Ca2+-dependent metarhodopsin inactivation mediated by calmodulin and NINAC myosin III

SummaryPhototransduction in flies is the fastest known G protein-coupled signaling cascade, but how this performance is achieved remains unclear. Here, we investigate the mechanism and role of rhodopsin inactivation. We determined the lifetime of activated rhodopsin (metarhodopsin = M∗) in whole-cell recordings from Drosophila photoreceptors by measuring the time window within which inactivating M∗ by photoreisomerization to rhodopsin could suppress responses to prior illumination. M∗ was inactivated rapidly (τ ∼20 ms) under control conditions, but ∼10-fold more slowly in Ca2+-free solutions. This pronounced Ca2+ dependence of M∗ inactivation was unaffected by mutations affecting phosphorylation of rhodopsin or arrestin but was abolished in mutants of calmodulin (CaM) or the CaM-binding myosin III, NINAC. This suggests a mechanism whereby Ca2+ influx acting via CaM and NINAC accelerates the binding of arrestin to M∗. Our results indicate that this strategy promotes quantum efficiency, temporal resolution, and fidelity of visual signaling

A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidance

The most versatile and robust flying machines are still those produced by nature through evolution. The solutions to the 6 DOF control problem faced by these machines are implemented in extremely small neuronal structures comprising thousands of neurons. Hence, the biological principles of flight control are not only very effective but also efficient in terms of their implementation. An important question is to what extent these principles can be generalized to man-made flying platforms. Here, this question is investigated in relation to the computational and behavioral principles of the opto-motor system of the fly and locust. The aim is to provide a control infrastructure based only on biologically plausible and realistic neuronal models of the insect opto-motor system. It is shown that relying solely on vision, biologically constrained neuronal models of the fly visual system suffice for course stabilization and altitude control of a blimp-based UAV. Moreover, the system is augmented with a collision avoidance model based on the Lobula Giant Movement Detector neuron of the Locust. It is shown that the biologically constrained course stabilization model is highly robust and that the combined model is able to perform autonomous indoor flight