ArticleeLife2025
Dynamics of striatal action selection and reinforcement learning.
Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Changes in Cerebellar Multiunit Activity Associated with Ventrolateral Striatal Injury During Spontaneous Motor Behavior.Medical sciences (Basel, Switzerland) · 2026Article
- Static and dynamic functional connectivity of the bilateral insula in patients with migraine without aura: a resting-state fMRI study.Frontiers in neurology · 2026Article
- Reward-driven adaptation of movements requires strong recurrent basal ganglia-cortical loops.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Post-learning replay of hippocampal-striatal activity is biased by reward-prediction signals.Nature communications · 2025Article
- Refinement of efficient encodings of movement in the dorsolateral striatum throughout learning.Cell reports · 2025Article
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5 authors.
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Abstract
Spiny projection neurons (SPNs) in dorsal striatum are often proposed as a locus of reinforcement learning in the basal ganglia. Here, we identify and resolve a fundamental inconsistency between striatal reinforcement learning models and known SPN synaptic plasticity rules. Direct-pathway (dSPN) and indirect-pathway (iSPN) neurons, which promote and suppress actions, respectively, exhibit synaptic plasticity that reinforces activity associated with elevated or suppressed dopamine release. We show that iSPN plasticity prevents successful learning, as it reinforces activity patterns associated with negative outcomes. However, this pathological behavior is reversed if functionally opponent dSPNs and iSPNs, which promote and suppress the current behavior, are simultaneously activated by efferent input following action selection. This prediction is supported by striatal recordings and contrasts with prior models of SPN representations. In our model, learning and action selection signals can be multiplexed without interference, enabling learning algorithms beyond those of standard temporal difference models.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.