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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
(Kubo, F. et al., 2014, Neuron 81:1344-59), 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. For a full version of this paper, see
https://arxiv.org/abs/1805.01277.