Closed-Loop Estimation of Retinal Network Sensitivity by Local Empirical Linearization

Abstract

Understanding how sensory systems process information depends crucially on identifying which features of the stimulus drive the response of sensory neurons, and which ones leave their response invariant. This task is made difficult by the many non-linearities that shape sensory processing. Here we present a novel perturbative approach to understand information processing by sensory neurons, where we linearize their collective response locally in stimulus space. We added small perturbations to reference stimuli and tested if they triggered visible changes in the responses, adapting their amplitude according to the previous responses with closed-loop experiments. We developed a local linear model that accurately predicts the sensitivity of the neural responses to these perturbations. Applying this approach to the rat retina, we estimated the optimal performance of a neural decoder and showed that the non-linear sensitivity of the retina is consistent with an efficient encoding of stimulus information. Our approach can be used to characterize experimentally the sensitivity of neural systems to external stimuli locally, quantify experimentally the capacity of neural networks to encode sensory information, and relate their activity to behaviour.

Significance Statement Understanding how sensory systems process information is an open challenge mostly because these systems have many unknown nonlinearities. A general approach to studying nonlinear systems is to expand their response perturbatively. Here we apply such a method experimentally to understand how the retina processes visual stimuli. Starting from a reference stimulus, we tested whether small perturbations to that reference (chosen iteratively using closed-loop experiments) triggered visible changes in the retinal responses. We then inferred a local linear model to predict the sensitivity of the retina to these perturbations, and showed that this sensitivity supported an efficient encoding of the stimulus. Our approach is general and could be used in many sensory systems to characterize and understand their local sensitivity to stimuli.