Alexandre Tiriac

The laboratory of Dr. Alexandre Tiriac is seeking an ambitious candidate for a postdoctoral research position. The Tiriac lab studies how various factors promote or impair the development of the nervous system with the goal of understanding the neural basis of cognitive disorders. Specifically, we are interested in recruiting a postdoctoral researcher interested in taking the lead on a project studying the role of brain states on the development of the nervous system. We study these questions in the mouse visual system, but opportunities exist to branch out to other sensory and motor systems. More information about our current research projects can be found here (https://www.tiriaclab.org/research). With mentorship from Dr. Tiriac, the postdoctoral researcher is expected to independently design and conduct experiments. Example research techniques include dissections, surgeries, calcium imaging using two-photon microscopy, electrophysiological recording using multielectrode arrays, processing brain tissue for histology, and computational techniques. The postdoctoral researcher is expected to perform data analyses, generate figures, and draft research manuscripts that will be submitted to neuroscience journals. Support will be provided for the postdoctoral researcher to attend at least one scientific conference a year to network and present data. If desired by the candidate, the postdoctoral researcher would be supported in the submission of competitive grant applications (e.g.: F32, K99/R00, LSRF, T32). A curated mentorship plan will be designed depending on the candidate’s career goal (industry vs tenure track position). The Tiriac lab strives to provide a supportive and inclusive research environment that fosters interdisciplinary training and collaborative exchange. The lab is based in Vanderbilt’s Department of Biological Sciences. We are also affiliated with the Vanderbilt Brain Institute and the Vanderbilt Vision Research Center. As part of so these programs, the Tiriac lab has access to numerous cores that enhance and speed up our research (currently used example cores: automated genotyping, microscopy core, histology core, and behavior core).

Dr. Erika Eggers

The Eggers Lab in the UArizona Departments of Physiology and Biomedical Engineering is seeking highly motivated candidates for the full-time position of Postdoctoral Research Associate I. Dr. Eggers’s NIH-funded research incorporates physiological light stimulation, electrophysiology, immunohistochemical and optogenetic approaches to identify cellular, synaptic and molecular mechanisms that underlie changes in retinal neurons in diabetes. The goal of current studies is to determine how retinal calcium and dopamine signaling are altered in multiple levels of the rod pathway during early stages of diabetes and to identify potential targets for treatment. More information on our current research interests can be found at: http://eggerslab.sites.arizona.edu/.

Magdalena Renner

The Institute of Molecular and Clinical Ophthalmology Basel (IOB) is seeking a highly motivated Research Assistant to join the Retinal Organoid Platform. IOB is a research institute combining basic and clinical research. Its mission is to drive innovations in understanding vision and its diseases and develop new therapies for vision loss. It is a place where your expertise will be valued, your abilities challenged, and your knowledge expanded. The Retinal Organoid Platform uses human retinal organoids derived from pluripotent stem cells as models for inherited retinal degeneration. Therefore, the Retinal Organoid Platform is involved in collecting and reprogramming into iPSC cells from patients with retinal disease as a resource for IOB researchers and collaborators around the globe. Furthermore, the Retinal Organoid Platform is introducing precise patient mutations into hiPSC by genome engineering. Your responsibilities: - In vitro culture of human induced pluripotent stem (iPSC) cells and retinal organoids - Gene editing of iPSC by CRISPR/Cas9, and full characterization of mutant cells - Characterization of iPSC and retinal organoids by various histology, molecular biology and microscopy techniques - Vector construction and molecular cloning - Applying and improving new methodologies to enhance the creation of mutant iPSC - Reprogramming of human primary cells to iPSC - Biobanking of primary cells and iPSC - Involvement in lab management and organization - Close collaboration with group members and IOB groups

Go with the visual flow: circuit mechanisms for gaze control during locomotion

Seeing a changing world through the eyes of coral fishes

Computational modelling of ocular pharmacokinetics

Pharmacokinetics in the eye is an important factor for the success of ocular drug delivery and treatment. Pharmacokinetic features determine the feasible routes of drug administration, dosing levels and intervals, and it has impact on eventual drug responses. Several physical, biochemical, and flow-related barriers limit drug exposure of anterior and posterior ocular target tissues during treatment during local (topical, subconjunctival, intravitreal) and systemic administration (intravenous, per oral). Mathematical models integrate joint impact of various barriers on ocular pharmacokinetics (PKs) thereby helping drug development. The models are useful in describing (top-down) and predicting (bottom-up) pharmacokinetics of ocular drugs. This is useful also in the design and development of new drug molecules and drug delivery systems. Furthermore, the models can be used for interspecies translation and probing of disease effects on pharmacokinetics. In this lecture, ocular pharmacokinetics and current modelling methods (noncompartmental analyses, compartmental, physiologically based, and finite element models) are introduced. Future challenges are also highlighted (e.g. intra-tissue distribution, prediction of drug responses, active transport).

Retinal input integration in excitatory and inhibitory neurons in the mouse superior colliculus in vivo

Why age-related macular degeneration is a mathematically tractable disease

Among all prevalent diseases with a central neurodegeneration, AMD can be considered the most promising in terms of prevention and early intervention, due to several factors surrounding the neural geometry of the foveal singularity. • Steep gradients of cell density, deployed in a radially symmetric fashion, can be modeled with a difference of Gaussian curves. • These steep gradients give rise to huge, spatially aligned biologic effects, summarized as the Center of Cone Resilience, Surround of Rod Vulnerability. • Widely used clinical imaging technology provides cellular and subcellular level information. • Data are now available at all timelines: clinical, lifespan, evolutionary • Snapshots are available from tissues (histology, analytic chemistry, gene expression) • A viable biogenesis model exists for drusen, the largest population-level intraocular risk factor for progression. • The biogenesis model shares molecular commonality with atherosclerotic cardiovascular disease, for which there has been decades of public health success. • Animal and cell model systems are emerging to test these ideas.

Retinal Photoreceptor Diversity Across Mammals

Inhibition in the retina

Molecular Characterization of Retinal Cell Types: Insights into Evolutionary Origins and Regional Specializations

Reimagining the neuron as a controller: A novel model for Neuroscience and AI

We build upon and expand the efficient coding and predictive information models of neurons, presenting a novel perspective that neurons not only predict but also actively influence their future inputs through their outputs. We introduce the concept of neurons as feedback controllers of their environments, a role traditionally considered computationally demanding, particularly when the dynamical system characterizing the environment is unknown. By harnessing a novel data-driven control framework, we illustrate the feasibility of biological neurons functioning as effective feedback controllers. This innovative approach enables us to coherently explain various experimental findings that previously seemed unrelated. Our research has profound implications, potentially revolutionizing the modeling of neuronal circuits and paving the way for the creation of alternative, biologically inspired artificial neural networks.

Incorporating visual evidence and counter-evidence to estimate self-movement

Sensory Consequences of Visual Actions

We use rapid eye, head, and body movements to extract information from a new part of the visual scene upon each new gaze fixation. But the consequences of such visual actions go beyond their intended sensory outcomes. On the one hand, intrinsic consequences accompany movement preparation as covert internal processes (e.g., predictive changes in the deployment of visual attention). On the other hand, visual actions have incidental consequences, side effects of moving the sensory surface to its intended goal (e.g., global motion of the retinal image during saccades). In this talk, I will present studies in which we investigated intrinsic and incidental sensory consequences of visual actions and their sensorimotor functions. Our results provide insights into continuously interacting top-down and bottom-up sensory processes, and they reify the necessity to study perception in connection to motor behavior that shapes its fundamental processes.

Visual Monitoring of Visual Appearance as a Feedback System in Dynamic Camouflage

How fly neurons compute the direction of visual motion

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits, involving a comparison of the signals from neighboring photoreceptors over time. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Much progress has been made in recent years in the fruit fly Drosophila melanogaster by genetically targeting individual neuron types to block, activate or record from them. Our results obtained this way demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

Foundation models in ophthalmology

Abstract to follow.

Computational and mathematical approaches to myopigenesis

Myopia is predicted to affect 50% of all people worldwide by 2050, and is a risk factor for significant, potentially blinding ocular pathologies, such as retinal detachment and glaucoma. Thus, there is significant motivation to better understand the process of myopigenesis and to develop effective anti-myopigenic treatments. In nearly all cases of human myopia, scleral remodeling is an obligate step in the axial elongation that characterizes the condition. Here I will describe the development of a biomechanical assay based on transient unconfined compression of scleral samples. By treating the scleral as a poroelastic material, one can determine scleral biomechanical properties from extremely small samples, such as obtained from the mouse eye. These properties provide proxy measures of scleral remodeling, and have allowed us to identify all-trans retinoic acid (atRA) as a myopigenic stimulus in mice. I will also describe nascent collaborative work on modeling the transport of atRA in the eye.

Comparative transcriptomics of retinal cell types

Light-driven dopamine release in the adult and developing retina

Restoring function in advanced disease with photoreceptor cell replacement therapy

Human and Zebrafish retinal circuits: similarities in day and night

Diverse applications of artificial intelligence and mathematical approaches in ophthalmology

Ophthalmology is ideally placed to benefit from recent advances in artificial intelligence. It is a highly image-based specialty and provides unique access to the microvascular circulation and the central nervous system. This talk will demonstrate diverse applications of machine learning and deep learning techniques in ophthalmology, including in age-related macular degeneration (AMD), the leading cause of blindness in industrialized countries, and cataract, the leading cause of blindness worldwide. This will include deep learning approaches to automated diagnosis, quantitative severity classification, and prognostic prediction of disease progression, both from images alone and accompanied by demographic and genetic information. The approaches discussed will include deep feature extraction, label transfer, and multi-modal, multi-task training. Cluster analysis, an unsupervised machine learning approach to data classification, will be demonstrated by its application to geographic atrophy in AMD, including exploration of genotype-phenotype relationships. Finally, mediation analysis will be discussed, with the aim of dissecting complex relationships between AMD disease features, genotype, and progression.

How what you do shapes what you see

Seeing slowly - how inner retinal photoreceptors support vision and circadian rhythms in mice and humans

Neural circuits for vision in the natural world

Started at 09 .15 - A WHOLE DAY symposium celebrating the work of Mike Land

Note: British 16.15 is the finishing time

Routing and modulation of retinal input to circuits underlying aversive behavior

The smart image compression algorithm in the retina: a theoretical study of recoding inputs in neural circuits

Computation in neural circuits relies on a common set of motifs, including divergence of common inputs to parallel pathways, convergence of multiple inputs to a single neuron, and nonlinearities that select some signals over others. Convergence and circuit nonlinearities, considered individually, can lead to a loss of information about the inputs. Past work has detailed how to optimize nonlinearities and circuit weights to maximize information, but we show that selective nonlinearities, acting together with divergent and convergent circuit structure, can improve information transmission over a purely linear circuit despite the suboptimality of these components individually. These nonlinearities recode the inputs in a manner that preserves the variance among converged inputs. Our results suggest that neural circuits may be doing better than expected without finely tuned weights.

Retinal and brain circuits underlying the effects of light on behavior

Visual circuits for threat anticipation

Learning to see stuff

Humans are very good at visually recognizing materials and inferring their properties. Without touching surfaces, we can usually tell what they would feel like, and we enjoy vivid visual intuitions about how they typically behave. This is impressive because the retinal image that the visual system receives as input is the result of complex interactions between many physical processes. Somehow the brain has to disentangle these different factors. I will present some recent work in which we show that an unsupervised neural network trained on images of surfaces spontaneously learns to disentangle reflectance, lighting and shape. However, the disentanglement is not perfect, and we find that as a result the network not only predicts the broad successes of human gloss perception, but also the specific pattern of errors that humans exhibit on an image-by-image basis. I will argue this has important implications for thinking about appearance and vision more broadly.

Deep learning applications in ophthalmology

Deep learning techniques have revolutionized the field of image analysis and played a disruptive role in the ability to quickly and efficiently train image analysis models that perform as well as human beings. This talk will cover the beginnings of the application of deep learning in the field of ophthalmology and vision science, and cover a variety of applications of using deep learning as a method for scientific discovery and latent associations.

Interplay between circuits that mediate spontaneous retinal waves and early light responses during retinal development

Vision for Predation

Active vision in Drosophila

Melanopsin contributions to vision in mice and man

Context-dependent selectivity to natural scenes in the retina

Regional variation of photoreceptor and circuit function in the primate retina

Development and evolution of neuronal connectivity

In most animal species including humans, commissural axons connect neurons on the left and right side of the nervous system. In humans, abnormal axon midline crossing during development causes a whole range of neurological disorders ranging from congenital mirror movements, horizontal gaze palsy, scoliosis or binocular vision deficits. The mechanisms which guide axons across the CNS midline were thought to be evolutionary conserved but our recent results suggesting that they differ across vertebrates.  I will discuss the evolution of visual projection laterality during vertebrate evolution.  In most vertebrates, camera-style eyes contain retinal ganglion cell (RGC) neurons projecting to visual centers on both sides of the brain. However, in fish, RGCs are thought to only innervate the contralateral side. Using 3D imaging and tissue clearing we found that bilateral visual projections exist in non-teleost fishes. We also found that the developmental program specifying visual system laterality differs between fishes and mammals. We are currently using various strategies to discover genes controlling the development of visual projections. I will also present ongoing work using 3D imaging techniques to study the development of the visual system in human embryo.

Seeing the world through moving photoreceptors - binocular photomechanical microsaccades give fruit fly hyperacute 3D-vision

To move efficiently, animals must continuously work out their x,y,z positions with respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively sample the eyes’ optical image disparity is not understood because this fundamental information-limiting step has not been investigated in vivo over the eyes’ whole sampling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a morphodynamic active sampling theory. It shows how photomechanical photoreceptor microsaccades enable Drosophila superresolution three-dimensional vision and proposes neural computations for accurately predicting these flies’ depth-perception dynamics, limits, and visual behaviors.

A model of colour appearance based on efficient coding of natural images

An object’s colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and “illusions” have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for when measuring an object’s perceived colour. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial band’s lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next we systematically test the model’s ability to qualitatively predict over 35 brightness and colour phenomena, with almost complete success. This implies that contrary to high-level processing explanations, much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a basis for modelling the vision of humans and other animals.

Color vision circuits for primate intrinsically photosensitive retinal ganglion cells

The rising and setting of the sun is accompanied by changes in both the irradiance and the spectral distribution of the sky. Since the discovery of intrinsically photosensitive retinal ganglion cells (ipRGCs) 20 years ago, considerable progress has been made in understanding melanopsin's contributions to encoding irradiance. Much less is known about the cone inputs to ipRGCs and how they could encode changes in the color of the sky. I will summarize our recent connectomic investigation into the cone-opponent inputs to primate ipRGCs and the implications of this work on our understanding of circadian photoentrainment and the evolution of color vision.

What the fly’s eye tells the fly’s brain…and beyond

Fly Escape Behaviors: Flexible and Modular We have identified a set of escape maneuvers performed by a fly when confronted by a looming object. These escape responses can be divided into distinct behavioral modules. Some of the modules are very stereotyped, as when the fly rapidly extends its middle legs to jump off the ground. Other modules are more complex and require the fly to combine information about both the location of the threat and its own body posture. In response to an approaching object, a fly chooses some varying subset of these behaviors to perform. We would like to understand the neural process by which a fly chooses when to perform a given escape behavior. Beyond an appealing set of behaviors, this system has two other distinct advantages for probing neural circuitry. First, the fly will perform escape behaviors even when tethered such that its head is fixed and neural activity can be imaged or monitored using electrophysiology. Second, using Drosophila as an experimental animal makes available a rich suite of genetic tools to activate, silence, or image small numbers of cells potentially involved in the behaviors. Neural Circuits for Escape Until recently, visually induced escape responses have been considered a hardwired reflex in Drosophila. White-eyed flies with deficient visual pigment will perform a stereotyped middle-leg jump in response to a light-off stimulus, and this reflexive response is known to be coordinated by the well-studied giant fiber (GF) pathway. The GFs are a pair of electrically connected, large-diameter interneurons that traverse the cervical connective. A single GF spike results in a stereotyped pattern of muscle potentials on both sides of the body that extends the fly's middle pair of legs and starts the flight motor. Recently, we have found that a fly escaping a looming object displays many more behaviors than just leg extension. Most of these behaviors could not possibly be coordinated by the known anatomy of the GF pathway. Response to a looming threat thus appears to involve activation of numerous different neural pathways, which the fly may decide if and when to employ. Our goal is to identify the descending pathways involved in coordinating these escape behaviors as well as the central brain circuits, if any, that govern their activation. Automated Single-Fly Screening We have developed a new kind of high-throughput genetic screen to automatically capture fly escape sequences and quantify individual behaviors. We use this system to perform a high-throughput genetic silencing screen to identify cell types of interest. Automation permits analysis at the level of individual fly movements, while retaining the capacity to screen through thousands of GAL4 promoter lines. Single-fly behavioral analysis is essential to detect more subtle changes in behavior during the silencing screen, and thus to identify more specific components of the contributing circuits than previously possible when screening populations of flies. Our goal is to identify candidate neurons involved in coordination and choice of escape behaviors. Measuring Neural Activity During Behavior We use whole-cell patch-clamp electrophysiology to determine the functional roles of any identified candidate neurons. Flies perform escape behaviors even when their head and thorax are immobilized for physiological recording. This allows us to link a neuron's responses directly to an action.

How do ipRGCs work? Evidence from the pupil light reflex

Since the discovery of the intrinsically photosensitive retinal ganglion cells (ipRGCs) – just two decades ago – substantial work has been carried out trying to understand their functioning. In this seminar, I’ll focus on pupillometry studies that have provided key clues about ipRGC behavior. Specifically, the interaction between the intrinsic response, rods, and cones will be discussed.

Light-induced moderations in vitality and sleep in the field

Retinal light exposure is modulated by our behavior, and light exposure patterns show strong variations within and between persons. Yet, most laboratory studies investigated influences of constant lighting settings on human daytime functioning and sleep. In this presentation, I will discuss a series of studies investigating light-induced moderations in sleepiness, vitality and sleep, with a strong focus on the temporal dynamics in these effects, and the bi-directional relation between persons' light profiles and their behavior.

Why do some animals have more than two eyes?

The evolution of vision revolutionised animal biology, and eyes have evolved in a stunning array of diverse forms over the past half a billion years. Among these are curious duplicated visual systems, where eyes can be spread across the body and specialised for different tasks. Although it sounds radical, duplicated vision is found in most major groups across the animal kingdom, but remains poorly understood. We will explore how and why animals collect information about their environment in this unusual way, looking at examples from tropical forests to the sea floor, and from ancient arthropods to living jellyfish. Have we been short-changed with just two eyes? Dr Lauren Sumner-Rooney is a Research Fellow at the OUMNH studying the function and evolution of animal visual systems. Lauren completed her undergraduate degree at Oxford in 2012, and her PhD at Queen’s University Belfast in 2015. She worked as a research technician and science communicator at the Royal Veterinary College (2015-2016) and held a postdoctoral research fellowship at the Museum für Naturkunde, Berlin (2016-2017) before arriving at the Museum in 2017.

The evolution and development of visual complexity: insights from stomatopod visual anatomy, physiology, behavior, and molecules

Bioluminescence, which is rare on land, is extremely common in the deep sea, being found in 80% of the animals living between 200 and 1000 m. These animals rely on bioluminescence for communication, feeding, and/or defense, so the generation and detection of light is essential to their survival. Our present knowledge of this phenomenon has been limited due to the difficulty in bringing up live deep-sea animals to the surface, and the lack of proper techniques needed to study this complex system. However, new genomic techniques are now available, and a team with extensive experience in deep-sea biology, vision, and genomics has been assembled to lead this project. This project is aimed to study three questions 1) What are the evolutionary patterns of different types of bioluminescence in deep-sea shrimp? 2) How are deep-sea organisms’ eyes adapted to detect bioluminescence? 3) Can bioluminescent organs (called photophores) detect light in addition to emitting light? Findings from this study will provide valuable insight into a complex system vital to communication, defense, camouflage, and species recognition. This study will bring monumental contributions to the fields of deep sea and evolutionary biology, and immediately improve our understanding of bioluminescence and light detection in the marine environment. In addition to scientific advancement, this project will reach K-college aged students through the development and dissemination of educational tools, a series of molecular and organismal-based workshops, museum exhibits, public seminars, and biodiversity initiatives.

Do direction selective retinal ganglion cells encode information uniformly?

Bernstein Conference 2024

End-to-end pipeline to achieve state-of-the-art cell typing in large-scale retinal recordings

Bernstein Conference 2024

Generalizing deep neural network model captures the functional organization of feature selective retinal ganglion cell axonal boutons in the superior colliculus

Bernstein Conference 2024

The retina processes different parts of a moving object in parallel

Bernstein Conference 2024

Short-term adaptation reshapes retinal ganglion cell selectivity to natural scenes

Bernstein Conference 2024

Toward a biophysically-detailed, fully-differentiable model of the mouse retina

Bernstein Conference 2024

A biophysically detailed model of retinal degeneration

COSYNE 2022

Divergence of chromatic information in GABAergic amacrine cells in the retina

COSYNE 2022

Emergence of an orientation map in the mouse superior colliculus from stage III retinal waves

COSYNE 2022

Identifying the nonlinear structure of receptive fields in the mammalian retina

COSYNE 2022

Identifying the nonlinear structure of receptive fields in the mammalian retina

COSYNE 2022

Large retinal populations are collectively organized to efficiently process natural scenes

COSYNE 2022

Large retinal populations are collectively organized to efficiently process natural scenes

COSYNE 2022

Large-scale paired recordings reveal strong and specific connections between retina and midbrain.

COSYNE 2022

Large-scale paired recordings reveal strong and specific connections between retina and midbrain.

COSYNE 2022

Mind the gradient: context-dependent selectivity to natural images in the retina revealed with a novel perturbative approach

COSYNE 2022

Mind the gradient: context-dependent selectivity to natural images in the retina revealed with a novel perturbative approach

COSYNE 2022

The smart image compression algorithm in the retina: recoding inputs in neural circuits

COSYNE 2022

The smart image compression algorithm in the retina: recoding inputs in neural circuits

COSYNE 2022

A biophysically detailed model of retinal degeneration

COSYNE 2023

Brainstem serotonin neurons selectively gate retinal information flow to thalamus

COSYNE 2023

Model-guided discovery of a retinal chromatic feature detector that signals visual context changes

COSYNE 2023

A novel deep neural network models two streams of visual processing from retina to cortex

COSYNE 2023

Pre-training artificial neural networks with spontaneous retinal activity improves image prediction

COSYNE 2023

Retinal scene statistics for freely moving mice

COSYNE 2023

Single-cell precision of axonal projection from the retina to the superior colliculus in mice

COSYNE 2023

Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses

COSYNE 2023

Zero-shot learning of decodable natural features from the retina using a U-net

COSYNE 2023

Carbon-based neural interfaces to probe retinal and cortical circuits with functional ultrasound imaging in vivo

FENS Forum 2024

Characterization of retinal signal transformation in the mouse superior colliculus

FENS Forum 2024

Comparative analysis of biophysical properties of ON-alpha sustained RGCs in wild-type and rd10 retina

FENS Forum 2024

Comparison of modulation efficiency between normal and degenerated primate retina

FENS Forum 2024

Decoding retinitis pigmentosa: Unveiling PRPF31 mutation effects on human iPSC-derived retinal organoids in vitro models

FENS Forum 2024

The early light experience alters Stage II retinal waves via dopamine-modulated pathway in the developing mouse retina

FENS Forum 2024

Functional and morphological characterization of zebrafish retinal ganglion cell subtypes expressing the transcription factor Satb2

FENS Forum 2024

High dose fish oil supplementation decreases amyloid beta accumulation in retinal blood vessels in 5xFAD mice

FENS Forum 2024

High precision ultrasound stimulation of the retina with photoacoustic membrane

FENS Forum 2024

Histaminergic circadian modulation of mouse retinal output in vivo

FENS Forum 2024

Human microglia-dependent viral-mediated inflammation impairs retinal organoid development

FENS Forum 2024

Influence of dendritic morphology on spike generation in alpha retinal ganglion cells

FENS Forum 2024