Changes in behavioral state modify neural activity in many systems. In some vertebrates such modulation has been observed and interpreted in the context of attention and sensorimotor coordinate transformations. Here we report state-dependent activity modulations during walking in a visual-motor pathway of Drosophila. We used two-photon imaging to monitor intracellular calcium activity in motion-sensitive lobula plate tangential cells (LPTCs) in head-fixed Drosophila walking on an air-supported ball. Cells of the horizontal system (HS)–a subgroup of LPTCs–showed stronger calcium transients in response to visual motion when flies were walking rather than resting. The amplified responses were also correlated with walking speed. Moreover, HS neurons showed a relatively higher gain in response strength at higher temporal frequencies, and their optimum temporal frequency was shifted toward higher motion speeds. Walking-dependent modulation of HS neurons in the Drosophila visual system may constitute a mechanism to facilitate processing of higher image speeds in behavioral contexts where these speeds of visual motion are relevant for course stabilization.

The body of an animal determines how the nervous system produces behavior. Therefore, detailed modeling of the neural control of sensorimotor behavior requires a detailed model of the body. Here we contribute an anatomically-detailed biomechanical whole-body model of the fruit fly Drosophila melanogaster in the MuJoCo physics engine. Our model is general-purpose, enabling the simulation of diverse fly behaviors, both on land and in the air. We demonstrate the generality of our model by simulating realistic locomotion, both flight and walking. To support these behaviors, we have extended MuJoCo with phenomenological models of fluid forces and adhesion forces. Through data-driven end-to-end reinforcement learning, we demonstrate that these advances enable the training of neural network controllers capable of realistic locomotion along complex trajectories based on high-level steering control signals. With a visually guided flight task, we demonstrate a neural controller that can use the vision sensors of the body model to control and steer flight. Our project is an open-source platform for modeling neural control of sensorimotor behavior in an embodied context.Competing Interest StatementThe authors have declared no competing interest.

An important strategy for efficient neural coding is to match the range of cellular responses to the distribution of relevant input signals. However, the structure and relevance of sensory signals depend on behavioral state. Here, we show that behavior modifies neural activity at the earliest stages of fly vision. We describe a class of wide-field neurons that provide feedback to the most peripheral layer of the Drosophila visual system, the lamina. Using in vivo patch-clamp electrophysiology, we found that lamina wide-field neurons respond to low-frequency luminance fluctuations. Recordings in flying flies revealed that the gain and frequency tuning of wide-field neurons change during flight, and that these effects are mimicked by the neuromodulator octopamine. Genetically silencing wide-field neurons increased behavioral responses to slow-motion stimuli. Together, these findings identify a cell type that is gated by behavior to enhance neural coding by subtracting low-frequency signals from the inputs to motion detection circuits.