to such learning, their precise roles and the neural mechanisms underlying them are not well understood. In this work, we propose neural- and circuit-level mechanisms by which motor cortex, thalamus, and striatum support motor learning. In this model, the combination of fast cortical learning and slow subcortical learning gives rise to a covert learning process through which control of behavior is gradually transferred from cortical to subcortical circuits, while protecting learned behaviors that are practiced repeatedly against overwriting by future learning. Together, these results point to a new computational role for thalamus in motor learning and, more broadly, provide a framework for understanding the neural basis of habit formation and the automatization of behavior through practice.
Source: Nature Communications
Authors: James M. Murray, G. Sean Escola
Language: English
Topic: Motor Control and Adaptation
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,380
Awards: CRCNS: Refining computational models of motor sequence learning and execution, Computational and circuit mechanisms underlying motor control, G3844816011, NeuroNex Theory Team: Columbia University Theoretical Neuroscience Center, Reinforcement learning and action sequencing in subcortical and cortical circuits, G4969685445, G5805940406, Reinforcement learning and action sequencing in subcortical and cortical circuits, G6477103862, G6605009349, G7034110047, G814101816, G8709554971, G9000049176, The internal states of neural circuits: data analysis, modeling, and disease
OpenAlex ID: W3113864970