Project Summary Natural vision relies on segmenting spatially distributed stimuli within the visual environment and encoding them according to spatial context. For neurons in the primary visual cortex (V1), their response depends on the variance of visual stimuli between the classical receptive field and the surround. This property known as “surround modulation” powerfully influences sensory coding but its magnitude and sign flexibly depends on the degree of similarity of features, such as orientation, between the center and surround. The specific V1 circuits that explain orientation dependence of surround modulation are largely unknown. This project aims to elucidate the synaptic and circuit mechanisms of orientation dependence of surround suppression through an all-in-vivo approach combining two-photon (2P) targeted whole-cell electrophysiology, single-cell resolution 2P holographic optogenetics to map synaptic connectivity, and 2P calcium imaging to record population activity. The hypothesis of this project is that the orientation dependence of surround suppression depends on orientation-tuned somatostatin (SST) interneuron-mediated lateral inhibition via co-tuned excitation from the surround. As SST interneurons respond strongly to large iso-oriented but not cross-oriented visual stimulation where the center and surround are orthogonally oriented, this suggests that the tuning of SST interneurons to spatial context is key to feature dependence of surround suppression. To test the core hypothesis of tuning-specific connectivity driving feature-preference across retinotopic space, the aims in this project will first examine the synaptic drive in SST interneurons that gives rise to their selectivity for relative orientation of the center and surround, and next use a novel combinatory in vivo 2p calcium imaging/2p holographic optogenetic approach to test whether orientation-specific connectivity supports the physiological tuning of V1 SST neurons and pyramidal cells to contextual stimuli. These experiments will not only provide a mechanistic understanding of aspects of visual computation, but also demonstrate conceptual and technical advances that may be applied broadly to other key questions in neuroscience.