  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.
