PROJECT SUMMARY A large body of work has revealed how motor cortex (M1) drives the refinement of emergent motor skills towards precise, automatic actions. Moreover, our lab’s findings implicate the emergence of corticostriatal (CS) functional connectivity between M1 and dorsolateral striatum (DLS) as essential to refinement of skills that involve both gross and fine motor movements, such as a skilled reach-to-grasp (RTG) task where rats execute precise reaches to retrieve reward pellets. However, while much is known about the evolution of M1/DLS activity towards predictable behavior output, little is known about how the motor system responds to large errors by allowing flexible adaptation of learnt skill through behavioral exploration. Here we use a new variant of the RTG task (where rats first learn reaching to one location before the pellet holder is moved to a non-overlapping position, termed ‘re-aiming’ task hereafter) to probe how the CS network enables behavioral flexibility in response to environmental changes. Our preliminary data shows that while rats eventually “re-aim” to the new pellet location, the process occurs only across days, rather than within day, and involves a transitory state of heightened motor variability. This suggests that “offline” consolidation during sleep plays a key role in mediating the balance between behavioral stability vs exploration, and the central hypothesis of this proposal is that non-rapid eye movement (NREM) sleep bidirectionally modulates CS connectivity to enable behavioral exploration. Here we use an interdisciplinary approach of exploratory data analysis, computational modeling, and causal manipulations to answer the above question. In Aim 1, we will assess how the temporal nesting of sleep spindles with slow oscillations and delta-waves modulates both behavioral switchover and theta frequency LFP coherence during reaching (the emergence of which has been shown to track with successful reaching behavior). In Aim 2, we will fit spiking data with a cross-area computational model to dissociate the dynamics of M1 vs DLS activity across the re-aiming paradigm to gain deeper insight into each region’s respective contributions to behavioral stability vs flexibility. And lastly in Aim 3, we will use closed-loop optogenetics to causally determine the contributions of sleep spindles to both behavioral stability and CS functional connectivity. Together, these experiments will further our understanding of sleep physiology as it relates to behavioral flexibility and lay a strong foundation towards sleep-based interventions for motor disorders after injury, stroke, and perhaps even mental health disorders (such as addiction or obsessive-compulsive disorder) that prominently implicate corticostriatal connectivity.