Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding

TL;DR

This study introduces a method to measure retinal sensitivity to stimulus perturbations, revealing a frequency-dependent peak that supports the efficient coding hypothesis by maximizing information transmission.

Contribution

Developed a closed-loop experimental approach to characterize retinal sensitivity and demonstrate a frequency peak consistent with efficient coding principles.

Findings

Retinal response sensitivity peaks at a specific frequency.

Sensitivity analysis aligns with maximizing information transmission.

Method applicable to other sensory systems without known encoding models.

Abstract

According to the theory of efficient coding, sensory systems are adapted to represent natural scenes with high fidelity and at minimal metabolic cost. Testing this hypothesis for sensory structures performing non-linear computations on high dimensional stimuli is still an open challenge. Here we develop a method to characterize the sensitivity of the retinal network to perturbations of a stimulus. Using closed-loop experiments, we explore selectively the space of possible perturbations around a given stimulus. We then show that the response of the retinal population to these small perturbations can be described by a local linear model. Using this model, we computed the sensitivity of the neural response to arbitrary temporal perturbations of the stimulus, and found a peak in the sensitivity as a function of the frequency of the perturbations. Based on a minimal theory of sensory processing, we argue that this peak is set to maximize information transmission. Our approach is relevant to testing the efficient coding hypothesis locally in any context where no reliable encoding model is known.