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Modeling pattern noise in responses of fly motion detectors to naturalistic scenes

Insects have a very efficient visual system that helps them to perform extraordinarily complicated navigational acts and precisely controlled aerobatic flight. Physiological evidence suggests that flight control is guided by a small system of 'tangential' neurons tuned to very specific types of complex motion by the way that they collate information from local motion detectors. One class of tangential neurons, the 'horizontal system' (HS) neurons, respond with opponent graded responses to yaw stimuli. Using the results of physiological experiments, we have developed a model, based on an array of Reichardt correlators, for the receptive field of HS neurons that view optical flow along the equator. Our model incorporates additional non-linearities that mimic known properties of the insect motion pathway, including logarithmic encoding of luminance, saturation and motion adaptation (adaptive gain-control). In this paper, we compare the response of our elaborated model with fly HS neuron responses to naturalistic image panoramas. Such responses are dominated by noise which is largely non-random. Deviations in the correlator response are likely due to the structure of the visual scene, which we term "Pattern noise". To investigate the influence of anisotropic features in producing pattern noise, we presented a panoramic image at various initial positions, and versions of the same image modified to disrupt vertical contours. We conclude that the response of the fly neurons shows evidence of local saturation at key stages in the motion pathway. This saturation reduces the effect of pattern noise and improves the coding of velocity. Our model provides an excellent basis for the development of biomimetic yaw sensors for robotic applications.