Optogenetic targeting of AII amacrine cells restores retinal computations performed by the inner retina.

Most inherited retinal dystrophies display progressive photoreceptor cell degeneration leading to severe visual impairment. Optogenetic reactivation of inner retinal neurons is a promising avenue to restore vision in retinas having lost their photoreceptors. Expression of optogenetic proteins in surviving ganglion cells, the retinal output, allows them to take on the lost photoreceptive function. Nonetheless, this creates an exclusively ON retina by expression of depolarizing optogenetic proteins in all classes of ganglion cells, whereas a normal retina extracts several features from the visual scene, with different ganglion cells detecting light increase (ON) and light decrease (OFF). Refinement of this therapeutic strategy should thus aim at restoring these computations. Here we used a vector that targets gene expression to a specific interneuron of the retina called the AII amacrine cell. AII amacrine cells simultaneously activate the ON pathway and inhibit the OFF pathway. We show that the optogenetic stimulation of AII amacrine cells allows restoration of both ON and OFF responses in the retina, but also mediates other types of retinal processing such as sustained and transient responses. Targeting amacrine cells with optogenetics is thus a promising avenue to restore better retinal function and visual perception in patients suffering from retinal degeneration.

A biophysical model explains the spontaneous bursting behavior in the developing retina.

During early development, waves of activity propagate across the retina and play a key role in the proper wiring of the early visual system. During a particular phase of the retina development (stage II) these waves are triggered by a transient network of neurons, called Starburst Amacrine Cells (SACs), showing a bursting activity which disappears upon further maturation. The underlying mechanisms of the spontaneous bursting and the transient excitability of immature SACs are not completely clear yet. While several models have attempted to reproduce retinal waves, none of them is able to mimic the rhythmic autonomous bursting of individual SACs and reveal how these cells change their intrinsic properties during development. Here, we introduce a mathematical model, grounded on biophysics, which enables us to reproduce the bursting activity of SACs and to propose a plausible, generic and robust, mechanism that generates it. The core parameters controlling repetitive firing are fast depolarizing V-gated calcium channels and hyperpolarizing V-gated potassium channels. The quiescent phase of bursting is controlled by a slow after hyperpolarization (sAHP), mediated by calcium-dependent potassium channels. Based on a bifurcation analysis we show how biophysical parameters, regulating calcium and potassium activity, control the spontaneously occurring fast oscillatory activity followed by long refractory periods in individual SACs. We make a testable experimental prediction on the role of voltage-dependent potassium channels on the excitability properties of SACs and on the evolution of this excitability along development. We also propose an explanation on how SACs can exhibit a large variability in their bursting periods, as observed experimentally within a SACs network as well as across different species, yet based on a simple, unique, mechanism. As we discuss, these observations at the cellular level have a deep impact on the retinal waves description.

Bayesian Analysis of Perceived Eye Level.

To accurately perceive the world, people must efficiently combine internal beliefs and external sensory cues. We introduce a Bayesian framework that explains the role of internal balance cues and visual stimuli on perceived eye level (PEL)-a self-reported measure of elevation angle. This framework provides a single, coherent model explaining a set of experimentally observed PEL over a range of experimental conditions. Further, it provides a parsimonious explanation for the additive effect of low fidelity cues as well as the averaging effect of high fidelity cues, as also found in other Bayesian cue combination psychophysical studies. Our model accurately estimates the PEL and explains the form of previous equations used in describing PEL behavior. Most importantly, the proposed Bayesian framework for PEL is more powerful than previous behavioral modeling; it permits behavioral estimation in a wider range of cue combination and perceptual studies than models previously reported.