Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons

TL;DR

This study demonstrates that zebrafish pretectal neurons' selectivities for optic flow are best explained by sparse coding of naturalistic motion patterns, aligning with neurophysiological data.

Contribution

It shows that sparse coding models can accurately predict the specificities of zebrafish pretectal neurons for optic flow patterns, highlighting the role of natural motion statistics.

Findings

Sparse coding models replicate neuronal response frequencies.

Predicted receptive fields include Gabor and dipole patterns.

Optic flow neuron selectivities reflect natural motion statistics.

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 et al. 2014), 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.