Distributional coding of associative learning within projection-defined populations of midbrain dopamine neurons
Midbrain dopamine neurons are thought to play key roles in learning by conveying the difference between expected and actual outcomes. While this teaching signal is often considered to be uniform, recent evidence instead supports diversity in dopamine signaling. However, it remains poorly understood how heterogeneous signals might be organized to facilitate the role of downstream circuits mediating distinct aspects of behavior. Here we investigated the organizational logic of dopaminergic signaling by recording and labeling individual midbrain dopamine neurons during associative behavior. We defined combinations of protein expression and cellular localization to sort recorded neurons according to the striatal regions they innervate. Our findings show that reward information and task variables are not only heterogeneously encoded, with multiplexing, but also differentially distributed across populations of dopamine neurons projecting to different regions of striatum. These data, supported by computational modelling, indicate that such distributional coding can maximize dynamic range and tailor dopamine signals to facilitate the specialized roles of different striatal regions.