Testing optokinetic head or eye movements is an established method to determine visual performance of laboratory animals, including chickens, guinea pigs, mice, or fish. It is based on the optokinetic reflex which causes the animals to track a drifting stripe pattern with eye and head movements. We have developed an improved version of the optomotor test with better control over the stimulus parameters, as well as a high degree of automation. The stripe pattern is presented on computer monitors surrounding the animal. By tracking the head position of freely moving animals in real time, the visual angle under which the stripes of the pattern appeared was kept constant even for changing head positions. Furthermore, an algorithm was developed for automated evaluation of the tracking performance of the animal. Comparing the automatically determined behavioral score with manual assessment of the animals' tracking behavior confirmed the reliability of our methodology. As an example, we reproduced the known contrast sensitivity function of wild type mice. Furthermore, the progressive decline in visual performance of a mouse model of retinal degeneration, rd10, was demonstrated.

No abstract

No abstract

Zebrafish pretectal neurons exhibit specificities for large-field optic flow patterns associated with rotatory or translatory body motion. We investigate the hypothesis that these specificities reflect the input statistics of natural optic flow. Realistic motion sequences were generated using computer graphics simulating self-motion in an underwater scene. Local retinal motion was estimated with a motion detector and encoded in four populations of directionally tuned retinal ganglion cells, represented as two signed input variables. This activity was then used as input into one of two learning networks: a sparse coding network (competitive learning) and backpropagation network (supervised learning). Both simulations develop specificities for optic flow which are comparable to those found in a neurophysiological study [8], and relative frequencies of the various neuronal responses are best modeled by the sparse coding approach. We conclude that the optic flow neurons in the zebrafish pretectum do reflect the optic flow statistics. The predicted vectorial receptive fields show typical optic flow fields but also "Gabor" and dipole-shaped patterns that likely reflect difference fields needed for reconstruction by linear superposition.

No abstract