Optimal sensor design for estimating local velocity in natural environments

Motion coding in the brain undoubtedly reflects the statistics of retinal image motion occurring in the natural environment. To characterize these statistics it is useful to measure motion in artificial movies derived from simulated environments where the "ground truth" is known precisely. Here we consider the problem of coding retinal image motion when an observer moves through an environment. Simulated environments were created by combining the range statistics of natural scenes with the spatial statistics of natural images. Artificial movies were then created by moving along a known trajectory at a constant speed through the simulated environments. We find that across a range of environments the optimal integration area of local motion sensors increases logarithmically with the speed to which the sensor is tuned. This result makes predictions for cortical neurons involved in heading perception and may find use in robotics applications.