A metabotropic glutamate receptor agonist enhances visual signal fidelity in a mouse model of retinitis pigmentosa.

Many inherited retinal diseases target photoreceptors, which transduce light into a neural signal that is processed by the downstream visual system. As photoreceptors degenerate, physiological and morphological changes to retinal synapses and circuitry reduce sensitivity and increase noise, degrading visual signal fidelity. Here, we pharmacologically targeted the first synapse in the retina in an effort to reduce circuit noise without sacrificing visual sensitivity. We tested a strategy to partially replace the neurotransmitter lost when photoreceptors die with an agonist of receptors that ON bipolars cells use to detect glutamate released from photoreceptors. In rd10 mice, which express a photoreceptor mutation that causes retinitis pigmentosa (RP), we found that a low dose of the mGluR6 agonist l-2-amino-4-phosphonobutyric acid (L-AP4) reduced pathological noise induced by photoreceptor degeneration. After making in vivo electroretinogram recordings in rd10 mice to characterize the developmental time course of visual signal degeneration, we examined effects of L-AP4 on sensitivity and circuit noise by recording in vitro light-evoked responses from individual retinal ganglion cells (RGCs). L-AP4 decreased circuit noise evident in RGC recordings without significantly reducing response amplitudes, an effect that persisted over the entire time course of rod photoreceptor degeneration. Subsequent in vitro recordings from rod bipolar cells (RBCs) showed that RBCs are more depolarized in rd10 retinas, likely contributing to downstream circuit noise and reduced synaptic gain, both of which appear to be ameliorated by hyperpolarizing RBCs with L-AP4. These beneficial effects may reduce pathological circuit remodeling and preserve the efficacy of therapies designed to restore vision.

Efficient coding of natural scenes improves neural system identification.

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage normative principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the "stand-alone" system identification model, it also produced more biologically plausible filters, meaning that they more closely resembled neural representation in early visual systems. We found these results applied to retinal responses to different artificial stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. The benefit of natural scene statistics became marginal, however, for predicting the responses to natural movies. In summary, our results indicate that efficiently encoding environmental inputs can improve system identification models, at least for noise stimuli, and point to the benefit of probing the visual system with naturalistic stimuli.

Natural environment statistics in the upper and lower visual field are reflected in mouse retinal specializations.

Pressures for survival make sensory circuits adapted to a species' natural habitat and its behavioral challenges. Thus, to advance our understanding of the visual system, it is essential to consider an animal's specific visual environment by capturing natural scenes, characterizing their statistical regularities, and using them to probe visual computations. Mice, a prominent visual system model, have salient visual specializations, being dichromatic with enhanced sensitivity to green and UV in the dorsal and ventral retina, respectively. However, the characteristics of their visual environment that likely have driven these adaptations are rarely considered. Here, we built a UV-green-sensitive camera to record footage from mouse habitats. This footage is publicly available as a resource for mouse vision research. We found chromatic contrast to greatly diverge in the upper, but not the lower, visual field. Moreover, training a convolutional autoencoder on upper, but not lower, visual field scenes was sufficient for the emergence of color-opponent filters, suggesting that this environmental difference might have driven superior chromatic opponency in the ventral mouse retina, supporting color discrimination in the upper visual field. Furthermore, the upper visual field was biased toward dark UV contrasts, paralleled by more light-offset-sensitive ganglion cells in the ventral retina. Finally, footage recorded at twilight suggests that UV promotes aerial predator detection. Our findings support that natural scene statistics shaped early visual processing in evolution.

Neural circuits in the mouse retina support color vision in the upper visual field.

Color vision is essential for an animal's survival. It starts in the retina, where signals from different photoreceptor types are locally compared by neural circuits. Mice, like most mammals, are dichromatic with two cone types. They can discriminate colors only in their upper visual field. In the corresponding ventral retina, however, most cones display the same spectral preference, thereby presumably impairing spectral comparisons. In this study, we systematically investigated the retinal circuits underlying mouse color vision by recording light responses from cones, bipolar and ganglion cells. Surprisingly, most color-opponent cells are located in the ventral retina, with rod photoreceptors likely being involved. Here, the complexity of chromatic processing increases from cones towards the retinal output, where non-linear center-surround interactions create specific color-opponent output channels to the brain. This suggests that neural circuits in the mouse retina are tuned to extract color from the upper visual field, aiding robust detection of predators and ensuring the animal's survival.

The temporal structure of the inner retina at a single glance.

The retina decomposes visual stimuli into parallel channels that encode different features of the visual environment. Central to this computation is the synaptic processing in a dense layer of neuropil, the so-called inner plexiform layer (IPL). Here, different types of bipolar cells stratifying at distinct depths relay the excitatory feedforward drive from photoreceptors to amacrine and ganglion cells. Current experimental techniques for studying processing in the IPL do not allow imaging the entire IPL simultaneously in the intact tissue. Here, we extend a two-photon microscope with an electrically tunable lens allowing us to obtain optical vertical slices of the IPL, which provide a complete picture of the response diversity of bipolar cells at a "single glance". The nature of these axial recordings additionally allowed us to isolate and investigate batch effects, i.e. inter-experimental variations resulting in systematic differences in response speed. As a proof of principle, we developed a simple model that disentangles biological from experimental causes of variability and allowed us to recover the characteristic gradient of response speeds across the IPL with higher precision than before. Our new framework will make it possible to study the computations performed in the central synaptic layer of the retina more efficiently.

An arbitrary-spectrum spatial visual stimulator for vision research.

Visual neuroscientists require accurate control of visual stimulation. However, few stimulator solutions simultaneously offer high spatio-temporal resolution and free control over the spectra of the light sources, because they rely on off-the-shelf technology developed for human trichromatic vision. Importantly, consumer displays fail to drive UV-shifted short wavelength-sensitive photoreceptors, which strongly contribute to visual behaviour in many animals, including mice, zebrafish and fruit flies. Moreover, many non-mammalian species feature more than three spectral photoreceptor types. Here, we present a flexible, spatial visual stimulator with up to six arbitrary spectrum chromatic channels. It combines a standard digital light processing engine with open source hard- and software that can be easily adapted to the experimentalist's needs. We demonstrate the capability of this general visual stimulator experimentally in the in vitro mouse retinal whole-mount and the in vivo zebrafish. With this work, we intend to start a community effort of sharing and developing a common stimulator design for vision research.