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Zebrafish pretectal neurons exhibit specificities for large-field optic flowpatterns associated with rotatory or translatory body motion. We investigatethe hypothesis that these specificities reflect the input statistics of naturaloptic flow. Realistic motion sequences were generated using computer graphicssimulating self-motion in an underwater scene. Local retinal motion wasestimated with a motion detector and encoded in four populations ofdirectionally tuned retinal ganglion cells, represented as two signed inputvariables. This activity was then used as input into one of two learningnetworks: a sparse coding network (competitive learning) and backpropagationnetwork (supervised learning). Both simulations develop specificities for opticflow which are comparable to those found in a neurophysiological study (Kubo etal. 2014), and relative frequencies of the various neuronal responses are bestmodeled by the sparse coding approach. We conclude that the optic flow neuronsin the zebrafish pretectum do reflect the optic flow statistics. The predictedvectorial receptive fields show typical optic flow fields but also "Gabor" anddipole-shaped patterns that likely reflect difference fields needed forreconstruction by linear superposition.

Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons