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
Affiliations
Nathan F. Parker
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
Avinash Baidya
Center for Neuroscience, University of California, Davis, Davis, CA 95616, USA; Department of Physics and Astronomy, University of California, Davis, Davis, CA 95616, USA
Julia Cox
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
Laura M. Haetzel
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
Anna Zhukovskaya
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
Malavika Murugan
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
Ben Engelhard
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
Mark S. Goldman
Center for Neuroscience, University of California, Davis, Davis, CA 95616, USA; Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA 95616, USA; Department of Ophthalmology and Vision Science, University of California, Davis, Davis, CA 95616, USA; Corresponding author
Ilana B. Witten
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Department of Psychology, Princeton University, Princeton, NJ 08544, USA; Corresponding author
Summary: 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.
