The temporal structure of the inner retina at a single glance

Most discriminative stimuli for functional cell type clustering

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron.

Distributed feature representations of natural stimuli across parallel retinal pathways

How sensory systems extract salient features from natural environments and organize them across neural pathways is unclear. Combining single-cell and population two-photon calcium imaging in mice, we discover that retinal ON bipolar cells (second-order neurons of the visual system) are divided into two blocks of four types. The two blocks distribute temporal and spatial information encoding, respectively. ON bipolar cell axons co-stratify within each block, but separate laminarly between them (upper block: diverse temporal, uniform spatial tuning; lower block: diverse spatial, uniform temporal tuning). ON bipolar cells extract temporal and spatial features similarly from artificial and naturalistic stimuli. In addition, they differ in sensitivity to coherent motion in naturalistic movies. Motion information is distributed across ON bipolar cells in the upper and the lower blocks, multiplexed with temporal and spatial contrast, independent features of natural scenes. Comparing the responses of different boutons within the same arbor, we find that axons of all ON bipolar cell types function as computational units. Thus, our results provide insights into the visual feature extraction from naturalistic stimuli and reveal how structural and functional organization cooperate to generate parallel ON pathways for temporal and spatial information in the mammalian retina.

Differences in nonlinearities determine retinal cell types

Classifying neurons in different types is still an open challenge. In the retina, recent works have taken advantage of the ability to record from a large number of cells to classify ganglion cells into different types based on functional information. Although the first attempts in this direction used the receptive field properties of each cell to classify them, more recent approaches have proposed to cluster ganglion cells directly based on their response to stimuli. These two approaches have not been compared directly. Here, we recorded the responses of a large number of ganglion cells and compared two methods for classifying them into functional groups, one based on the receptive field properties, and the other one using directly their responses to stimuli with various temporal frequencies. We show that the response-based approach allows separation of more types than the receptive field-based method, leading to a better classification. This better granularity is due to the fact that the response-based method takes into account not only the linear part of ganglion cell function but also some of the nonlinearities. A careful characterization of nonlinear processing is thus key to allowing functional classification of sensory neurons.NEW & NOTEWORTHY In the retina, ganglion cells can be classified based on their response to visual stimuli. Although some methods are based on the modeling of receptive fields, others rely on responses to characteristic stimuli. We compared these two classes of methods and show that the latter provides a higher discrimination performance. We also show that this gain arises from the ability to account for the nonlinear behavior of neurons.

Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina

Motion sensing is a critical aspect of vision. We studied the representation of motion in mouse retinal bipolar cells and found that some bipolar cells are radially direction selective, preferring the origin of small object motion trajectories. Using a glutamate sensor, we directly observed bipolar cells synaptic output and found that there are radial direction selective and non-selective bipolar cell types, the majority being selective, and that radial direction selectivity relies on properties of the center-surround receptive field. We used these bipolar cell receptive fields along with connectomics to design biophysical models of downstream cells. The models and additional experiments demonstrated that bipolar cells pass radial direction selective excitation to starburst amacrine cells, which contributes to their directional tuning. As bipolar cells provide excitation to most amacrine and ganglion cells, their radial direction selectivity may contribute to motion processing throughout the visual system.

Retinal horizontal cells use different synaptic sites for global feedforward and local feedback signaling

In the outer plexiform layer (OPL) of the mammalian retina, cone photoreceptors (cones) provide input to more than a dozen types of cone bipolar cells (CBCs). In the mouse, this transmission is modulated by a single horizontal cell (HC) type. HCs perform global signaling within their laterally coupled network but also provide local, cone-specific feedback. However, it is unknown how HCs provide local feedback to cones at the same time as global forward signaling to CBCs and where the underlying synapses are located. To assess how HCs simultaneously perform different modes of signaling, we reconstructed the dendritic trees of five HCs as well as cone axon terminals and CBC dendrites in a serial block-face electron microscopy volume and analyzed their connectivity. In addition to the fine HC dendritic tips invaginating cone axon terminals, we also identified "bulbs," short segments of increased dendritic diameter on the primary dendrites of HCs. These bulbs are in an OPL stratum well below the cone axon terminal base and make contacts with other HCs and CBCs. Our results from immunolabeling, electron microscopy, and glutamate imaging suggest that HC bulbs represent GABAergic synapses that do not receive any direct photoreceptor input. Together, our data suggest the existence of two synaptic strata in the mouse OPL, spatially separating cone-specific feedback and feedforward signaling to CBCs. A biophysical model of a HC dendritic branch and voltage imaging support the hypothesis that this spatial arrangement of synaptic contacts allows for simultaneous local feedback and global feedforward signaling by HCs.

Non-telecentric two-photon microscopy for 3D random access mesoscale imaging

Diffraction-limited two-photon microscopy permits minimally invasive optical monitoring of neuronal activity. However, most conventional two-photon microscopes impose significant constraints on the size of the imaging field-of-view and the specific shape of the effective excitation volume, thus limiting the scope of biological questions that can be addressed and the information obtainable. Here, employing a non-telecentric optical design, we present a low-cost, easily implemented and flexible solution to address these limitations, offering a several-fold expanded three-dimensional field of view. Moreover, rapid laser-focus control via an electrically tunable lens allows near-simultaneous imaging of remote regions separated in three dimensions and permits the bending of imaging planes to follow natural curvatures in biological structures. Crucially, our core design is readily implemented (and reversed) within a matter of hours, making it highly suitable as a base platform for further development. We demonstrate the application of our system for imaging neuronal activity in a variety of examples in zebrafish, mice and fruit flies.

Electrically tunable lenses - eliminating mechanical axial movements during high-speed 3D live imaging

The successful investigation of photosensitive and dynamic biological events, such as those in a proliferating tissue or a dividing cell, requires non-intervening high-speed imaging techniques. Electrically tunable lenses (ETLs) are liquid lenses possessing shape-changing capabilities that enable rapid axial shifts of the focal plane, in turn achieving acquisition speeds within the millisecond regime. These human-eye-inspired liquid lenses can enable fast focusing and have been applied in a variety of cell biology studies. Here, we review the history, opportunities and challenges underpinning the use of cost-effective high-speed ETLs. Although other, more expensive solutions for three-dimensional imaging in the millisecond regime are available, ETLs continue to be a powerful, yet inexpensive, contender for live-cell microscopy.

Temperature and species-dependent regulation of browning in retrobulbar fat

Retrobulbar fat deposits surround the posterior retina and optic nerve head, but their function and origin are obscure. We report that mouse retrobulbar fat is a neural crest-derived tissue histologically and transcriptionally resembles interscapular brown fat. In contrast, human retrobulbar fat closely resembles white adipose tissue. Retrobulbar fat is also brown in other rodents, which are typically housed at temperatures below thermoneutrality, but is white in larger animals. We show that retrobulbar fat in mice housed at thermoneutral temperature show reduced expression of the brown fat marker Ucp1, and histological properties intermediate between white and brown fat. We conclude that retrobulbar fat can potentially serve as a site of active thermogenesis, that this capability is both temperature and species-dependent, and that this may facilitate regulation of intraocular temperature.

Electrical Imaging of Light-Induced Signals Across and Within Retinal Layers

The mammalian retina processes sensory signals through two major pathways: a vertical excitatory pathway, which involves photoreceptors, bipolar cells, and ganglion cells, and a horizontal inhibitory pathway, which involves horizontal cells, and amacrine cells. This concept explains the generation of an excitatory center—inhibitory surround sensory receptive fields—but fails to explain the modulation of the retinal output by stimuli outside the receptive field. Electrical imaging of light-induced signal propagation at high spatial and temporal resolution across and within different retinal layers might reveal mechanisms and circuits involved in the remote modulation of the retinal output. Here we took advantage of a high-density complementary metal oxide semiconductor-based microelectrode array and investigated the light-induced propagation of local field potentials (LFPs) in vertical mouse retina slices. Surprisingly, the LFP propagation within the different retinal layers depends on stimulus duration and stimulus background. Application of the same spatially restricted light stimuli to flat-mounted retina induced ganglion cell activity at remote distances from the stimulus center. This effect disappeared if a global background was provided or if gap junctions were blocked. We hereby present a neurotechnological approach and demonstrated its application, in which electrical imaging evaluates stimulus-dependent signal processing across different neural layers.

Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics

While multicompartment models have long been used to study the biophysics of neurons, it is still challenging to infer the parameters of such models from data including uncertainty estimates. Here, we performed Bayesian inference for the parameters of detailed neuron models of a photoreceptor and an OFF- and an ON-cone bipolar cell from the mouse retina based on two-photon imaging data. We obtained multivariate posterior distributions specifying plausible parameter ranges consistent with the data and allowing to identify parameters poorly constrained by the data. To demonstrate the potential of such mechanistic data-driven neuron models, we created a simulation environment for external electrical stimulation of the retina and optimized stimulus waveforms to target OFF- and ON-cone bipolar cells, a current major problem of retinal neuroprosthetics.

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.

Mice are able to discriminate colors, at least in the upper visual field. Here, the authors provide a comprehensive characterization of retinal circuits underlying this behavior.