Differential response of the retinal neural code with respect to the sparseness of natural images

TL;DR

This study investigates how the sparseness of natural images influences the neural coding in the retina, revealing that retinal ganglion cell spike timing adapts to natural image statistics.

Contribution

Introduces a new class of stimuli with controlled sparseness and demonstrates its effect on retinal neural responses, linking image statistics to neural coding.

Findings

Retinal spike timing reliability varies with image sparseness

Retinal responses adapt to natural image statistics

Spike pattern distribution reflects image sparseness

Abstract

Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. To investigate the role of this sparseness in the efficiency of the neural code, we designed a new class of random textured stimuli with a controlled sparseness value inspired by measurements of natural images. Then, we tested the impact of this sparseness parameter on the firing pattern observed in a population of retinal ganglion cells recorded ex vivo in the retina of a rodent, the Octodon degus. These recordings showed in particular that the reliability of spike timings varies with respect to the sparseness with globally a similar trend than the distribution of sparseness statistics observed in natural images. These results suggest that the code represented in the spike pattern of ganglion cells may adapt to this aspect of the statistics of natural images.