Project Summary Learning and performing complex skills such as speech or music requires precise control of motor variability. While elevated motor variability can spur the learning of new behaviors, excessive variability can impair performance of learned skills. How the brain controls motor variability during learning and in expert performance remains unclear. Intriguingly, the basal ganglia (BG) is an important source of motor variability in both health and disease, and is a key site where dopamine (DA) reinforces more successful behaviors. Indeed, the BG’s ability to regulate motor variability is especially critical for complex sequential skills such as speech, where variability can arise at both the level of elementary motor “syllables” and the sequential “syntax” in which these syllables are organized. How DA signaling in the BG influences motor variability during the learning of complex sequential skills akin to speech or music is poorly understood. Moreover, rather than acting alone, an emerging view is that DA signaling is strongly modulated by other signaling molecules, such as adenosine (Ado), which may track the metabolic costs associated with extensive motor practice. Here I will characterize how Ado and DA release in the BG are related to each other, to motor variability, and to the learning of vocal motor sequences. In direct service of BRAIN initiative goals, I will combine cutting-edge computational and optical tools along with an innovative molecular-genetic approach to dissect both neuromodulator and cell- type specific contributions to motor variability and learning. My Specific Aims are: 1) To image Ado and DA in the sBG during juvenile vocal learning. 2) To establish the necessity of sBG Ado to regulate variability and test for a direct link between Ado and DA release. 3) To genetically tag “indirect” and “direct” spiny neuron types and assess how Ado modulates their activity to influence song variability. Individually, each aim will move beyond a single-neuromodulator model of BG skill learning, and collectively they will help reveal fundamental mechanisms that control motor variability across learning and performance. I will conduct this research under the supervision of Drs. Richard Mooney, Josh Huang, and John Pearson, an interdisciplinary team of accomplished mentors that provides me with complementary expertise in behavioral, systems neuroscience, computational, and cutting-edge genetic techniques. In addition to my deep interest in understanding natural forms of behavioral learning, I bring my own expertise in analyzing behavior in concert with optical methods. This proposal will allow me to both deepen and broaden my expertise, and will provide significant training in novel behavioral, computational, genetic, and imaging techniques. This integrative approach to systems neuroscience and natural behavior will enhance my capabilities as an independent researcher while addressing BRAIN Initiative goals.