Reinforcement Mechanisms for Learning Vocal Behaviors - Renewal - 1

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PROJECT SUMMARY How skilled behaviors like speech and language are actively maintained throughout life is not well understood and still poorly studied. Our research program uses songbird vocal learning and vocal production to understand how forebrain circuits and reinforcement mechanisms are used to acquire and then maintain learned vocalizations. Our research has helped demonstrate that midbrain dopaminergic circuits bidirectionally guide learned changes in song in a manner consistent with them functioning as reward prediction error signals envisaged by reinforcement models. Building from this, we turn our attention to understand how the basal ganglia and dopaminergic circuits support the lifelong maintenance of behavior. We hypothesize that predictive dopaminergic signals safeguard the lifelong maintenance of natural behaviors. Our initial studies provide a glimpse at central mechanisms sufficient to initiate the long term decrystallization of a previously learned and internally reinforced natural behavior. Using a variety of cutting-edge approaches that we have optimized for circuit interrogation in songbirds, we aim to dissect the cellular and synaptic mechanisms associated with song decrystallization, and the role of circuit nodes downstream of dopaminergic pathways in song maintenance and song decrystallization. In the first aim we will test the role of predictive dopaminergic signals in the long-term maintenance of adult zebra finch song using optogenetic manipulations and functional imaging of dopamine activity in adult animals. In the second aim we will examine the cellular and synaptic mechanisms of song decrystallization. In the third aim we will test the role of pallidal-thalamic circuits downstream of dopaminergic striatal pathways in the implementation and rescue of song decrystallization. Together, these studies can provide fundamental and mechanistic insights into how the brain continuously monitors and updates behavior to maintain expert performance and reveal what happens when this process goes awry.