Establishing the role of dopamine in Vocal Learning in Songbirds

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Project Summary: During behavior, the brain is constantly bombarded with sensory feedback. Learning to produce an appropriate motor response upon integrating that sensory information and processing it is essential for the survival of any organism. Such sensorimotor learning is always dependent on reinforcement cues, whether those cues are provided externally or through an internal evaluation of an organism's sensory feedback. While recent studies, including our own work, have implicated a role for dopamine in reinforcement learning, how dopamine conveys information about an external reinforcer in a naturally occurring behavior is still unclear. The proposed project will study reinforcement cue based learning in a natural behavior in Bengalese finches. Bengalese finches perform a stereotypical learned motor behavior (song) that is heavily dependent on sensory feedback for maintenance. Their song consists of a series of rapidly produced vocal gestures (“syllables”). Though their songs are usually stable in adulthood, our lab has developed a reinforcement learning paradigm that drives adult birds to change one particular acoustic feature of their song (pitch). Thus our proposal will use this learning paradigm to elucidate the role of dopamine in associating an external reinforcement cue with a particular component of behavior that is then changed. Our central hypothesis is that dopamine performs two major distinct computations in reinforcement learning and that these computations are modulated by the two broad subtypes of dopamine receptors. We will test our hypothesis through two specific aims. Our first aim uses Bayesian Inference to elucidate these computations underlying reinforcement learning and identify the relative contributions each computation makes in birds that learned successfully. Our second aim uses pharmacological manipulations to establish the role of individual dopamine receptor pathways in reinforcement learning. To conclude, our studies will greatly enhance our understanding of the neural computations underlying reinforcement learning. Such understanding would furthermore help in identifying and eventually treating learning deficits that occur following traumatic brain injury or other neurological insults.