Abstract
The hypothesis that midbrain dopamine (DA) neurons broadcast an error signal for the prediction of reward (reward prediction error, RPE) is among the great successes of computational neuroscience1–3. However, recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global signal. Instead, when animals are placed in a high-dimensional environment, DA neurons in the ventral tegmental area (VTA) display substantial heterogeneity in the features to which they respond, while also having more consistent RPE-like responses at the time of reward. Here we introduce a new “Vector RPE” model that explains these findings, by positing that DA neurons report individual RPEs for a subset of a population vector code for an animal’s state (moment-to-moment situation). To investigate this claim, we train a deep reinforcement learning model on a navigation and decision-making task, and compare the Vector RPE derived from the network to population recordings from DA neurons during the same task. The Vector RPE model recapitulates the key features of the neural data: specifically, heterogeneous coding of task variables during the navigation and decision-making period, but uniform reward responses. The model also makes new predictions about the nature of the responses, which we validate. Our work provides a path to reconcile new observations of DA neuron heterogeneity with classic ideas about RPE coding, while also providing a new perspective on how the brain performs reinforcement learning in high dimensional environments.
Competing Interest Statement
The authors have declared no competing interest.
