The neural circuit implementation of distributional reinforcement learning

Distributional reinforcement learning (RL) algorithms outperform traditional RL by representing the full probability distribution of future outcomes rather than its mean. Neural signatures consistent with distributional coding have been observed in dopamine neurons and in striatal value representations. However, how distributional RL is implemented within the anatomical circuit organization of the basal ganglia remains unknown. Uncovering how representations of outcome distributions are organized across brain circuits has broad implications for both fundamental and clinical neuroscience, providing new insights into how the brain represents uncertainty and how these representations shape risk-sensitive learning and decision-making. Here, we investigated how representations of outcome distributions are organized across dopamine projections to the striatum by combining striatum-wide fiber photometry of dopamine axonal activity with Neuropixels recordings from projection-defined dopamine neurons. We found that distributional reward prediction error signals are preferentially encoded in lateral ventral striatum-projecting dopamine neurons, but that reward distribution information is shared between the ventral and the dorsal striatum. Additionally, an anatomical gradient of optimism in the midbrain supports the diversity of value predictions required for distributional RL. Our findings extend the actor-critic framework proposed for the basal ganglia by revealing distributional prediction errors in a subpopulation of mesolimbic dopamine neurons capable of training parallel striatal regions thought to implement different types of behavioral control.