Project Abstract How the brain reinforces neural activity to drive learning is a fundamental question in neuroscience. Dopamine is a critical neuromodulator that shapes neural circuits during reinforcement learning by modifying synaptic connections that encode rewarding behaviors. However, the precise synaptic mechanisms by which dopamine-dependent plasticity sculpts behaviorally relevant cortical ensembles remain poorly understood. A major barrier to addressing this question has been the lack of experimental approaches that allow for real-time control of reinforcement signals while simultaneously tracking their effects on synaptic activity. Overcoming this limitation is essential for uncovering how dopamine influences synaptic connectivity and circuit function to drive adaptive behavior. In this proposal, I will utilize cutting-edge in vivo imaging technology, combined with newly developed opsins and sensors for observing and manipulating dopamine dynamics, to implement a novel brain-machine interface (BMI) paradigm to study the role of dopamine in reinforcement learning at the level of individual synapses. This research is structured across a K99 mentored phase and an R00 independent phase, with three specific aims. In Aim 1, I will employ a novel optical BMI paradigm combined with two-photon calcium imaging to characterize how dopamine-driven reinforcement learning reorganizes synaptic inputs onto behaviorally relevant cortical ensembles. In Aim 2, I will track functional synaptic activity during reinforcement learning to determine how dopamine directly alters synaptic activity strength and dynamics over time. Finally, in Aim 3, I will use genetically encoded dopamine sensors and optogenetics to map the spatiotemporal release of dopamine, and apply chemogenetic and pharmacological manipulations to assess where, when, and how dopamine drives synaptic plasticity in vivo. These experiments will leverage numerous advanced methodologies, some of which were developed