期刊
JOURNAL OF NEUROSCIENCE
卷 42, 期 18, 页码 3847-3855出版社
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.1771-21.2022
关键词
cerebellum; electrophysiology; primates; Purkinje cells; reinforcement learning; reward
资金
- Zegar Family Foundation
- Dana Foundation
- National Eye Institute [R24EY-015634, R21 EY-020631, R01 EY-017039, RO1-NS113078, P30 EY-019007]
- Keck Foundation
Recent studies have shown that the cerebellum is not only involved in motor control but also plays a role in reward processing. In an experiment with monkeys, researchers found that the discharge patterns of cerebellar cortex changed during the learning of associations between movements and visual symbols. Despite being related to both reward and movement, these signals were independent of each other.
Although the cerebellum has been traditionally considered to be exclusively involved in motor control, recent anatomic and clinical studies show that it also has a role in reward-processing. However, the way in which the movement-related and the reward-related neural activity interact at the level of the cerebellar cortex and contribute toward learning is still unclear. Here, we studied the simple spike activity of Purkinje cells in the mid-lateral cerebellum when 2 male monkeys learned to associate a right or left-hand movement with one of two visual symbolic cues. These cells had distinctly different discharge patterns between an overtrained symbol-hand association and a novel symbol-hand association, responding in association with the movement of both hands, although the kinematics of the movement did not change between the two conditions. The activity change was not related to the pattern of the visual symbols, the movement kinematics, the monkeys' reaction times, or the novelty of the visual symbols. The simple spike activity changed throughout the learning process, but the concurrent complex spikes did not instruct that change. Although these neurons also have reward-related activity, the reward-related and movement-related signals were independent. We suggest that this mixed selectivity may facilitate the flexible learning of difficult reinforcement learning problems.
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