Sparse coding predicts optic flow specificities of zebrafish pretectal neurons

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.

Prediction of future input explains lateral connectivity in primary visual cortex

“…Sparse coding networks have been widely employed in modeling receptive fields and response properties 17,38,39 , and more recently have been applied to local connectivity in visual cortex. Sparse coding argues that the brain is optimized to represent stimuli efficiently such that only a small number of neurons are strongly activated at a given time.…”

“…The key contribution of the present work is in uniting different aspects of both short-range and long-range V1 connectivity with neuronal feature preferences under a single unsupervised learning objective. Sparse coding networks have been widely employed in modeling receptive fields and response properties 17,38,39 , and more recently have been applied to local connectivity in visual cortex. Sparse coding argues that the brain is optimized to represent stimuli efficiently such that only a small number of neurons are strongly activated at a given time.…”

Exploitation of Image Statistics with Sparse Coding in the Case of Stereo Vision

“…We believe that the selectivity for patterns that are linked to physical causes is a general property of sparse representations of sensory data. For example, we have recently shown that applying sparse coding to optic flow data yields rather unexpected kernel shapes, which are tuned to directions of egomotion (Ecke et al, 2020). Screening for such selectivities can be a starting point for identifying the cues that are at the core of inference and it can yield predictions for properties of processing in diverse biological systems.…”