Choice-selective sequences dominate in cortical relative to thalamic inputs to NAc to support reinforcement learning

Nathan F. Parker, Avinash Baidya, Julia Cox, Laura M. Haetzel, Anna Zhukovskaya, Malavika Murugan, Ben Engelhard, Mark S. Goldman, Ilana B. Witten

Research output: Contribution to journalArticlepeer-review

Abstract

How are actions linked with subsequent outcomes to guide choices? The nucleus accumbens, which is implicated in this process, receives glutamatergic inputs from the prelimbic cortex and midline regions of the thalamus. However, little is known about whether and how representations differ across these input pathways. By comparing these inputs during a reinforcement learning task in mice, we discovered that prelimbic cortical inputs preferentially represent actions and choices, whereas midline thalamic inputs preferentially represent cues. Choice-selective activity in the prelimbic cortical inputs is organized in sequences that persist beyond the outcome. Through computational modeling, we demonstrate that these sequences can support the neural implementation of reinforcement-learning algorithms, in both a circuit model based on synaptic plasticity and one based on neural dynamics. Finally, we test and confirm a prediction of our circuit models by direct manipulation of nucleus accumbens input neurons.

Original languageEnglish (US)
Article number110756
JournalCell Reports
Volume39
Issue number7
DOIs
StatePublished - May 17 2022

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

Keywords

  • circuit modeling
  • CP: Neuroscience
  • imaging
  • learning
  • nucleus accumbens
  • optogenetics
  • prelimbic
  • reinforcement learning
  • thalamus

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