PROJECT SUMMARY/ABSTRACT While significant advances have been made over the past decade in understanding how sensory responses are modulated in the awake behaving animal, significant gaps in our knowledge still remain about how non-sensory internal factors related to changes in behavioral state can impact sensory signaling. First, our current understanding of how visual signaling is modulated by behavioral state is still limited to cortex with less emphasis on the inputs to cortex such as those coming from the thalamus, which is a brain region strongly modulated by changes in behavioral state. Second, while it has been widely recognized that spontaneous factors such as running, whisking, and changes in arousal can modulate visual activity, very few studies take these factors into account. Without accounting for non-sensory factors, we cannot fully understand the neural circuitry underlying sensory responses during active behavior. Thus, as the field of systems neuroscience moves towards understanding sensory processing in awake behaving animals, there is a critical need to understand how early sensory processing is modulated by shifts in the waking behavioral state. The proposed project will combine the recording of non-sensory internal factors such as arousal and spontaneous face movements with simultaneous extracellular recordings from the dorsal lateral geniculate nucleus (dLGN) of the thalamus while animals are actively performing a visual spatial detection task. By dissociating non-sensory variables such as arousal and movement from task engagement and visual spatial attention, I can test my main hypothesis that shifts in arousal, task-engagement, and spatial attention, have distinct effects on activity in dLGN. This hypothesis will be tested through three Aims. First, in Aim 1 I will determine the relationship between arousal level, face movement, and visual response properties in dLGN in mice passively viewing visual stimuli. In Aim 2, I will quantify how stimulus detection accuracy in a behavioral task impacts visual response properties in dLGN while simultaneously recording non-sensory variables, and use neural data combined with non-sensory factors to predict behavioral outcome on a trial-by-trial basis with a generalized linear model (GLM) to understand which predictors significantly contribute to behavioral outcome. In Aim 3, I will determine how visual spatial attention modulates visual responses in dLGN while recording non- sensory variables and dissociate changes in cognitive state from changes in arousal and movement to gain a more complete understanding of the circuit mechanisms of visual spatial attention. Together, these experiments will yield a detailed characterization of how sensory activity and non-sensory activity can be both complementary and dissociable at the level of dLGN. This work will underscore both a critical need to account for non-sensory internal variables, and the impact of shifts in behavioral state on early...