Using the dragonflies, I propose to characterize biological systems-level encoding strategies in the spatial and temporal domains that enable the adaptability and modulation of context-dependant multimodal sensing. To accomplish this will require an interdisciplinary approach including behavioural, electrophysiological, system identification, and computational modelling techniques. First (Aim 1), I will characterize the adaptability and robustness of mechanosensors on the wings by measuring how morphological changes due to wing damage impact sensor activity. Next (Aim 2), I will characterize the transformation of detected mechanosensory information by interneurons that receive inputs directly from wing mechanosensors and other modalities and output to wing steering motor neurons. Thirdly, (Aim 3), I will identify the mechanisms that modulate motor control by combining visual and mechanosensory information to determine relative contributions of sensory inputs in the production of motor output. Future work, (Aim 4) will synthesize experimental results from previous aims into a physiologically informed model to capture the key functional properties which contribute to the adaptability and robustness of insect sensory systems.