Biology subjects

Euler, T.

Publications and source records attributed to Euler, T..

35 records · Page 2Linked to original sources

bioRxiv2021

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

Motion is a critical aspect of vision. We studied the representation of motion in mouse retinal bipolar cells and found, surprisingly, that some bipolar cells possess motion-sensing capabilities that rely on their center-surround receptive fields. Using a glutamate sensor, we directly observed motion-sensitive bipolar cell synaptic output, which was strongest for local motion and dependent on the motions origin. We characterized bipolar cell receptive fields and found that there are motion and non-motion sensitive bipolar cell types, the majority being motion sensitive. Next, we used these bipolar cell receptive fields along with connectomics to design biophysical models of downstream cells. The models and experiments demonstrated that bipolar cells pass motion-sensitive excitation to starburst amacrine cells through direction-specific signals mediated by bipolar cells center-surround receptive field structure. As bipolar cells provide excitation to most amacrine and ganglion cells, their motion sensitivity may contribute to motion processing throughout the visual system.

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bioRxiv2021

Estimating smooth and sparse neural receptive fields with a flexible spline basis

Spatio-temporal receptive field (STRF) models are frequently used to approximate the computation implemented by a sensory neuron. Typically, such STRFs are assumed to be smooth and sparse. Current state-of-the-art approaches for estimating STRFs based empirical Bayes estimation encode such prior knowledge into a prior covariance matrix, whose hyperparameters are learned from the data, and thus provide STRF estimates with the desired properties even with little or noisy data. However, empirical Bayes methods are often not computationally efficient in high-dimensional settings, as encountered in sensory neuroscience. Here we pursued an alternative approach and encode prior knowledge for estimation of STRFs by choosing a set of basis function with the desired properties: a natural cubic spline basis. Our method is computationally efficient, and can be easily applied to Linear-Gaussian and Linear-Nonlinear-Poisson models as well as more complicated Linear-Nonlinear-Linear-Nonlinear cascade model or spike-triggered clustering methods. We compared the performance of spline-based methods to no-spline ones on simulated and experimental data, showing that spline-based methods consistently outperformed the no-spline versions. We provide a Python toolbox for all suggested methods (https://github.com/berenslab/RFEst/).

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bioRxiv2020

Mouse retinal specializations reflect knowledge of natural environment statistics

Pressures for survival drive sensory circuit adaption to a species habitat, making it essential to statistically characterise natural scenes. Mice, a prominent visual system model, are dichromatic with enhanced sensitivity to green and UV. Their visual environment, however, is rarely considered. Here, we built a UV-green camera to record footage from mouse habitats. We found chromatic contrast to greatly diverge in the upper but not the lower visual field, an environmental difference that may underlie the species superior colour discrimination in the upper visual field. Moreover, training an autoencoder on upper but not lower visual field scenes was sufficient for the emergence of colour-opponent filters. Furthermore, the upper visual field was biased towards dark UV contrasts, paralleled by more light-offset-sensitive cells in the ventral retina. Finally, footage recorded at twilight suggests that UV promotes aerial predator detection. Our findings support that natural scene statistics shaped early visual processing in evolution. Lead contactFurther information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Thomas Euler (thomas.euler@cin.uni-tuebingen.de)

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bioRxiv2020

Redefining the role of Ca2+-permeable channels in hereditary photoreceptor degeneration using the D- and L-cis enantiomers of diltiazem

Hereditary degeneration of photoreceptors has been linked to over-activation of Ca2+-permeable channels, excessive Ca2+-influx, and downstream activation of Ca2+-dependent calpain-type proteases. Unfortunately, after more than 20 years of pertinent research, unequivocal evidence proving significant and reproducible photoreceptor protection with Ca2+-channel blockers is still lacking. Here, we show that both D- and L-cis enantiomers of the anti-hypertensive drug diltiazem were very effective at blocking photoreceptor Ca2+-influx, most probably by blocking the pore of Ca2+-permeable channels. Yet, unexpectedly, this block neither reduced the activity of calpain-type proteases, nor did it result in photoreceptor protection. Remarkably, application of the L-cis enantiomer of diltiazem even led to a strong increase in photoreceptor cell death. These findings shed doubt on the previously proposed links between Ca2+ and retinal degeneration and are highly relevant for future therapy development as they may serve to refocus research efforts towards alternative, Ca2+-independent degenerative mechanisms.

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bioRxiv2020

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina

Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retinas cell types and the many neural circuits they form is emerging. However, the currently best performing models of retinal func{-}tion are black-box CNN models which are agnostic to such biological knowledge. In particular, these models typically neglect the role of the many inhibitory circuits involving amacrine cells and the biophysical mechanisms underlying synaptic release. Here, we present a computational model of temporal processing in the inner retina, including inhibitory feedback circuits and realistic synaptic release mechanisms. Fit to the responses of bipolar cells, the model generalized well to new stimuli including natural movie sequences, performing on par with or better than a benchmark black-box model. In pharmacology experiments, the model replicated in silico the effect of blocking specific amacrine cell populations with high fidelity, indicating that it had learned key circuit functions. Also, more in depth comparisons showed that connectivity patterns learned by the model were well matched to connectivity patterns extracted from connectomics data. Thus, our model provides a biologically interpretable data-driven account of temporal processing in the inner retina, filling the gap between purely black-box and detailed biophysical modeling.

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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.

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bioRxiv2019

Divergent excitation two photon microscopy for 3D random access mesoscale imaging at single cell resolution

ABSTACTIn neuroscience, diffraction limited two-photon (2P) microscopy is a cornerstone technique that permits minimally invasive optical monitoring of neuronal activity. However, most conventional 2P 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 divergent beam optics (DBO), we present an ultra-low-cost, easily implemented and flexible solution to address these limitations, offering a several-fold expanded three-dimensional field of view that also maintains single-cell resolution. We show that this implementation increases both the space-bandwidth product and effective excitation power, and allows for straight-forward tailoring of the point-spread-function. Moreover, rapid laser-focus control via an electrically tunable lens now 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, and fully compatible with a wide range of existing 2P customizations, 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 mice, zebrafish and fruit flies.

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bioRxiv2019

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

In the outer plexiform layer (OPL) of the mouse retina, two types of cone photoreceptors (cones) provide input to more than a dozen types of cone bipolar cells (CBCs). This transmission is modulated by a single horizontal cell (HC) type, the only interneuron in the outer retina. Horizontal cells form feedback synapses with cones and feedforward synapses with CBCs. However, the exact computational role of HCs is still debated. Along with performing global signaling within their laterally coupled network, HCs also provide local, cone-specific feedback. Specifically, it has not been clear which synaptic structures HCs use to provide local feedback to cones and global forward signaling to CBCs.\n\nHere, we reconstructed in a serial block-face electron microscopy volume the dendritic trees of five HCs as well as cone axon terminals and CBC dendrites to quantitatively analyze 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 located well below the cone axon terminal base and make contact to other cells mostly identified as other HCs or CBCs. Using immunolabeling we show that HC bulbs express vesicular gamma-aminobutyric acid transporters and co-localize with GABA receptor {gamma}2 subunits. Together, this suggests the existence of two synaptic strata in the mouse OPL, spatially separating cone-specific feedback and feedforward signaling to CBCs. A biophysics-based computational model of a HC dendritic branch supports the hypothesis that the spatial arrangement of synaptic contacts allows simultaneous local feedback and global feedforward signaling.

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bioRxiv2019

Type-specific dendritic integration in mouse retinal ganglion cells

Neural computation relies on the integration of synaptic inputs across a neurons dendritic arbour. However, the fundamental rules that govern dendritic integration are far from understood. In particular, it is still unclear how cell type-specific differences in dendritic integration arise from general features of neural morphology and membrane properties. Here, retinal ganglion cells (RGCs), which relay the visual systems first computations to the brain, represent an exquisite model. They are functionally and morphologically diverse yet defined, and they allow studying dendritic integration in a functionally relevant context. Here, we show how four morphologically distinct types of mouse RGC with shared excitatory synaptic input (transient Off alpha, transient Off mini, sustained Off, and F-miniOff) exhibit distinct dendritic integration rules. Using two-photon imaging of dendritic calcium signals and biophysical modelling, we demonstrate that these RGC types strongly differ in their spatio-temporal dendritic integration: In transient Off alpha cells, dendritic receptive fields displayed little spatial overlap, indicative of a dendritic arbour that is partitioned in largely isolated regions. In contrast, dendritic receptive fields in the other three RGCs overlapped greatly and were offset to the soma, suggesting strong synchronization of dendritic signals likely due to backpropagation of somatic signals. Also temporal correlation of dendritic signals varied extensively among these types, with transient Off mini cells displaying the highest correlation across their dendritic arbour. Modelling suggests that morphology alone cannot explain these differences in dendritic integration, but instead specific combinations of dendritic morphology and ion channel densities are required. Together, our results reveal how neurons exhibit distinct dendritic integration profiles tuned towards their type-specific computations in their circuits, with the interplay between morphology and ion channel complement as a key contributor.

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bioRxiv2019

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

Color vision is essential to the survival of most animals. Its neural basis lies in the retina, where chromatic signals from different photoreceptor types sensitive to distinct wavelengths are locally compared by neural circuits. Mice, like most mammals, are generally dichromatic and have two cone photoreceptor types. However, in the ventral retina most cones display the same spectral preference, impairing spectral comparisons necessary for color vision. This conflicts with behavioral evidence showing that mice can discriminate colors only in the corresponding upper visual field. Here, we systematically investigated the neural circuits underlying mouse color vision across three processing stages of the retina by recording the output of cones, bipolar and ganglion cells using two-photon imaging. Surprisingly, we found that across all retinal layers most color-opponent cells were located in the ventral retina. This started at the level of the cone output, where color-opponency was mediated by horizontal cells and likely involving rod photoreceptors. Next, bipolar cells relayed the chromatic information to ganglion cells in the inner retina, where type-specific, non-linear center-surround interactions resulted in specific color-opponent output channels to the brain. This suggests that neural circuits in the mouse retina are specifically tuned to extract color information from the upper visual field, aiding robust detection of aerial predators and ensuring the animals survival.

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bioRxiv2019

The temporal structure of the inner retina at a single glance

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

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bioRxiv2019

An arbitrary-spectrum spatial visual stimulator for vision research

Visual neuroscientists require accurate control of visual stimulation. However, few stimulator solutions simultaneously offer high spatio-temporal resolution and free control over the spectra of the light sources, because they rely on off-the-shelf technology developed for human trichromatic vision. Importantly, consumer displays fail to drive UV-shifted short wavelength-sensitive photoreceptors, which strongly contribute to visual behaviour in many animals, including mice, zebrafish and fruit flies. Moreover, many non-mammalian species feature more than three spectral photoreceptor types. Here, we present a flexible, spatial visual stimulator with up to 6 arbitrary spectrum chromatic channels. It combines a standard digital light processing engine with open source hard- and software that can be easily adapted to the experimentalists needs. We demonstrate the capability of this general visual stimulator experimentally in the in vitro mouse retinal whole-mount and the in vivo zebrafish. Hereby, we intend starting a community effort of sharing and developing a common stimulator design.

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bioRxiv2019

Systematic spatio-temporal mapping reveals divergent cell death pathways in three mouse models of hereditary retinal degeneration

Calcium (Ca2+) dysregulation has been linked to neuronal cell death, including in hereditary retinal degeneration. Ca2+ dysregulation is thought to cause rod and cone photoreceptor cell death. Spatial and temporal heterogeneities in retinal disease models have hampered validation of this hypothesis. We examined the role of Ca2+ in photoreceptor degeneration, assessing the activation pattern of Ca2+-dependent calpain proteases, generating spatio-temporal maps of the entire retina in the cpfl1 mouse model for primary cone degeneration, and in the rd1 and rd10 models for primary rod degeneration. We used Gaussian process models to distinguish the temporal sequences of degenerative molecular processes from other variability sources. In the rd1 and rd10 models, spatio-temporal pattern of increased calpain activity matched the progression of primary rod degeneration. High calpain activity coincided with activation of the calpain-2 isoform but not with calpain-1, suggesting differential roles for both calpain isoforms. Primary rod loss was linked to upregulation of apoptosis-inducing factor (AIF), although only a minute fraction of cells showed activity of the apoptotic marker caspase-3. After primary rod degeneration concluded, caspase-3 activation appeared in cones, suggesting apoptosis as the dominant mechanism for secondary cone loss. Gaussian process models highlighted calpain activity as a key event during primary rod photoreceptor cell death. Our data suggests a causal link between Ca2+ dysregulation and primary, non-apoptotic degeneration of photoreceptors and a role for apoptosis in secondary degeneration of cones, highlighting the importance of the spatial and temporal location of key molecular events, which may guide the evaluation of new therapies.

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bioRxiv2018

Bayesian Hypothesis Testing And Experimental Design For Two-Photon Imaging Data

Variability, stochastic or otherwise, is a central feature of neural circuits. Yet the means by which variation and uncertainty are derived from noisy observations of neural activity is often unprincipled, with too much weight placed on numerical convenience at the cost of statistical rigour. For two-photon imaging data, composed of fundamentally probabilistic streams of photon detections, the problem is particularly acute. Here, we present a complete statistical pipeline for the inference and analysis of neural activity using Gaussian Process Regression, applied to two-photon recordings of light-driven activity in ex vivo mouse retina. We demonstrate the flexibility and extensibility of these models, considering cases with non-stationary statistics, driven by complex parametric stimuli, in signal discrimination, hierarchical clustering and inference tasks. Sparse approximation methods allow these models to be fitted rapidly, permitting them to actively guiding the design of light stimulation in the midst of ongoing two-photon experiments.

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bioRxiv2018

Mouse dLGN receives input from a diverse population of retinal ganglion cells with limited convergence

In the mouse, the parallel output of more than 30 functional types of retinal ganglion cells (RGCs) serves as the basis for all further visual processing. Little is known about how the representation of visual information changes between the retina and the dorsolateral geniculate nucleus (dLGN) of the thalamus, the main relay station between the retina and cortex. Here, we functionally characterized responses of retrogradely labeled dLGN-projecting RGCs and dLGN neurons to the same set of visual stimuli. We found that many of the previously identified functional RGC types innervate the dLGN, which maintained a high degree of functional diversity. Using a linear model to assess functional connectivity between RGC types and dLGN neurons, we found that the responses of dLGN neurons could be predicted as a linear combination of inputs from on average five RGC types, but only two of those had the strongest functional impact. Thus, mouse dLGN receives input from a diverse population of RGCs with limited functional convergence.

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bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

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bioRxiv2017

Local Signal Processing In Mouse Horizontal Cell Dendrites

The mouse retina contains a single type of horizontal cell, a GABAergic interneuron that samples from all cone photoreceptors within reach and modulates their glutamatergic output via parallel feedback mechanisms. Because horizontal cells form an electrically-coupled network, they have been implicated in global signal processing, such as large scale contrast enhancement. Recently, it has been proposed that horizontal cells can also act locally at the level of individual cone photoreceptors. To test this possibility physiologically, we used two-photon microscopy to record light stimulus-evoked Ca2+ signals in cone axon terminals and horizontal cell dendrites as well as glutamate release in the outer plexiform layer. By selectively stimulating the two mouse cone opsins with green and UV light, we assessed whether signals from individual cones remain \"isolated\" within horizontal cell dendritic tips, or whether they spread across the dendritic arbour. Consistent with the mouse s opsin expression gradient, we found that the Ca2+ signals recorded from dendrites of dorsal horizontal cells were dominated by M- and those of ventral horizontal cells by S-opsin activation. The signals measured in neighbouring horizontal cell dendritic tips varied markedly in their chromatic preference, arguing against global processing. Rather, our experimental data and results from biophysically realistic modelling support the idea that horizontal cells can process cone input locally, extending the \"classical\" view of horizontal cells function. Pharmacologically removing horizontal cells from the circuitry reduced the sensitivity of the cone signal to low frequencies, suggesting that local horizontal cell feedback shapes the temporal properties of cone output.\n\nHighlightsO_LILight-evoked Ca2+ signals in horizontal cell dendrites reflect opsin gradient\nC_LIO_LIChromatic preferences in neighbouring dendritic tips vary markedly\nC_LIO_LIMouse horizontal cells process cone photoreceptor input locally\nC_LIO_LILocal horizontal cell feedback shapes the temporal properties of cone output\nC_LI\n\neTOC BlurbChapot et al. show that local light responses in mouse horizontal cell dendrites inherit properties, including chromatic preference, from the presynaptic cone photoreceptor, suggesting that their dendrites can provide \"private\" feedback to cones, for instance, to shape the temporal filtering properties of the cone synapse.

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