Gaussian Processes for Prediction of Homing Pigeon Flight Trajectories

We construct and apply a stochastic Gaussian Process (GP) model of flight trajectory generation for pigeons trained to home from specific release sites. The model shows increasing predictive power as the birds become familiar with the sites, mirroring the animal's learning process. We show how the increasing similarity between successive flight trajectories can be used to infer, with increasing accuracy, an idealised route that captures the repeated spatial aspects of the bird's flight. We subsequently use techniques associated with reduced-rank GP approximations to objectively identify the key waypoints used by each bird to memorise its idiosyncratic habitual route between the release site and the home loft.