A feature-specific prediction error model explains dopaminergic heterogeneity

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Abstract
The hypothesis that midbrain dopamine (DA) neurons broadcast a reward prediction error (RPE) is among the great successes of computational neuroscience. However, recent results contradict a core aspect of this theory: specifically that the neurons convey a scalar, homogeneous signal. While the predominant family of extensions to the RPE model replicates the classic model in multiple parallel circuits, we argue that these models are ill suited to explain reports of heterogeneity in task variable encoding across DA neurons. Instead, we introduce a complementary ‘feature-specific RPE’ model, positing that individual ventral tegmental area DA neurons report RPEs for different aspects of an animal’s moment-to-moment situation. Further, we show how our framework can be extended to explain patterns of heterogeneity in action responses reported among substantia nigra pars compacta DA neurons. This theory reconciles new observations of DA heterogeneity with classic ideas about RPE coding while also providing a new perspective of how the brain performs reinforcement learning in high-dimensional environments.
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Funding Information
  • United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (W911NF-16-1-0474, W911NF1710554)
  • NIH K99 Grant
  • U.S. Department of Health & Human Services | NIH | Center for Scientific Review (U19 NS104648-01)
  • Brain Research Foundation
  • New York Stem Cell Foundation
  • NIH R01 grant, U19 Grant, ARO, Brain Research Foundation, Simons Collaboration on the Global Brain, and New York Stem Cell Foundation
  • NSF GRFP

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