SUMMARY The nervous system of social species has evolved to perceive and evaluate signals within a social context. Social information therefore must impact how the brain processes information, yet little is still known about how the brain integrates social information to produce actions in a social context. This lack of knowledge exists in part because social context is difficult to quantify and because the majority of studies are performed in species that do not have a particularly rich social structure. Here we propose to study the brown-headed cowbird (Molothrus ater), a highly gregarious songbird species whose social behavior has been well studied and where vocal and non-vocal communication signals form a central and critical component of its social system. We have created a “smart aviary” equipped with cameras and microphones that is capable of monitoring behavior in each individual during the entire breeding season. Our aim is to create a fully automated system using computer vision and machine learning technology to evaluate moment-to- moment behavioral interactions between all member of the group (9 females and 7 males) over the entire breeding season. We have assembled an interdisciplinary team of engineers, neurobiologists and computational scientists, to create a platform where we can record dynamics quantify learning directly the segment invasive in enable social develop quantify associated social and evaluate how brain are shaped within a complex social context over an ethologically relevant timescale. To moment-to-moment behavior in each individual bird, we are developing a novel machine approach that tracks each bird and predicts its position, orientation, pose, and shape from images using artificial neural networks and a 3D articulated mesh model. By collecting output of the model over consecutive frames we will obtain a pose trajectory, which we will and classify into discrete behaviora l types. We also aim to develop a miniature non- wirelessly powered and transmitting recording device optimized for long duration recording our aviary that critically does not impact bird individual or social behavior. Such a device would us to link neural activation patterns to discrete behavioral events (e.g. male song) within the context in which these specific events occurred. Supplied with our rich dataset, we aim to mathematical tools necessary to generate social network models that will allow us to the specific state of the bird social network associated with neural activation patterns with individual behavioral events. To the best of our nowledge, the proposal to link network state to neural activation in a precise quantitative manner has never before been , k attempted. Through these efforts, we will be well positioned to subsequently pursue a Targeted Brain Circuits Projects R01 to investigate in a quantitative manner how social context influences brain activity.