Biology subjects

Euler, T.

Publications and source records attributed to Euler, T..

At least 19 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.

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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.

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bioRxiv2026

On-Off coding is latent in vertebrate visual circuits

Across sensory systems, neurons often encode stimulus changes of opposite sign as On and Off signals, a fundamental strategy for representing deviations from baseline. In vertebrate vision, this computation is classically attributed to an early, hardwired split in the retina, with segregated pathways subsequently propagated through downstream circuits. Here, we challenge this view. Combining comparative transcriptomics, pharmacology, genetics, in vivo imaging and electrophysiology in zebrafish, with validation in mouse retina, we show that On-Off coding is a latent and intrinsic property of visual circuits. Bipolar cells frequently co-express receptor systems of opposing polarity and can generate mixed responses that are normally suppressed by inhibitory circuitry. Disrupting inhibition or selectively blocking receptor pathways unmasks robust On-Off signalling. In addition, mixed On-Off responses emerge naturally as effective light input increases, including during development and at higher light levels. Across processing stages, including retinal ganglion cells and central neurons, inhibition repeatedly enforces apparent polarity segregation. Thus, polarity splitting is a distributed, dynamically regulated computation rather than a fixed circuit feature. HIGHLIGHTSO_LIMixed On-Off signals are latent across visual processing stages C_LIO_LIInhibition actively sculpts polarity, not just refines it C_LIO_LIPolarity splitting is a dynamic, distributed and reusable computation C_LIO_LIConserved mechanism revealed in zebrafish and mouse C_LI

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bioRxiv2026

The functional organization of retinal input to the mouse superior colliculus

The superior colliculus integrates retinal input to drive rapid, adaptive visual behavior, yet how the functional diversity of retinal ganglion cell types is represented in superior colliculus remains poorly understood. Using chronic two-photon calcium imaging of retinal ganglion cell axonal boutons in awake mice, we recorded over 200,000 boutons across superficial superior colliculus layers -- a scale that enabled systematic comparison with large-scale ex vivo retinal datasets. This revealed that the superior colliculus receives a near-complete sampling of retinal ganglion cell functional diversity. Functionally distinct response types were organized in systematic laminar gradients: not only response properties such as direction selectivity and contrast suppression, but retinal response types themselves varied systematically with depth. To probe how this organized input encodes natural scenes, we trained a "digital twin" deep network model on natural movie responses and validated its generalization to parametric stimuli, including cell type identification. Leveraging this model to generate predicted responses to looming stimuli, we identify a discrete subset of retinal response types tuned for collision detection at low angular thresholds -- a specialization embedded within a broader, non-specialized retinal population. The digital twin is made publicly available as a community resource. Together, these findings provide a comprehensive functional map of retinal drive to the superior colliculus and an in silico platform for linking retinal cell types to behaviorally relevant superior colliculus computations.

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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.

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bioRxiv2025

A data and task-constrained mechanistic model of the mouse outer retina shows robustness to contrast variations

Visual processing starts in the outer retina where photoreceptors transform light into electrochemical signals. These signals are modulated by inhibition from horizontal cells and sent to the inner retina via excitatory bipolar cells. The outer retina is thought to play an important role in contrast invariant coding of visual information, but how the different cell types implement this computation together remains incompletely understood. To understand the role of each cell type, we developed a fully-differentiable biophysical model of a circular patch of mouse outer retina. The model includes 200 cone photoreceptors with a realistic phototransduction cascade and ribbon synapses as well as horizontal and bipolar cells, all with cell-type specific ion channels. Going beyond decades of work constraining biophysical models of neurons only by experimental data, we used a dual approach, constraining some parameters of the model with available measurements and others by a visual task: (1) We fit the parameters of the cone models to whole cell patch-clamp measurements of photocurrents and two-photon glutamate imaging measurements of synaptic release. (2) We then trained the spatiotemporal outer retina model with photoreceptors and the other cell types to perform a visual classification task with varying contrast and luminance levels. We found that our outer retina model could learn to solve the classification task despite contrast and luminance variance in the stimuli. Testing different cell type compositions and connectivity patterns, we found that feedback from horizontal cells did not further improve task performance beyond that of excitatory photoreceptors and bipolar cells. This is surprising given that horizontal cells are positioned to mediate communication across cones and that they add to the models number of trainable parameters. Finally, we found that our model generalized better to out of distribution contrast levels than a linear classifier. Our work shows how the nonlinearities found in the outer retina can accomplish contrast invariant classification and teases apart the contributions of different cell types.

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bioRxiv2025

openretina: Collaborative Retina Modelling Across Datasets and Species

The retina provides a unique opportunity to develop a complete and precise model of a computational module in the central nervous system. Deep learning has recently vastly advanced efforts towards this goal, yet decades of data, code, and analysis practices remain fragmented between labs -- limiting reproducibility, comparison, and cumulative progress. We argue that an open, collaborative modelling ecosystem is now essential to move the field from isolated studies toward a unified, quantitative account of retinal computation. To this end, we present openretina, a modular Python package built on PyTorch that provides a standardised framework for training, evaluating, and interpreting neural network models of the retina. The package implements a shared "Core + Readout" model architecture with a reproducible training pipeline, a common data format based on HDF5, unified evaluation metrics, and in silico analysis techniques from the literature. In its initial release, openretina integrates five publicly available datasets spanning various species and recording modalities. For each dataset, we provide curated preprocessing, standardised data loaders, and pre-trained model checkpoints that serve as reproducible baselines for benchmarking new approaches. We demonstrate the platforms utility through example use cases: first, a gradient field analysis linking the instability of optimal stimuli to spatial contrast encoding in ON-OFF retinal ganglion cells; second, systematic benchmarking of architectures within and across datasets, revealing that substantial explainable variance remains uncaptured by current models. By making research tools interoperable across laboratories, openretina lays the groundwork for closing this gap collectively.

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bioRxiv2024

Vertebrate vision is ancestrally based on competing cone circuits

Vision first evolved in the water, where light becomes increasingly monochromatic with viewing distance. The presence of spectrally broad ( white) light is therefore the exclusive remit of the visual foreground. However, if and how aquatic visual systems exploit this white effect as an inductive bias, for example to judge distance, remains unknown. By combining two-photon imaging with hyperspectral stimulation, genetic cone-type ablation, and behaviour, we here show that zebrafish suppress neural responses to the visual background by contrasting greyscale and colour circuits that emerge at the first synapse of vision. To do so, zebrafish use an early retinal architecture that fundamentally differs from that of mammals: Rather than combining cone signals to drive the retinal output leading to behaviour, zebrafish vision is built around competing ancestral cone systems: Red/UV versus green/blue. Of these, the non-opponent red and UV cones, which are retained in mammals, are necessary and sufficient for vision. By contrast, the colour opponent green and blue cones, which are lost in mammals, form a net-suppressive auxiliary system that shape the core drive from red and UV cones. Our insights challenge the long-held notions that cones act in concert to drive visual behaviour, and that their spectral diversity primarily serves colour vision. Instead, we posit that vertebrate vision is ancestrally built upon opposing cone systems that emerged to exploit the strong spectral interactions of light with water. This alternative view points at terrestrialisation, not nocturnalisation, as the leading driver for visual circuit reorganisation in mammals.

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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.

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bioRxiv2024

A hemispheric dome setup for naturalistic visual stimulation in head-fixed mice

The visual system of any animal species is adapted to its ecological niche. Thus, investigating visual function and behavior using naturalistic stimuli holds significant potential. In mice, these adaptations include a field of view of [~]280{degrees} and cone opsins sensitive to UV and green wavelengths. Such adaptations, however, cannot be probed with standard consumer displays. To present naturalistic visual stimuli to mice, we built a hemispheric dome setup, enabling the controlled projection of wide-field movies with UV-green spectral content. For our UV-green projection, we used a customized light engine with external high-power LEDs. We mitigated spatial distortions introduced by the projection through a geometry-independent calibration procedure. Additionally, we adapted a head-mounted eye tracking system to capture behavioral responses of head-fixed mice viewing these stimuli. We validated our setup by quantifying the pupillary light reflex to uniform stimuli and the optokinetic reflex to drifting gratings. Finally, in experiments with naturalistic movies, we investigated whether mice showed differential saccades and eye positions based on visual input. Comparing naturalistic movies to a uniform screen control condition, we observed that although head-fixed mice did not make targeted saccades during movies, their overall eye position consistently shifted towards more frontal regions of visual space. This indicates that mice adjust their eye position in a stimulus-dependent way, potentially to optimize visual processing of information ahead in the visual field. Together, our results highlight the utility of our setup for in vivo studies of the mouse visual system with more naturalistic visual stimulation. Significance StatementOver the past decades, mice have emerged as a prominent model for vision. Nonetheless, compared to primates, mice inhabit a different visual niche, featuring several species-specific adaptations, including a wide field of view and sensitivity to UV light. Both adaptations cannot be adequately studied using consumer displays commonly employed in vision research. We therefore built a hemispheric dome setup allowing the presentation of wide field movies with appropriate spectral content and tracking eye movements and pupil dynamics in head-fixed mice with minimal shadows. We provide a baseline assessment of the setup leveraging two well-established eye reflexes. We also showcase the setups utility by reporting adjustments in eye movements when mice viewed the naturalistic movies.

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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.

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bioRxiv2024

GABAergic amacrine cells balance biased chromatic information in the mouse retina

The retina extracts chromatic information present in an animals environment. In the mouse, the feed-forward, excitatory pathway through the retina is dominated by a chromatic gradient, with green and UV signals primarily processed in the dorsal and ventral retina, respectively. However, at the output of the retina, chromatic tuning is more mixed, suggesting that amacrine cells alter spectral tuning. We genetically targeted the population of 40+ GABAergic amacrine cell types and used two-photon calcium imaging to systematically survey chromatic responses in their dendritic processes. We found that amacrine cells show diverse chromatic responses in different spatial regions of their receptive fields and across the dorso-ventral axis of the retina. Compared to their excitatory inputs from bipolar cells, amacrine cells are less chromatically tuned and less likely to be colour-opponent. We identified 25 functional amacrine cell types that, in addition to their chromatic properties, exhibit distinctive achromatic receptive field properties. A combination of pharmacological interventions and a biologically-inspired deep learning model revealed how lateral inhibition and recurrent excitatory inputs shape chromatic properties of amacrine cells. Our data suggest that amacrine cells balance the strongly biased spectral tuning of excitation in the mouse retina and thereby support increased diversity in chromatic information of the retinal output.

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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.

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bioRxiv2022

Super-resolution STED imaging in the inner and outer whole-mount mouse retina

Since its invention in 1994, super-resolution microscopy has become a popular tool for advanced imaging of biological structures, allowing visualisation of subcellular structures at a spatial scale below the diffraction limit. Thus, it is not surprising that recently, different super-resolution techniques are being applied in neuroscience, e.g. to resolve the clustering of neurotransmitter receptors and protein complex composition in presynaptic terminals. Still, the vast majority of these experiments were carried out either in cell cultures or very thin tissue sections, while there are only a few examples of super-resolution imaging in thick (> ~50 m) biological samples. In that context, the mammalian whole-mount retina has rarely been studied with super-resolution microscopy. Here, we aimed at establishing a stimulated-emission-depletion (STED) microscopy protocol for imaging whole-mount retina. To this end, we developed sample preparation including horizontal slicing of retinal tissue, an immunolabeling protocol with STED-compatible fluorophores and optimised the STED microscopes settings. We labelled subcellular structures in somata, dendrites, and axons of retinal ganglion cells in the inner mouse retina. Under optimal conditions, we achieved a mean lateral spatial resolution of ~120 nm (using the full width of half-maximum as a proxy) for the thinnest filamentous structures in our preparation and a resolution enhancement of two or higher compared to conventional confocal images. When combined with horizontal slicing of the retina, these settings allowed us visualisation of putative GABAergic horizontal cell synapses in the outer retina with a similar resolution. Taken together, we successfully established a STED protocol for reliable super-resolution imaging in the whole-mount mouse retina, which enables investigating, for instance, protein complex composition and cytoskeletal ultrastructure at retinal synapses in health and disease.

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bioRxiv2022

A chromatic feature detector in the retina signals visual context changes

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour-opponency in transient Suppressed-by-Contrast RGCs (tSbC), a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by heador eye-movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

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bioRxiv2022

Spike desensitisation as a mechanism for high-contrast selectivity in retinal ganglion cells

In the vertebrate retina, several dozens of parallel channels relay information about the visual world to the brain. These channels are represented by the different types of retinal ganglion cells (RGCs), whose responses are rendered selective for distinct sets of visual features by various mechanisms. These mechanisms can be roughly grouped into synaptic interactions and cell-intrinsic mechanisms, with the latter including dendritic morphology as well as ion channel complement and distribution. Here, we investigate how strongly ion channel complement can shape RGC output by comparing two mouse RGC types, the well-described ON alpha cell and a little-studied ON cell that is EGFP-labelled in the Igfbp5 mouse line and displays an unusual selectivity for high-contrast stimuli. Using patch-clamp recordings and computational modelling we show that in ON Igfbp5 cells - but not in the ON alpha cells - a higher activation threshold and a pronounced slow inactivation of the voltage-gated Na+ channels are responsible for the distinct contrast tuning and transient responses of ON Igfbp5 RGCs, respectively. This study provides an example for the powerful role that the last stage of retinal processing can play in shaping RGC responses. SIGNIFICANCE STATEMENTHere, we investigated, how voltage-gated sodium channels contribute to shaping the light responses of mouse retinal ganglion cells. Using single-cell electrophysiology and computational modelling, we studied a ganglion cell type that displays highly transient responses and an unusual selectivity for visual high-contrast stimuli. We found that the cells characteristic responses were largely determined by intrinsic mechanisms, notably, a high activation threshold and a pronounced slow inactivation of its voltage-gated sodium channels. Therefore, our study demonstrates how sodium channels at the last stage of retinal signal processing can contribute to shape retinal output to higher visual areas the brain; it also adds a rare example for how channel complement can be directly linked to feature selectivity.

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bioRxiv2022

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. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

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bioRxiv2021

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal. We compare our method to previous approaches on a large-scale dataset of two-photon imaging recordings of retinal bipolar cell responses to visual stimuli. This dataset provides a unique benchmark as it contains biological signal from well-defined cell types that is obscured by large inter-experimental variability. In a supervised setting, we compare the generalization performance of cell type classifiers across experiments, which we validate with anatomical cell type distributions from electron microscopy data. In an unsupervised setting, we remove inter-experimental variability from data which can then be fed into arbitrary downstream analyses. In both settings, we find that our method achieves the best trade-off between removing inter-experimental variability and preserving biological signal. Thus, we offer a flexible approach to remove inter-experimental variability and integrate datasets across experiments in systems neuroscience. Code available at https://github.com/eulerlab/rave.

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