Biology subjects

Oesterle, J.

Publications and source records attributed to Oesterle, J..

9 recordsLinked to original sources

bioRxiv2026

Eyewire II - A connectomic resource for resolving cell types and circuits of the mouse retina

Comprehensive wiring diagrams from electron microscopy (EM) are a powerful tool to understand the inner workings of the brain. The retina is an easily accessible part of the brain that performs complex visual computations. Its thin, layered structure offers a unique opportunity to decipher neural cell types and map their connectivity. A major obstacle has been the limited size of existing retinal EM datasets, which could not resolve rare cell types and neurons with large dendritic arbors. Here, we describe Eyewire II, a large-scale EM dataset covering nearly 1 mm2 of the adult mouse retina - roughly 10-100 times larger than previous retinal EM volumes. Human proofreading of an automated reconstruction has so far yielded more than 8, 000 bipolar cells, 13, 000 amacrine cells, and 4, 000 retinal ganglion cells. Automated detection is complete for synaptic ribbons and in progress for conventional synapses. Prior to EM imaging, visual responses to diverse stimuli - including natural movies - were recorded in a subset of neurons using two-photon Ca2+ imaging, enabling direct alignment of morphological and functional cell type identity. To enable high-throughput, automatic cell typing, we devised a human-in-the-loop approach that combines deep learning with human expert annotations. As a proof-of-principle, we show that morphological features of reconstructed bipolar cells are sufficient to recover all 15 known bipolar cell types with regular, non-overlapping mosaics. Together, these data and tools establish Eyewire II as a shared resource for the field of retina research. Already now, more than 30 laboratories worldwide are contributing proofreading, expert annotations, and software tools, advancing Eyewire II towards a complete cell type catalog and synaptic wiring diagram of a mammalian retina.

neuroscience↗

bioRxiv2026

Inhibition benefits neural system identification

Neural system identification approaches use empirical data to fit the stimulus-response functions of neurons. Augmented by deep neural networks, such models have achieved high predictive performance and allow to perform in silico experiments to test hypotheses. Yet, many of these methods ignore common features in visual systems, such as inhibitory interactions between neurons, which are essential for nonlinear neural computation. Here, we incorporate inhibition as an inductive bias into a deep model for neural prediction and investigate the influence of inhibition on the learned transfer functions. To this end, we employ difference-of-Gaussian (subtraction) and within-channel divisive normalization (division), which have been proposed to relate inhibition to neural processing, in deep networks for predicting visual responses. We observe that incorporating such operations maintains the predictive performance and encourages the learning of biologically plausible kernels reminiscent of neural representation in early vision. Additionally, our in silico experiments demonstrate that implementing either sub-tractive or divisive operation benefits the learning of surround suppression but not cross-orientation inhibition. Interestingly, while division increases the sparsity of activation and reduces the sparsity of weights, subtraction has the reverse effect.

neuroscience↗

bioRxiv2025

A large-scale dataset of functional mouse ganglion cell layer responses

We present the AO_SCPLOWLLC_SCPLOW-GCL dataset, a large-scale resource of functional two-photon Ca2+-imaging recordings with rich meta-data information from more than 80,000 cells in the ganglion cell layer (GCL) of the ex vivo mouse retina. Collected over nine years across more than 139 experimental sessions, the dataset provides recordings of light-evoked responses to various stimuli, including a shared set of core stimuli. To enable cell-type-specific analyses, cells are probabilistically assigned to 46 previously characterized functional groups, including retinal ganglion cells and displaced amacrine cells. Further, we assessed the influence of experimental and biological factors on the functional responses and found only small batch effects across experimenters, setups, and recording sessions, highlighting the datasets consistency. The AO_SCPLOWLLC_SCPLOW-GCL dataset offers a comprehensive and standardised reference for studying retinal computation at scale. It supports population-level analyses, computational modelling, and the development of machine learning approaches for biological time-series data. Future releases will expand the dataset with additional mouse lines and light stimuli, creating a growing resource for the vision science community.

neuroscience↗

bioRxiv2024

Photoreceptor degeneration has heterogeneous effects on functional retinal ganglion cell types

Retinitis pigmentosa is a hereditary disease causing progressive degeneration of rod and cone photoreceptors, with no effective therapies. Using the rd10 mouse model, which mirrors the human condition, we examined its disease progression. Rods deteriorate by postnatal day (P) 45, followed by cone degeneration, with most photoreceptors lost by P180. Until then, retinal ganglion cells (RGCs) remain light-responsive, albeit only under photopic conditions, despite extensive outer retinal remodelling. However, it is still unknown whether the different functional RGC types remain stable, or if some types differentially alter their activity or are even lost during disease progression. Here, we addressed if and how the response diversity of functional RGC types changes with rd10 disease progression. At P30, we were able to identify all functional wild-type RGC types also in rd10 retina, suggesting that at this early degenerative stage, the full breadth of retinal output is still present. Remarkably, we found that the fractions of functional types changed throughout progressing degeneration between rd10 and wild-type: Responses of RGCs with Off-components ( Off and On-Off RGCs) seemed to be more vulnerable than On-cells, with Fast-On types being the most resilient. Notably, direction-selective RGCs appeared to be more vulnerable than orientation-selective RGCs. In summary, we found differences in resilience of response types (from resilient to vulnerable): Uncertain > Fast On > Slow On > On-Off > Off. Taken together, our results suggest that rd10 photoreceptor degeneration has heterogeneous effects on functional RGC types, with distinct sets of types losing their characteristic light responses earlier than others. This differential susceptibility of RGC circuits may be of relevance for future neuroprotective therapeutic strategies.

neuroscience↗

bioRxiv2024

Sustained ON alpha retinal ganglion cells in the temporal retina exhibit task-specific regional adaptions in dendritic signal integration

Various retinal ganglion cells (RGCs) show regional adaptations, for instance, to increase visual acuity. However, for many RGC types, it is not known how they vary in their signalprocessing properties across the retina. In the mouse retina, sustained ON alpha (sON) RGCs were found to have differences in morphology and receptive field sizes along the nasotemporal axis, and temporal sON RGCs are likely to play a role in visually guided hunting. Thus, we hypothesised that this cell type also exhibits regional adaptations on the level of dendritic signal processing and that these adaptations are advantageous for prey capture. Here, we measured dendritic signals from individual sON RGCs at different locations in the exvivo whole-mount mouse retina using two-photon microscopy. We measured both postsynaptic Ca2+ signals at the dendrites of individual RGCs and presynaptic glutamate signals from bipolar cells (BCs). We found that temporal sON RGC dendrites exhibit, in addition to the expected sustained-ON signals with only weak surrounds, signals with strong surround suppression, which were not present in nasal sON RGCs. This difference was also present in the excitatory presynaptic inputs from BCs, suggesting a presynaptic origin. Finally, using population models in an encoder-decoder paradigm, we showed that these adaptations might be beneficial for detecting crickets in hunting behaviour.

neuroscience↗

bioRxiv2023

Nitric oxide mediates differential effects in mouse retinal ganglion cells

Neuromodulators have major influences on the regulation of neural circuit activity across the nervous system. Nitric oxide (NO) has been shown to be a prominent neuromodulator in many circuits and has been extensively studied in the retina. Here, it has been associated with the regulation of light adaptation, gain control, and gap junctional coupling, but its effect on the retinal output, specifically on the different types of retinal ganglion cells (RGCs), is still poorly understood. In this study, we used two-photon Ca2+imaging and multi-electrode array (MEA) recordings to measure light-evoked activity of RGCs in the ganglion cell layer in the ex vivo mouse retina. This approach allowed us to investigate the neuromodulatory effects of NO on a cell type-level. Our findings reveal that NO selectively modulates the suppression of temporal responses in a distinct subset of contrast-suppressed RGC types, increasing their activity without altering the spatial properties of their receptive fields. Given that under photopic conditions, NO release is triggered by quick changes in light levels, we propose that these RGC types signal fast contrast changes to higher visual regions. Remarkably, we found that about one-third of the RGC types, recorded using two-photon Ca2+imaging, exhibited consistent, cell type-specific adaptational response changes throughout an experiment, independent of NO. By employing a sequential-recording paradigm, we could disentangle those additional adaptational response changes from drug-induced modulations. Taken together, our research highlights the selective neuromodulatory effects of NO on RGCs and emphasizes the need of considering non-pharmacological activity changes, like adaptation, in such study designs.

neuroscience↗

bioRxiv2021

Numerical uncertainty can critically affect simulations of mechanistic models in neuroscience

Understanding neural computation on the mechanistic level requires models of neurons and neuronal networks. To analyze such models one typically has to solve coupled ordinary differential equations (ODEs), which describe the dynamics of the underlying neural system. These ODEs are solved numerically with deterministic ODE solvers that yield single solutions with either no, or only a global scalar bound on precision. It can therefore be challenging to estimate the effect of numerical uncertainty on quantities of interest, such as spike-times and the number of spikes. To overcome this problem, we propose to use recently developed sampling-based probabilistic solvers, which are able to quantify such numerical uncertainties. They neither require detailed insights into the kinetics of the models, nor are they difficult to implement. We show that numerical uncertainty can affect the outcome of typical neuroscience simulations, e.g. jittering spikes by milliseconds or even adding or removing individual spikes from simulations altogether, and demonstrate that probabilistic solvers reveal these numerical uncertainties with only moderate computational overhead.

neuroscience↗

bioRxiv2021

Eye-Region Specific Ribbon Tuning Supports Distinct Modes of Synaptic Transmission in Same-Type Cone-Photoreceptors

Many sensory systems use ribbon-type synapses to transmit their signals to downstream circuits. The properties of this synaptic transfer fundamentally dictate which aspects in the original stimulus will be accentuated or suppressed, thereby partially defining the detection limits of the circuit. Accordingly, sensory neurons have evolved a wide variety of ribbon geometries and vesicle pool properties to best support their diverse functional requirements. However, the need for diverse synaptic functions does not only arise across neuron types, but also within. Here we show that UV-cones, a single type of photoreceptor of the larval zebrafish eye, exhibit striking differences in their synaptic ultrastructure and consequent calcium to glutamate transfer function depending on their location in the eye. We arrive at this conclusion by combining serial section electron microscopy and simultaneous "dual-colour" 2-photon imaging of calcium and glutamate signals from the same synapse in vivo. We further use the functional dataset to fit a cascade-like model of the ribbon synapse with different vesicle pool sizes, transfer rates and other synaptic properties. Exploiting recent developments in simulation-based inference, we obtain full posterior estimates for the parameters and compare these across different retinal regions. The model enables us to extrapolate to new stimuli and to systematically investigate different response behaviours of various ribbon configurations. We also provide an interactive, easy-to-use version of this model as an online tool. Overall, we show that already on the synaptic level of single neuron types there exist highly specialized mechanisms which are advantageous for the encoding of different visual features.

neuroscience↗

bioRxiv2020

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

Multicompartment models have long been used to study the biophysical mechanisms underlying neural information processing. However, it has been challenging to infer the parameters of such models from data. Here, we build on recent advances in Bayesian simulation-based inference to estimate the parameters of detailed models of retinal neurons whose anatomical structure was based on electron microscopy data. We demonstrate how parameters of a cone, an OFF- and an ON-cone bipolar cell model can be inferred from standard two-photon glutamate imaging with simple light stimuli. The inference method starts with a prior distribution informed by literature knowledge and yields a posterior distribution over parameters highlighting parameters consistent with the data. This posterior allows determining how well parameters are constrained by the data and to what extent changes in one parameter can be compensated for by changes in another. To demonstrate the potential of such data-driven mechanistic neuron models, we created a simulation environment for external electrical stimulation of the retina as used in retinal neuroprosthetic devices. We used the framework to optimize the stimulus waveform to selectively target OFF- and ON-cone bipolar cells, a current major problem of retinal neuroprothetics. Taken together, this study demonstrates how a data-driven Bayesian simulation-based inference approach can be used to estimate parameters of complex mechanistic models with high-throughput imaging data.

neuroscience↗