Implementation of saturation for modelling pattern noise using naturalistic stimuli

Abstract

Insects with their amazing visual system are able to perform exceptional navigational feats. In order to understand how they perform motion detection and velocity estimation, much work has been done in the past 40 years and many models of motion detection have been proposed. One of the earliest and most prominent models is the Reichardt correlator model. We have elaborated the Reichardt correlator model to include additional non-linearities that mimic known properties of the insect motion pathway, including logarithmic encoding of luminance and saturation at various stages of processing. In this paper, we compare the response of our elaborated model with recordings from fly HS neurons 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. Pattern noise is investigated by implementing saturation at different stages in our model and comparison of each of these models with the physiological data from the fly is performed using cross covariance technique.